AU2023288052A1

AU2023288052A1 - Method and apparatus for detecting and quantifying disorganization (misalignment, disarrangement) within a material

Info

Publication number: AU2023288052A1
Application number: AU2023288052A
Authority: AU
Inventors: Albert Merlin Nguematsia; Martin Roger ZEBAZE DJOUMESSI
Original assignee: Individual
Current assignee: Individual
Priority date: 2022-06-20
Filing date: 2023-06-20
Publication date: 2025-01-30
Also published as: WO2023245235A1

Abstract

According to its broad aspect, the present invention relates to a method and apparatus for measuring the alignment(s) between two or more materials (objects, tissue, organs, or structures); identifying abnormal alignment (misalignment, disorganization), quantifying the degree or magnitude of the disorganization between the two (or more) materials. The method and system comprise identifying the data containing the information on the alignment between two or more objects (structures, or materials) from an alignment data source, analyzing the alignment data, and quantifying the alignment between the two or more objects (or materials), identifying (if present) an abnormal alignment (misalignment, disorganization), and quantifying the extent or magnitude of the abnormal alignment (disorganization).

Description

METHOD AND APPARATUS FOR DETECTING AND QUANTIFYING DISORGANIZATION (MISALIGNMENT, DISARRANGEMENT) WITHIN A MATERIAL.

FIELD OF THE INVENTION

The present invention relates to a method and apparatus for measuring the disorganization (misalignment, disarray) between two or more materials (objects, tissue, organs, or structures); identifying abnormal organization (disarrangement), quantifying the degree or magnitude of the disorganization(s) between the said two (or more) materials (or components of the materials).

BACKGROUND OF THE INVENTION

Measuring the disorganization including quantifying the characteristics (or properties) of the disorganization between two or more materials (organs, tissues, objects, or structures...) is essential in many fields, especially in medicine.

A material (object, structure, organ, tissue) can be abnormal because of (i.) Low quantity- i.e., the amount is of the material small, (ii.) Deterioration - i.e., the material is decayed, or (Hi.) Disorganized (disarranged)- i.e., the constituents of the material are poorly interrelated, interconnected, or misaligned. Disorganization can occur regardless of the amount of the material, and whether or not individuals’ components are decay.

In the medical field, in many cases, diseases and conditions affect, alter or change the organization of organs, tissues, materials, or structures. Hence, in many situations, diagnosis, and prevention of diseases relies on identifying and quantifying the disorganization (disarrangement, mess up) within tissues (organs, materials, or structures) caused by diseases or conditions. Consequently, treatments and/or interventions aimed at addressing these conditions or diseases rely on correcting, rectifying, or modifying the disorganization within the material (organ, or tissue).

Consequently, detecting any disorganization and quantifying the extent of the disorganization within a material is essential for the diagnosis, prevention, and treatment of diseases. Furthermore, quantifying the disorganization may be critical for the assessment of the response to therapies and interventions aimed at correcting diseases and conditions.

The problem is that whilst there are many methods to quantify the amount of a material or the extent of its decay (deterioration), there are few (if any) methods or tests available to detect or quantify the extent of disorganization between materials (or its components) including biological tissues. In particular, methods to do so in vivo (in living persons) in clinical and research settings.

The issues stem from the fact that there are many challenges inherent to the quantification of disorganization (misalignment, disarrangement) between materials or biological tissues (bone, cartilage, joints, heart ...), especially in in vivo settings.

To do so often requires obtaining data containing information on the arrangements between the constituents (components) forming the material or tissue. This is often done using various imaging modalities such as X-rays, Computed Tomography (CT), magnetic resonance devices, or any other imaging devices such as a microscope or camera. From the acquired image, the characteristics of the organs or objects including their arrangement, and alignment (organization) of the constituents of the object (material, tissue) can be quantified. However, this is a difficult task; to the extent that there are currently very few (if any) tools, methods, apparatus, or devices available to detect or quantify the extent of disorganization (reciprocal of organization) between the constituents of the material within the image.

Consequently, the assessment of any organization (arrangement) between materials (or its constituents) is largely (if not exclusively) done by an expert professional from a visual assessment of the image. This is cumbersome, non-quantitative, suggestive, and incomprehensive, with sometimes both low reproducibility and low accuracy. This is a serious limitation in many industries.

The lack or scarcity of methods, apparatus, or devices to perform (automatically if desired) the measurement of disorganization is an important limitation in the field of medicine, and other disciplines. In the medical field, for example, the lack of a tool to measure the identity, detect any disorganization (misalignment), and quantify its extent, especially in clinical practice results in an incomprehensive, limited ability to prevent, diagnose diseases, or assess the response to therapies and other interventions. Therefore, there is a critical need for an accurate, reproducible, and comprehensive measurement of the disorganization within a material including its characteristics.

A good organization (alignment) indicates and is the result of a good cohesion, interrelatedness, interplay, or connection between constituents forming structures or objects at virtually all scales. This includes the macro, micro, or nanoscales. This property (feature) is critical for the health and stability of the material (or system of many objects).

For instance, if a load (force) is applied on a well-organized, and thus made of well-aligned constituents (or materials), it will be transferred, conducted, and transmitted in a normal, coherent, effective manner. As a result, as examples, (i.) a bone with well- aligned, organized structures will be less fragile (i.e., less likely to break upon loading), (ii.) Muscle strains or ruptures are less likely to occur, (iii.) joint sprains are also less likely to occur, (iv.) More generally, any material (biological or not) made of well-organized components is more stable and less likely to sustain structural failure or any type of failure.

Conversely, a bad or poor alignment (organization) indicates, and/or is the result of poor interrelatedness, inadequate structural cohesion, or connection between constituents forming the material (object, tissue); and this applies to all scales (macro, micro, or nanoscales).

A bad alignment (disorganization) between constituents of a material is an abnormality. For example. In the field of medicine, a poor organization (misalignment, disarrangement, disarray) could be due to many processes such as diseases (cancers, infections, metabolic abnormalities, necrosis, infarction, deformities...), or trauma. Regardless of the cause, if a system is made of disorganized components (materials, structures) it cannot function efficiently. It will abnormal, unstable, and unhealthy. Hence, it is prone to failure. For instance, in the field of musculoskeletal health, if a load (force) is applied to a system or structure made of misaligned (disorganized) components, it will be transferred or conducted in an abnormal fashion; the transfer or conduction of the load will be incoherent, ineffective, inefficient. As a result, there will be ineffective or unstable load transfer with potentially deleterious or harmful consequences. As examples, (i.) a bone with misaligned (disarranged, disorganized) structures will be more fragile (i.e., more prone to fracture), (ii.) A muscle will be more likely to sustain a strain or rupture, (iii.) A joint with misaligned or disorganized structures is more likely to sustain a sprain, (iv.) More generally, any material (biological, rocks, minerals, biomaterials, food...) made of disorganized components is unstable and hence, more likely to sustain structural failure or any type of failure.

Detecting and quantifying the extent of disorganization between materials (or their constituents) is a crucial unmet need in many disciplines and sectors of industry. Consequently, the present invention describes a method and apparatus for detecting and quantifying disorganization between materials (or their constituents). In one particular embodiment, the invention is a method and system to measure disorganization within a material (or object) on data obtained from images acquired using imaging devices such as X-rays, Computed tomography (CT) or Magnetic Resonance Imaging (MRI) obtained in living (in vivo) settings.

The present invention is described in detail, in the context of the measurement of disorganization within the femoral bone as an example.

SUMMARY OF THE INVENTION

In view of the foregoing, the present invention describes a method and apparatus for measuring disorganization (misalignment) within materials (or objects, structures, organs, tissues). Some applications of the invention are also described.

According to a first broad aspect of this invention, there is provided a method for quantifying (automatically if desired) the alignment between at least one object or material or structure (object of interest) and at least one other object or material (target object), the method comprising:

Identifying the alignment data between at least one object of interest, and one target object from an alignment Data source;

Assessing the alignment data. The assessment comprises extracting and separating the information on the alignments of the two or more objects;

Analyzing the alignments data on each of the two or more objects (or materials);

Quantifying the alignment between the two or more objects (or their constituents);

Identifying (if present) an abnormal alignment (misalignment, disorganization);

Quantifying the extent or magnitude of the disorganization (misalignment).

Quantifying the alignment, identifying, and quantifying abnormal alignment or disorganization between at least one object of object of interest, and at least one target object may comprise comparing the alignment of one object to a referent alignment data.

The referent alignment data (comparator) may be that of a comparator or referent phantom custom-tailored to one or more materials (or objects); Usually custom- tailored to the material (object) for which the disorganization is being assessed (object of interest).

Creating a custom-tailored phantom of an object of interest may comprise:

Simulating a modification of the morphology (such as size, shape... .) of the said object;

Or simulating a modification of the composition or organization (arrangement, alignment) of constituents of the said object of interest;

Or simulating a modification of both the morphology, and that of composition or organization of constituents of the said object of interest.

The simulated changes are done in such a way that the alignment data obtained on the simulated object (custom-tailored phantom) can serve as referent for the purpose of measuring the extent misalignment (disorganization) within the original object (object of interest). The characteristics of the invention allow the method to operate automatically (and the system described below to be automated).

The materials (or objects) could be biological tissues or organs or non-biological tissues (rocks, minerals, biomaterials, food....).

The two or more materials could be of different nature. In one embodiment, this could be the alignment (organization) of one or more components of a biological material vis-a-vis one or more constituents of a non-biological material.

At least one set of alignment data in the alignment data source may be that of a phantom custom-tailored one or more objects (materials) of interest.

The Alignment Data Source may be any data source such a set of executable instructions or software configured to output alignment data, an imaging device, or any device configured to output alignment data.

The method may comprise assessing the alignment data. This involves ascertaining that the data contains all information required for the quantification of the alignment and hence, disorganization (misalignment).

During the assessment, the alignment data may be unsuitable for several reasons such as missing one or more alignments data required for the processing to proceed. When this occurs, the assessor of alignment may instruct Alignment Data Source (with which, it is in data communication) to correct output corrected data suitable for the alignment measurement.

After the data is identified and assessed as suitable, the method may comprise analyzing the data.

Analyzing the data may comprise performing a preliminary analysis or preprocessing to prepare (i.e., make ready) the alignment data to the extent that is ready for the required quantification of alignments.

This initial analysis or pre-processing step may comprise: - Removing outliers or any noise that may affect the measurement of the alignment.

- Filtering the data to extract the relevant alignment data

- Standardizing the alignments data. This has several advantages including adjusting the alignment data measured on different scales to a common scale so that all alignments quantified have the same scale regardless of the Alignment Data Source. This also allows easy comparability of alignments results regardless of the Alignment Data source.

Analyzing the alignment data may comprise selecting the type of alignment to be used for alignment measured or quantified. Many types of alignment may be quantified.

In some embodiments, the alignment is quantifying as a deviation from the orientation (or direction) defined by an object of interest (or that all its constituents or components) and a target object.

In other embodiments, quantifying including characterizing the alignment may involve quantifying the change (or variations), or differences in the alignment value between different portions (or locations) of the object of the interest. In this embodiment, quantification of the alignment may involve processes such as calculating the first order derivative of a function or curve representing the alignment value at each location (or position) within the object of interest.

Still In other embodiments, quantifying including characterizing the alignment may involve quantifying the rate of changes (or variations) or changes in the differences in the alignment value between different locations (or portions) of the object of the interest. In this embodiment, analysis of the alignment may involve processes such as calculating the second order derivative of a function or curve representing the alignment value at each location (or position) within the object of interest.

In other embodiments, quantifying including characterizing the alignment may involve performing any transformation of a function (or curve) showing the alignment value at each location within the object. This may include as examples calculating any other derivative, or an integral value of the said function.

The method may comprise further analyzing the prepared alignment data of the materials (or objects) after it has been preprocessed (e.g., filtering, noise removal, and standardization as described above).

At this step, in some embodiment the additional analysis is performed on the prepared alignment data of the materials (or objects) only. This is done when a referent alignment data is not required for the quantification of the alignment of the object of interest. Therefore, the alignment data is sufficient for the quantification of the alignment, and the identification and quantification of any abnormal alignment (misalignment, disorganization).

In other embodiments, a referent (or comparator) is required for the quantification of the alignment in the next steps of the processing. In this case, the method comprises further analyzing the prepared, preprocessed alignment data of the referent. The referent alignment data may be that of phantom custom-tailored to an object of interest in such way that the said data can be used this purpose.

After analysis of the alignment data of the objects, and that a referent custom- tailored phantom (if required) is performed, the method involves quantifying the alignment.

In some embodiments, a referent alignment data is not required. In these embodiments, the quantification of the alignment may comprise first computing a referent direction (or orientation) defined by an object of interest and a target component - that is a principal direction. Then, the misalignment of any constituents of an object of interest may then be quantified as deviation from the said principal direction. Identifying and quantifying the degree of misalignment of constituents may involve comparing the principal orientation (or direction) defined by the material of interest and the target material, to a predefined direction. Examples of predefined orientations may include standard ones such as the vertical, horizontal, or any direction.

In other embodiments, a referent is required. In these embodiments, quantifying the misalignment may involve comparing, contrasting the alignment data of the objects or (materials) of interest to that of the referent comparator. It should be appreciated that any form of comparison operation of the two datasets may be used to derive or quantify the alignment of the object. This may comprise but not limited to subtracting alignment data from that of the referent.

Creating a custom-tailored phantom alignment data to use as referent may comprise:

In some embodiments, creating alignment data of an object of interest after modifying (or simulating a modification of) the size, shape, the composition or spatial organization of the material within the object so that it can serve as a phantom.

In other embodiments, creating alignment data of an object of interest may involve assigning to the said object (such as its image), one or more specific characteristics. This may comprise assigning one unique intensity (attenuation or density) value, or assigning a specific pattern of attenuation or densities to the image of the said object of interest.

The alignment or misalignment (disorganization) may be quantified on the entire (whole) object (materials), or parts, or portions of the said object.

The method may comprise partitioning the object into alignment zones. It should be noted that alignment zones and misalignment (disorganization) zones are used interchangeably (and so are alignment, misalignment, or disorganization).

Alignment zones are different from anatomical, spatial or geographical zones. These are zones related to each other by specific alignment (organization) characteristics or features (Hence, similar misalignment features).

In some embodiments, identifying or defining an alignment zone may involve allocating to the same alignment zone, different portions or regions (such as contiguous ones) of an object that have the same alignment value. In other embodiments, identifying or defining an alignment zone may involve allocating to the same alignment zone, different portions or regions (such as contiguous) of an object that the same differences in the alignment (or misalignment) value.

Still in other embodiments, identifying or defining an alignment zone may involve allocating to the same alignment zone, different portions or regions (such as contiguous) of an object that have the same rate (or variation, or pattern) of differences in the alignment value.

Identifying and quantifying abnormal alignment (misalignment, disorganization) may involve quantifying characteristics (features) that may distinguish a normal from an abnormal alignment in the entire object, parts of the object, and/or in identified and separated alignment zones.

Quantified misalignment features (or properties) may comprise but are not limited to:

- A maximum (peak) misalignment (disorganization) value

- A minimum misalignment value

- Total misalignment value or misalignment magnitude

- The range of misalignment: This is the difference between the maximum and minimum misalignment value.

- The position, or location of the alignment zone

- Size, area or volume of the alignment zone

- The sharpness of the misalignment. This reflect the extent to which the maximum (or peak) misalignment value in a given alignment zone differ from other alignment value within the zone.

In this embodiment, the misalignment sharpness is quantified as:

Peak

Sharp ² Equation 1 rness = - Magnitud —e

- The heterogeneity or variability in the misalignment values within the zone or portion

- Any combination of the misalignment features. In some embodiments, identifying and quantifying misalignment may involve quantifying the extent to which one or more of the above quantified misalignment features deviates from a preset value.

In other embodiments, identifying and quantifying misalignment may involve quantifying the extent to which one or more of the above quantified alignment features in a given alignment zone differ from that observed in other alignment zones within the same object of interest.

Still in other embodiments, in normally symmetrical structures (object, or materials), identifying and quantifying misalignment may involve quantifying the extent to which the alignment value differs between conventionally (or normally) symmetrical parts or aspects of the structures or objects. In order words, this involve the quantifying magnitude of alignment (organization) asymmetry.

The method may comprise displaying one or more results of the analysis in an image displaying device.

In a particular embodiment, quantification of the misalignment comprises the misalignment between two bones such as between the femur and the acetabular bone.

In other embodiments, quantification of the misalignment may comprise quantifying the misalignments between any two bones such femur vis-a-vis tibia, tibia vis-a-vis calcaneus, or two adjoining vertebrae.

Alignment measurement, and quantification of misalignment (disorganization) such as described in the present invention alignment has many applications. The following are just some examples.

- Quantification of the misalignment (disorganization) may allow identification of fracture-vulnerable (at risk for fragility) individuals. Fracture-vulnerable people may include patients with bone fragility due to diseases such as diabetes, rheumatoid arthritis, metabolic diseases, chronic kidney diseases, patients classified as having osteoporosis, and those who sustain fracture but do not have osteoporosis. It is proposed that these diseases cause bone fragility and fractures by promoting or enhancing the accumulation of disorganized bone tissue. Hence, they have in common the feature that they are disorders of disorganized bone tissue.

We believe that fragility fractures that occur in people who do not osteoporosis are due misalignments causing ineffective, inefficient load transfer, and thus load transfer instability. This produces damage that weaken the bones, ultimately resulting in fractures.

- In a particular embodiment, the fragility fracture is an unusual type of femoral fracture denoted atypical femoral fracture (AFFs). These fractures are associated with the use of antiresorptive therapies; medications that rather preventing fractures as intended, instead paradoxically increase the risk of this type of fractures. We believe that these fractures are due to disorganized (misaligned) bone tissue within the femoral bone.

- In other embodiments, quantification of the alignment (hence, disorganization), may allow identification of spinal diseases or conditions related to ineffective interplay (or interrelatedness) between spinal structures, and thus misalignment. An example of such as spine disorders are back pain. Low back pain is one of the most common health problems with 50-80% of adults experiencing low back pain at some point in their life. Lower back pain is more common in patients in patients with hip osteoarthritis and has been attributed at least partly, to abnormal sagittal spine-pelvis-leg alignment. Hence, a disorganization within the spine-pelvis-leg system.

- Still in other embodiments, quantification of the misalignment, may allow Identification of patients at risk for joint disorders, diseases (or conditions) such as osteoarthritis (such as knees, hips, feet...). Optimal joints function depends on a good alignment.

The method may involve quantifying changes over time of misalignment (disorganization) characteristics (features or properties), and using the quantified changes to monitor the effects of diseases and treatments on organs or tissues. - In one embodiment, the organ or tissue may be bone or cartilage. In these cases, the method comprises using features of the misalignment to assist in the diagnosis diseases, prediction and monitoring of the response to the treatment of diseases affecting cartilage or bone matrix spatial alignment. This includes as examples: arthritis (e.g., osteoarthritis, rheumatoid arthritis...), periostitis, osteomyelitis or conditions such as bone loss (e.g., due to ageing, diseases, osteoporosis, immobilization...), diabetes, Gaucher Disease, Chronic Kidney Disease, metabolic diseases such as Paget Disease, Hypophosphatasia, osteomalacia or other conditions that increase the risk for fragility fractures.

- Still in the case where the organ of interest is bone or cartilage, the method may involve using the changes over time of characteristics or features disorganization to monitor the effects on interventions (exercise, physical therapies...) and treatments such using drugs to treat bone or cartilage conditions.

Regarding bone, treatments may include antiresorptives drugs such as Denosumab, bisphosphonates (alendronate, zoledronic acid, risedronate...) or anabolic therapies such as teriparatide, abaloparatide or romosozumab.

Regarding cartilage, treatments may include drugs such as Biological Diseasemodifying anti-rheumatic drugs (DMARDs) such as infliximab, etanercept, rituximab, or abatacept. This may also include other drugs such as methotrexate, leflunomide/teriflunomaide, sulfasalazine, hydroxychloroquine or other drugs and interventions used to treat affects bone, joints and cartilage.

- Still in the case where the organ of interest is bone, the method may involve quantifying changes over time of characteristics or features of the misalignment, to monitor the progress of fracture healing, and/or identifying delayed union, and/or nonunion of a fracture.

Bone and cartilage are just examples of materials (organs, tissues...) that be assessed using the measurement of the disorganization characteristics described this in invention. In other embodiments, in the medical field, any biological issue may be assessed using the disorganization characteristics or features.

The system and method may be embodied in a computer-readable medium that can be installed in personal computers (PC), Dual Xray absorptiometry machines (DXA), Computed Tomography (CT) devices or other devices for automated (if desired) measurement of the disorganization within materials (or objects) to diagnosis diseases and/or monitoring of treatments.

In some embodiments, the imaging device and the method and system (e.g., embodied in a machine-readable medium) may be combined in an apparatus for automated (if desired) diagnosis of diseases and/or monitoring of treatments

In other embodiments, the computer-readable medium may be a set of executable instructions or software embedded in a device or apparatus for automated (if desired) measurement of the misalignment and its characteristics for the diagnosis of diseases and/or monitoring of treatments

Thus, according to a second broad aspect of the invention, there is provided a method and apparatus or device for quantifying (automatically if desired) the misalignment (disorganization) between materials (or their constituents), the method and apparatus comprising;

Analyzing or processing an image with two or more materials (or objects, organs, tissues..)- a first object (object of interest) and a second object (target object);

Creating the alignment data between the objects in the images;

Analyzing the alignment data;

Analyzing includes quantifying the alignment of at least one object in relation to a second object;

Identifying and quantifying any abnormal alignment (disarrangement, disorganization) of an object (or its constituents) of interest in the relation to a target object.

In a particular embodiment, the measured misalignment is that of the femur in relation to the acetabulum. Thus, according to a third broad aspect of the current invention, there is provided a method and apparatus, for quantifying the misalignment of the femoral (including automatically if desired) in relation to the acetabulum. The method comprising:

Identifying an alignment data source containing information on the alignments of femur in relation to the acetabulum;

Assessing the alignment data. The assessment comprising extracting and separating data containing information on the alignments of the femur (and/or its components), in the relation to the acetabulum (and/or its components) within the alignment data source;

Analysing the alignments data of the femur and the acetabulum (and/or their components or constituents);

Quantifying the alignment between the femur (and/or its components) and the acetabulum (and/or its components);

Identifying abnormal alignment (misalignment) between objects (materials or components);

Quantifying the degree or extent of abnormal alignment between the femur and the acetabulum.

Quantification of the misalignment of the femur vis-a-vis the acetabulum may comprise creating a phantom custom-tailored to the specific femur within the image to obtain a referent alignment dataset.

The alignment data may be obtained from the analysis of images containing the femur and the acetabulum (or parts of these bones) acquired using any imaging technology such as dual X-ray absorptiometry (DXA), computed tomography, magnetic resonance imaging, or a camera or any other imaging source.

In some embodiments, the imaging device may contain an image processing device configured to analyze the acquired images and output alignment data between the femur and the acetabulum.

In other embodiments, the imaging device maybe in data communication (directly or indirectly) with an image processing device which is configured to analyze the acquired images and output alignment data between the femur and the acetabulum. The alignment data may be obtained from the analysis of images from an image processing software.

In this embodiment, the image analysis software or device may be adapted to output alignment data, that are then inputted (automatically) if desired in the apparatus implementing the method to measure the misalignment between materials (objects or system or many objects) described in some embodiments of the present invention.

According to a fourth broad aspect, the invention provides executable instructions (or software) that can be embodied in a computer readable medium such as software imbedded or permanently stored therein, that, when executed by a computer or processor of a computer, cause the computer or processor of the computer to perform the method for the measurement of the misalignment (disorganization) between materials, and quantify the misalignment characteristics (features or properties) described above.

It also should be noted that the method is of particular, but by no means of exclusive application to biological samples or materials or objects only.

The result of the analyses would usually be outputted for use. The results of the analyses may be outputted for review and/or further analyses. The results may also be outputted to a memory, or memory medium, for future use, later review or further analysis.

It should be noted that any of the features of the above described invention can be combined as suitable and desired.

BRIEF DESCRITION OF DRAWINGS

In order for the invention to be more clearly ascertained, embodiments are described below by way of examples, with reference to the accompanying drawing, in which:

Figure 1A is a schematic view of the system for measuring the misalignment between materials (or objects) according to one embodiment of the present invention.

Figure 1B is a schematic representation of the operation of the system for measuring misalignment (disorganization) between materials (objects or group of objects) according to one embodiment of the present invention.

Figure 2A is a more detailed schematic view of the Misalignment measurer 12 of the system of figure 1B according to an embodiment of the present invention.

Figure 2B is a schematic view of the Memory 28 of the Processing Controller 33 of the Misalignment Measurer 12 of the system of figure 1B according to one embodiment of the present invention.

Figures 3A and 3B are together figure 3. Figure 3 is with more details, a schematic view of the operation of the Misalignment Measurer 12 of the system of figure 1B according to an embodiment of the present invention.

Figure 4 is with more details, a schematic view of the operation of the Alignment Data Pre-processor 52 of Misalignment Measurer 12 of the system of figure 1B according to one embodiment of the present invention.

Figure 5 is an illustrative example of the calculation of the orientation (and thus, the alignment) in the embodiments where the object of interest is formed by many elements (or components).

The orientation of the femur (and thus, its alignment) vis-a-vis the acetabulum is used an example. The orientation of each component represented by a point (Pi) is computed as a vector (->) originating at a given selected referent point (Po) and ending Vi at the said point (Pi) . The orientation (and thus, the alignment) of the whole object may then be computed as the sum of all these vectors (-^). In this embodiment, the orientation vector at each position (Pi) may further be weighted by a measure of local properties such as the local attenuation (or intensity, or density) of pixel at the position or point (Pi) in the example described in this embodiment. Figure 6 illustrates as examples, some types of alignments quantification performed by Misalignment Measurer 12 of the system of figure 1B according to one embodiment of the present invention.

Quantification of the alignment of the femur in relation to the acetabulum in the application is used an example. Figure 6A is an original Image showing the femur (a portion) and the acetabulum (a portion is also shown). Sections or positions along the femoral have been normalized in a scale ranging from 0 to 100% at the preprocessing step by Alignment Data Pre-processor 52. Figure 6B Shows the alignment value (expressed as percent, also a normalized scale) at each position along the femoral shaft as quantified by Alignment Measurer 12 of the system of figure 1 B. At Figure 6C the quantified alignment is further illustrated as an image showing the alignment direction of different portions along the femoral shaft. Figure 6D shows the alignment value at each position along the femoral shaft computed as first order derivative in the embodiments where the alignment further is calculated as differences (or changes) in the alignment values. Figure 6E is another representation of the alignment value or misalignment (its reciprocical) at each position along the femoral shaft computed as second order derivative in the embodiments where the misalignment further is calculated as rate of changes (variation in the differences) in the alignment values.

Figures 7 is with more details, a schematic view of the operation of the Quantifier of Alignment Abnormalities 56 of Misalignment Measurer 12 of the system of figure 1B according to one embodiment of the present invention.

Figure 8 is a further example of the quantification of the alignment of the femur in relation to the acetabulum by Misalignment Measurer 12 of the system of Figure 1B according to one embodiment of the present invention. Figure 8A is an original Image showing parts of the femur and the acetabulum. Figure 8B is a displayed of the quantified alignments as displayed by the Displayer of Alignments Results 57. Also shown is an example of the Controller of Alignment Results 58 and user controllable features.

Figure 9 is another further example of the quantification of the alignment of the femur in relation to the acetabulum by Misalignment Measurer 12 of the system of figure 1 B according to one embodiment of the present invention. Figure 9B is a display of the quantified alignments by the Displayer of Alignments Results 57 in the case of the femur with no misalignment. The original with no significant abnormality is shown in Figure 9A. Figure 10C is a display of the quantified alignments by the Displayer of Alignments Results 57 showing an abnormal alignments (misalignment) presenting as a peak in the alignment curve displayed (dotted ellipse). Figure9 B1 shows the original image with the corresponding abnormality responsible of the irregularity (disorganization) Figure 10B is a magnified portion of the femur to better show the abnormality producing the misalignment.

DESCRIPTION OF EMBODIMENTS

A system for quantifying alignment and misalignment (disorganization) between materials (or objects, or structures) according to one embodiment of the present invention operates as shown schematically at 1 in figure 1A, and in more details in figure 1B. As showing in Figure 1 B, system 1 comprises:

• Alignment Data Source 23

• A Processor Module 13

The alignment data may be any type of data that is suitable for the quantification of the alignment between materials (or objects). The alignment data source is any device that produces data containing alignment information.

Alignment Data Source 23 may be an imaging device including an X-ray device, a Dual X ray absorptiometry (DXA), a computed tomography (CT), a magnetic resonance imaging, a camera, a microscope or any imaging device. In this embodiment, the acquired images of objects are then used to produce or generate the alignment data.

Alignment data source 23 may also be an image processing device (such as a software) that processes images from any imaging device to output alignment data.

Alignment data source 23 may also comprise a computer-readable storage medium, a set of executable instructions or software in which alignment data or images suitable for the quantification of the alignment have been stored. This include devices such as a Universal Serial Bus (USB) flash drives providing data storage, a hard disk drive, a Personal Computer (PC), or any other digital data storage medium.

The alignment data may also be any data suitable for alignment quantification that may be inputted via various means including directly (such as manually).

The Alignment data source may be an image of any bone(s). In this embodiment, Processor Module 13 analysis the image to output the alignment data required for alignment measurement.

In this embodiment, the alignment data is an image containing the femoral bone and the acetabulum.

In other embodiments, the alignment data may be an image of other bones such as the radius, the tibia, vertebrae, spine, or joints such as the knee, hip or wrist joints.

In the embodiments where the alignment data is an image of the bone, the alignment data source may be an imaging device such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray device, or scanning electron microscopy (SEM) used to acquire an image of the bone. The bone may be a specimen or sample, or bone in a living individual, or any other image that contains bone. The image may that of the entire bone or part (s) of the bone. The image may also be a cross section of a bone, or part of the cross section, any subsection of the bone.

The Alignment Data Source may be a bone image processing software that is configured to analyse images containing bones to output alignment data.

In this embodiment, the bone image software is configured to analyse images containing femur and the acetabulum and output the data on the alignment of the femur in relation to the acetabulum.

In other embodiments, the bone image software may be configured to analyse other bones images to generate alignment data. This may comprise bones such as the radius, the tibia, or joints such as the knee, hip or wrist joints. The image may be that of the entire bone or part (s) of the bone. The image may also be a cross section of a bone, or part of the cross section, any subsection of the bone.

Processor Module 13, as shown in Figure 1 B comprises Data input 24 and Misalignment Measurer 12.

Data input 24 of Processor Module 13 may be connected to one or more alignment data source devices and configured to receive the alignment data outputted by said Alignment Data Source devices. In some embodiments, the one or more Alignment Data Source devices are connected to the Processor Module 13 via Universal Serial Bus (USB) connecter.

Data input 24 may also receive alignment data via a computer-readable storage medium such as universal serial bus (USB) flash drive, hard disk drive, or any other digital data storage.

Data input 24 is also configured to receive alignment data via internet connection. In this case, Processor Module 13 may connect to the Internet via a wire or wireless connection such as 3G/4G/5GWifi/Wimax, etc. In some embodiments in which a wireless connection is used, a wireless module may be connected directly to the motherboard or via a USB port. In other embodiments in which a 3G/4G/5G etc... connection is used, a local Subscriber Identification Module (SIM) card may be used in order to connect to the network and allow Processor Module 13 to upload the alignment data.

Referring to Figure 1B, Misalignment Measurer 12 of Processor Module 13 comprises:

- A user interface 6; User interface 6 comprises, in this example, a display 8 and a keyboard 10. However, it will be appreciated that other known user interfaces or combinations thereof may be employed, including a computer mouse, a touch screen, a scanner, a printer and another computing device. - A Processing Controller 33; Processing Controller 33 includes several components. Referring to figure 2A, Processing Controller 33 includes an Alignment Analyzer 5 and a Memory 28 in data communication with each other; and in data communication with the Alignment Data Source 23.

Referring to figure 2B, Memory 28 includes RAM 25, EPROM 26 and a mass storage device 27. An instruction set may be stored in mass storage device 27 and, when required, loaded into RAM 25 for execution by Alignment Analyzer 5. The instruction set is adapted to control Misalignment Measurer 12 to perform the steps of the method of the embodiments as described below.

Figure 3 is a more detailed schematic view of Misalignment Measurer 12 of Processor Module 13 and its components which comprise User Interface 6 and Processing Controller 33. Shown in more details are the components of Alignment Analyzer 5 & Memory 28 of Processing Controller 33.

Alignment Analyzer 5 includes a Display Controller 10 for controlling the display of the alignment data by the Displayer of Alignment Data 51 , An Assessor of Alignment Data 50, an Alignment Data Pre-processor 52, an Analyser of Referent Alignment Data 53, an Analyser of Object(s) Alignment Data 54, An Alignment Quantifier 55, a Quantifier of Alignment Abnormalities 56, A Displayer of Alignment Results 57, A Controller of Alignments Results 58, and a final results outputter 59.

Memory 28 includes the instruction sets 281 for storage of instructions that when required are accessed for execution by Alignment Analyzer 5, Alignment Data Storage 282 for storing Alignment Data from Alignment Data Source 23 that may be uploaded, Data datasets storage 283 for storing information and data not related to the alignment data. This may include additional data that may be used for a comprehensive analysis of alignment characteristics or some accessories of results display such as sounds, music used to alert the user on some aspects of the results, Subresults storage 284 for storing intermediate results, and final results storage 285 for storing final results.

Referring to Figure 1B, Data input 24 of Processor Module 13 is adapted to input any alignment data from Alignment Data Source 23 into Misalignment Measurer 12 either for storage in Memory 28 or alignment quantification by Alignment Analyzer 5. In some embodiments, the inputted data is an image acquired from an imaging device. In other embodiments, the inputted data may be any data such any file formats contain information as an such as excel file, plain text format Comma Separated Values (CSV), pdf, word documents, or any data entered.

Again referring to Figure 3, the data inputted by Data Input 24 may be converted (if desired) into a graph or image, or any other form of display by Displayer of Alignment Data 51 controlled via Display Controller 10 so that the user can view the alignment data to be processed. This allows the assessment of the data including as examples, the suitability for the alignment analysis or verifications that the correct dataset has been inputted.

It can be seeing in Figure 3, that the step of displaying alignment data is not required for the alignment measurement. The user may control Alignment Analyzer 5 to skip the step of viewing the data if sufficiently confident that the correct data with the required specifications has been uploaded. In these circumstances, the analysis proceeds without a display of the uploaded alignment data.

Again, referring to Figure 3, the analysis process starts at step 50 with the assessment of the alignment data; a process through which Assessor of Alignment Data 50 of Alignment Analyzer 5 identifies the type of data inputted (image, file format, quality of information ...), assesses and determines if alignment data is suitable for the measurement of the alignment.

If the data is unsuitable for alignment measurement, the Assessor of Alignment data 50 via its data communication channel may instruct Displayer of Alignment Data 51 to display an alert to the user that the data is unsuitable for the operation. Various messages may be displayed such as “data unsuitable for alignment measurement’ . The reason for the unsuitability of the data may also be displayed.

If the unsuitable data can be corrected, the Assessor of Alignment data 50 again via its data communication channel with Alignment Data Source 23 may then, using the information in the instruction datasets 281 of Memory 28, instructs Alignment Data Source 23 to create an alignment data in a manner that it is suitable for the desired alignment measurement.

After the inputted alignment data has been identified and assessed as suitable for alignment measurement, the next step is for the Alignment Data Pre-processor 52 to perform a preliminary processing of data in order to prepare data in such a way it is ready for further processing.

Still referring to Figure 3, Alignment Data Pre-processor 52 performs an initial processing to prepare the alignment data. This involves a series of processing steps shown with more details in Figure 4, that transform the data in such a way that it is ready for alignment quantification.

As shown in Figure 4, Alignment Data Pre-processor 52 includes A Selector of Alignment Types 521 which is configured to select the type of alignment to be quantified by Alignment Analyzer 5, an Extractor of Relevant Alignment Data 522, a Repairer of Alignment Data 523, a Standardizer of Alignment Data 524, and a Separator of Alignment Data 525.

Alignment Analyser 5 is configured to measure many types of alignment. Thus, the initial step in the preprocessing is for the Pre-processor of Alignment Data 52 to select the type alignment to be measured by Alignment Analyser 5. This step determines the type of preprocessing or preliminary analysis needed to prepare the data for the measurement of the alignment

Regarding the type of alignment to be quantified, broadly, the alignment is quantified as the spatial position of a given material or object (third object) relative to the spatial arrangement (or position) of at least two other materials (or objects), the direction or orientation of which serves as a referent. The spatial arrangement of two referent objects may be further characterized by a line (such as a straight) starting in the first material (object of interest) and ending in second material (target object).

In some embodiments, the alignment is quantified as the extent to which the direction (orientation) defined by any given material (third object) and object of interest deviates or differs from that defined by the two referent objects (object interest and target object). Still in this embodiment, the referent direction may be predefined as standard directions such as the vertical or the horizontal, or any other predefined orientation or direction. In this, case the alignment of an object is quantified independently of the spatial position of the two referent objects.

It should be noted that this is a relatively standard approach to alignment quantification. In addition to this common approach to approach to alignment quantification, Alignment Measurer 5 is configured to further quantify other of type alignments using different approaches.

In other embodiments, measuring the alignment may comprise quantifying the alignments of many components or structures of the same object (such as the object of interest). In this case, the direction defined by a point or referent within the object of interest and any other components or structures provides a measure of the orientation of each structure (or components of the object). The alignment each component of the object of interest, is then quantified as the deviation of the direction of each component (relative to a referent in the object of interest) relative to a principal referent direction defined by the object of interest and the target object. The orientation of all the constituents forming the object of interest can then be quantified by combining all the orientations such as computing a sum or an average. The alignment is then derived from the calculated orientation.

Figure 5, shows an example of the quantification of the alignments of the components of the femur in the relation to the acetabulum.

In this example the object of interest is the femur (made of many components), and the target object is the acetabulum bone. Processor Module 13 Quantifies the alignment as follows:

A point (Po) on the femur (object of interest) is used as a referent point. Each constituents or components of the femur can be represented as a point (Pi). Thus, the orientation of each component can be computed as a vector (^) originating at a given selected referent point (Po) and ending at the said point (Pj) such as: Equation 2

Also, the orientation of any portion of the femur (or the entire) made of (n) components can quantified as a vector (->) such as:

Vn j=n

> f > _> Equation 3

Vn J PflPi j = O

To calculate the alignment of any components or constituents of the femur vis- a-vis the acetabulum, a referent is direction joining the femur and the acetabulum defined. In this embodiment of the present invention the middle of the acetabular dome (Ao) is used as the other referent point. Thus, the vector (^^)- (Po, A_o) forms the referent orientation for the calculation of the alignment.

The alignment of each component can be calculated as the deviation of its vector (->) from the referent vector ( — >).

Vi PoVo

The alignment of (n) components forming part (or the entire femur) can be calculated as the deviation of the combined vector (— >) from the referent vector ( — >).

Vn PoVo

However, quantification of the alignment as the deviation from predefined orientation is often insufficient in many instances to assess to effects of many conditions and diseases on the structural cohesion or interplay between materials or structures and thus, the stability of the system formed by many structures (organs or tissues). Thus, Processor Module 13 is configured quantifying the alignment in a way that better reflects the cohesion in the arrangement or interrelatedness between components of an object by accounting for the local material composition.

A good cohesion requires not only a good the spatial arrangement but also a correct composition of the object at any given location (or spatial position). Accordingly, Alignment Measurer 5 advantageously further quantify the alignment by accounting or including the local composition of the material or object in the quantification.

Thus, in this embodiment of the invention the quantified alignment is further weighted by a measure of the local composition of the object. To do so, the quantifying alignment is further transformed by a weighting function. This may be a constant, a variable factor or any mathematical processes that can be used to account the local material composition.

In the present embodiment, in the case of the alignment of the femur in relation to acetabulum, the orientation of each component of the femur computed as a vector (^) is further weighted by factor (K) to obtain such as:

— > = Ki * — > Equation 4

Vki PflPi

Where K, is a measure of the local material of composition.

Also, the orientation of any portion of the femur (or the entire) made of (n) components quantified as a vector (-►) is further quantified (— ►) as such: Equation 5

In this embodiment where the data source is as an image of femur and acetabulum, the weighting factor (K) is the local attenuation, intensity, or attenuation value (absolute or relative) of the pixel or voxel.

The alignment is calculated as deviation from these weighted vectors from the referent vector ( — >).

PoVo

We propose that the weighed alignment value so calculated provides a better assessment of the effects of diseases and treatments on organs and tissues such as bone and cartilage in this embodiment, than the standard non-weighted quantification of the alignment which ignores the differing and heterogenous composition of tissues forming an organ or an object (e.g. bone). As the weighted alignment contains additional information, it is also referred to, as functional alignment.

Sill regarding the type of alignment, in addition to the alignment values quantified as described above, regardless of whether the standard or weighted alignment value was quantified, in some embodiments, Alignment Measurer 5 may further quantify at each location or position, the change in the alignment value.

This because, in addition (or rather) of affecting the absolute alignment, diseases, conditions or processes may affect or change the differences (or variation) that are otherwise normal.

In these embodiments, the alignment at each is further calculated as the first order derivative of a curve representing the alignment value as a function of the location (or position) within the object. An example of quantifications of the alignment as change (or differences) in the alignment between two positions (such as contiguous ones) is illustrated in Figure 6D in the case of the alignment of the femur with the acetabulum.

In this embodiment, the alignment (A_p) at each location (p) within an object is further calculated as AC_P such as:

AC_p = A_p+1 - A_p. Equation 6

Where Ap+i is the alignment a point (p+1), A_p the alignment at point (p) and AC_P is the alignment change at point (p) and used as the alignment value at point (p). (It should be noted that this is different from the convention way of calculating the derivative).

Sill regarding the type of alignment, in addition to the change in the alignment value at each position as quantified as above, regardless of the type of alignment calculated, in other embodiments, Alignment Measurer 5 may further quantify the alignment each location (or position), as the rate of change in the alignment value- that is a difference in the way the alignment normally differs between locations (such as contiguous).

Normal differences reflect normal heterogeneity in the alignment pattern; whereas diseases and processes may affect this normal heterogeneity (pattern) regardless and independently of their effects on the actual value of the alignment. Abnormal or disorganized change (or differences) in the alignment across different locations or areas an object reflects an abnormality. We thus, propose that this approach to the quantification of the alignment by Alignment Measurer 5 may provide unique insights into health and diseases on materials, in particular organs and tissues such as bone, cartilage and joints.

In these embodiments, to quantify this heterogeneity or differing variation in the alignment pattern, Alignment Measurer 5 may quantify the alignment as the second order derivative of a curve representing the alignment value at each location or position within the object.

In this embodiment, the rate of change in the alignment (Arp) at each location (p) within an object may further calculated as A_rp such as:

A_r r_n p = AC_n P₊ +₁ 1 - AC_n p. > .. , Equation 7

Where AC_p+i is the alignment change a point (p+1) or the first derivative at this point, AC_P the alignment change at point (p) or the first order derivative at this point; and A_rp is the rate alignment change at point (p) and used as the alignment value at point (p).

An example of the quantification of the alignment as rate of change (differing differences) in the alignment between two positions (such as contiguous ones) is illustrated in Figure 6E in the case of the alignment of the femur with the acetabulum.

The pattern of change in the fashion or manner the alignment changes is critical for the quality of arrangement (organization or texture) of an object. In medicine, it is essential for the health organs and tissues. Abnormal variation of patterns (textures) reflect a disease regardless of the absolute or overall alignment.

In particular, as an example, when the alignment (or arrangement) of the elements of an object (or a system) changes in a regular, coherent or effective manner, this indicates a structurally stable and healthy object (or system); and this regardless of their absolute or overall alignment (or orientation). A force (load) applied to such a structure (e.g., a bone or muscle...) will be transfer efficiently and is unlikely to cause any damage, injury, rupture, sprain, strain or fracture.

Conversely, as an example, when the alignment (or arrangement) of the elements an object (or a system) changes in irregular, incoherent or ineffective manner, this reflects an unstable and unhealthy system; this independently the absolute or overall alignment (or orientation) of its components. A force or a load applied to such a structure (e.g., a bone or muscle...) will be transferred inefficiently and is likely to cause to cause damage, injury, rupture, sprain, strain or fracture.

For these reasons Misalignment Measurer 12 advantageously quantifies the alignment in this fashion.

Still, at step 521 , referring to Figure 4, regarding the type of alignment, selecting the alignment may comprise selection specific regions in which the alignment is to be quantified and/or the order in which the quantification of the alignment by Alignment Measurer 5 is performed.

Still regarding the type of alignment, it should be noted this is a user controllable setting in Processor Module 13. Referring to Figures, Display Controller 10 is in data communication with the Alignment Data Pre-processor 52, allowing the user to select the desired type of alignment to be quantified.

After Selector of Alignment Types 521 has selected the one or more types of alignment, the region(s) where the alignment will be quantified, and the order in which the alignment quantified, the next step in the processing is to extract from the data, the relevant alignment data for the process. Next, the Extractor of Relevant Alignment Data at step 522 may extract amongst the whole data, the data useful for the selected type of alignment measurement. The relevant alignment data extracted depends on the type alignment analysis, the location where the analysis will be performed and the order of the analysis.

Again referring to Figure 4, next at step 522, the Repairer of Alignment Data 522 of Alignment Data Pre-processor 52 is configured to detect and repair any abnormal data within the relevant alignment data.

Repairing alignment data may involve removing noise from the data using noise removal or reduction approaches such as:

- Removal of outliers

- Using moving average filters

- Using median filters.

Repairing alignment data may involve replacing missing or replacing outliers or noise in data using approaches such as: mean imputation, interpolation or extrapolation, substitution.

After step 522, the Standardizer of Alignment Data 523 of Alignment Data Preprocessor 52 standardizes the selected and repaired (if needed) alignment data.

Standardizing the alignment data may involve converting original alignment data into a predefined scale before any further processing. This may be done using many procedures such as the process of normalization.

Normalization by Standardizer of Alignment Data 523 typically involves adjusting alignment values measured on different scales (e.g., from different Alignment Data Source) to a common scale so that all alignments quantified by Misalignment Measurer 12 in subsequent steps have the same scale regardless of the Alignment Data Source.

In this embodiment, normalization is achieved using an approach similar to minimum-maximum scaling so that normalized alignment values range from 0 (minimum, bad alignment) to 100 (maximum; perfect alignment). To do so, alignments values (Aj) are converted to a normalized Alignment values (ANI) such as:

AM, = 255 Equation 8

Where MaxA and MinA are values representative of the maximum and minimum observed alignment values observed within the alignment dataset.

Standardization of alignment data has several other advantages besides ensuring a standardized alignment quantification process. This includes comparability of alignments results regardless of the Alignment Data source. This also enable universal interpretation of the alignment results of Alignment Measurer 12.

After standardization of alignments data, next at step 525, Separator of Alignment Data separates (if required) alignment data based on the purpose of their use in the subsequent processing steps.

In some embodiments, such as in the present embodiment, to accurately quantified the alignment, Processor Module 13 requires the alignment data of a referent (such as a custom-tailored phantom of the object of a material or object of interest). Thus, at this step the preprocessed alignment data is separated into at least two different datasets by the Separator of Alignment Data 525: (i.) The dataset containing information on the alignment of the objects (object of interest and target object) and (ii.) The alignment dataset of the referent

If the quantification by Misalignment measurer 12 requires at least two separate datasets at step 525, and the Separator of Alignment Data 525 can identify the required datasets, Alignment Data Pre-processor 52 via its data communication with Displayer of Alignment Data 51 is configured to display an error message or any other message to signal that the analysis cannot proceed because of missing data.

After all the required preprocessing steps have completed by Alignment Data Pre-processor 52, the next step on the measurement of the alignment by Misalignment Measurer 12 depends on whether a referent alignment data is required or not. If a referent alignment data is required next Misalignment Measurer 12 proceeds at step 53, to the analysis of the referent alignment data but limited to the preprocessed and standardized dataset. In this embodiment, the referent data is alignment data of phantom custom-tailored phantom to the object or material of interest.

The Analyser of Referent alignment data 53 is in data communication with the Analyser of Object Alignment data 54. This ensures the analysis of the referent data is performed in such a way that it can effectively be used as a referent or comparator for the quantification of the alignment of the object or material of interest (or its components). For example, the type of alignment quantified by the Analyser of Referent Alignment data 53 must be the same as the alignment quantified Analyser of Object Alignment data 54. However, although the analysis processes are similar, the results and output are designed to be complimentary.

Regardless of whether a referent alignment data is required or not, Alignment Analyser 5 proceeds at the 54 with the analysis of the alignment data of the objects. However, the analysis is limited to preprocessed and standardized subsets using similar processes as in preliminary analyses.

In the embodiments requiring a referent, at step 54, Analyser of Object Alignment data 54 performs, in communication with Analyser of Referent Data 53 performs a similar but complementary analysis to that of done on the referent data

Again referring to Figure 3, after the alignment data of both the object and the referent (if required) have analyzed at steps 53 and 54, next is for the Quantifier of Alignment 55 of Misalignment Measurer 12 to quantify the alignment of the one or more materials (or object of interest). The process of alignment of quantification depends whether referent alignment data is required or not.

In embodiments where a separate referent alignment data is not required, the alignment of the material or object may be quantified relative to a referent direction defined by an object of interest and a target object as described above at step 521 . Still in embodiments where a separate referent alignment data is not required, quantifying the alignment may also comprise quantifying the extent to which the direction of components or materials forming the object of the interest deviates from a specific predefined reference direction typically stored in the instruction datasets 281 of Memory 28. The reference may be a standard direction. Examples may include quantify the alignment as a deviation from a standard direction such as the vertical, horizontal, or any direction. The alignment so quantification may be expressed as an angle, a slope, a percent, or any other metrics.

Characteristics features of the alignment for all components of the object of interest may expressed as the mean alignment value, the mode, Standard Deviation, range, maximum and minimum or any measure of data distribution; and this without requiring a separate referent alignment data.

It should be noted this the standard approach to alignment quantification is not favor by Misalignment Measurer 12 of Processor Module 13 of the current invention. Rather a novel approach is used and favored by Processor Module 13 for many processes. This involves further quantifying the alignment by comparing or contrasting the object alignment data to that of a referent alignment data

In other embodiments such as in this embodiment, quantification of the alignment uses a referent alignment data. This is typically the alignment data of a phantom custom- tailored to an object of interest. In these embodiments, quantification of the object of interest is done by comparing, contrasting the object alignment data with the corresponding referent (such as phantom) alignment data. Many comparison operators may be used, including:

• Subtraction of object alignment data from those of the referent

• Addition of the alignments data.

• Multiplication or division of alignments data

• Identification and extraction of specific information (such as specific points or locations) on object alignment data using referent data

• Any mathematical operation to compare both data in order to extract the alignment of the object of interest. In this embodiment, Quantifier of Misalignment data 55 subtracts object alignment data from that of a custom-tailored phantom (referent). The difference (or deviation) between the alignment datasets provides a quantitative measure of the alignment of the object.

Referring again to Figure 3, after quantifying the alignment at step 55, the next step is for the Quantifier of Alignment abnormalities 56 to identify and quantify abnormal alignment (misalignment) in part(s) or whole object of interest. To do so involves quantifying alignment characteristics (features) that may distinguish a normal from an abnormal alignment.

The details of the operation of Quantifier of Alignment Abnormalities 56 are shown in Figure 7. Quantifier of Alignment Abnormalities 56 includes, an Identifier of Connectors of Alignment Zones 561, a Merger of Alignment Zones 562, a Separator of Alignment Zones 563, a Characterizer of Alignment Zone 564, a Ranker of Alignment Zones 565, and a Selector of Misaligned Zones 566 which is in communication with a Displayer of Alignments Results 57 to display the results.

The first step in the identification and quantification of misalignment is to connect locations (or portions, or areas) within the object that have similar alignment features into what is referred to in the current invention as alignment zones. The concept behind this novel approach to partitioning an object (or material) is that, if a misalignment (disorganization) is present, it will occur in alignment zones (as they have similar alignment characteristics), not necessarily in anatomical or geographical zones.

Accordingly, the first step in the quantification of misalignment (disorganization) is for the Identifier of Connectors of Alignment Zones 561 to identify patterns, trends or features (refer to as connectors) that indicate that areas (or portions, locations) within an object are connected, and thus belong to the same alignment zone. Identifier of Connectors of Alignment Zones 561 is configured to identify or recognize many types of connectors of alignment zones.

In some embodiments, Identifier of Connectors of Alignment Zones 561 is configured to identify as connector, and thus connected, locations or portions (such as contiguous or adjacent ones) with the same or an equal alignment value. This is referred to in the current invention as the “Equal Alignment Connector¹’. In this embodiment, two zones, a zone 1 (Zi) of Alignment (A_zi), and Zone 2 (Z2) of alignment (A_Z2) are identified as connected if:

A_Z1 = ^Az Equation 9

In other embodiments, Identifier of Connectors of Alignment Zones 561 is configured to identify as connector, and thus connected, locations or portions (such as contiguous or adjacent ones) which differ in alignment value by a given set value, constant alignment value (K). This connector is referred to in this invention as a “K Alignment Connector¹’. These are areas in which the alignment is changing are a constant rate. (It should be appreciated the equal alignment connector is a subtype of this connector). In this embodiment, two zones, a zone 1 (Z1) of Alignment (A_zi), and Zone 2 (Z2) of alignment (A_Z2) are identified as connected if:

A_Z2 - A_Z1 = K Equation 3

Still in other embodiments, Identifier of Connectors of Alignment Zones 561 is configured to identify as connector, and thus connected, locations or portions (such as contiguous or adjacent ones) in which the alignment value is changing in the same direction, regardless of the absolute value of the change (or difference). In other words, areas or zones sharing the same pattern of change in their alignment value. This is referred to in this invention, as a “Same Alignment Pattern Connector¹’.

In this embodiment, three zones, a zone 1 (Z1) of Alignment (A_zi), Zone 2 (Z2) of alignment (A_Z2), and Zone 3 (Z3) of alignment (A_z3) are identified as connected if:

Sign(A_Z2 — A_zl) = Sign (A_Z3 — A_z2) Equation 10

Misalignment Measurer 12 advantageously uses this connector in this embodiment of the present invention as it allows the identification of zones in which the change in alignment has a coherent, organized, orderly pattern and thus reflects stability and health.

Still in other embodiments, any connector may be used including connectors using any other pattern of changes or differences in the alignment values to identify alignment zones.

Still referring to Figure 7, after identifying all connectors, at step 562, the Merger of Alignment Zones uses the connectors identified at step 561 to merge portions, locations, areas of the object of interest into alignment zones. The merging process is done at the location (or position) where the connectors were identified.

The merging of connected areas allows the partitioning of the object into alignment zones (distinct from anatomical or geographical zones). Thus, still referring to Figure 7, the next step is for the Separator of Alignment Zones 563 to separate, and extract the alignment zones. The isolated or extracted alignments may be use for many purposes.

- This includes displaying the isolated and extracted alignment zones as results. Referring to Figure 3, Quantifier of Alignment Abnormalities 56 is in data communication with Displayer of Alignment Results 57 which it may instruct to display the alignment zones such as for visualization as part of the results of the alignment quantification. The separated and extracted alignment zone may be outputted as results by Final Results Outputter 59

- However, in most cases besides using isolated and extracted alignment zones as results, Misalignment Measurer 12 may further process the alignment zones for purposes such as identifying and quantification of abnormal alignment (misalignment). To do so, alignment features or properties are quantified not only in the entire object, but also in each alignment zone. Again, it should be noted that alignment and misalignment (its reciprocal) or disorganization are used interchangeably.

Again, referring to Figure 7, the next step is for the Characterizer of Alignment Zones 564 to quantify alignment (or misalignment) features or properties of each alignment zone. Features quantified include but are not:

1- The Position or location of each alignment zone;

2- The Size, areas or width of each alignment zone;

3- The Peak or maximum alignment value in each

4- The smallest or lowest alignment value in each zone.

5- The Range of alignment values in each zone. This calculated as the difference between the maximum and minimum alignment values.

6- The Mean alignment in each zone.

7- The Heterogeneity of alignment values in each zone. This can be quantified as the standard deviation, the interquartile range or any other measure of the heterogeneity.

8- The Magnitude. This is the total value of alignment in each zone.

9- The Sharpness of the alignment in zone. This reflect or captures the extent to which the maximum alignment value differs from all other alignment value in the alignment zone. In other word, the pointiness of the peak alignment value in the zone.

In this embodiment, the sharpness is calculated as

Sharpness Equation 11

Many other indices or features of the alignment zones may be quantified by the Characterizer of Alignment Zones 564 such as the median, mode, skewness or kurtosis.

In particular, Characterizer of Alignment Zones 564 may quantify the alignment density. That is, the number of alignment zones divided by the total number of zones within the object of interest. The higher the alignment zone density, the greater the heterogeneity in the alignment of different sections or portions of an object

In addition, the Characterizer of Alignment Zones 564 may compute any of the combination of the above-mentioned features or parameters to further characterize the alignment zones. This may include as examples:

- Combining the sharpness with the range or size to identify zones such as the widest and sharpest alignment zones. Zones with these combined features are more likely to be abnormal -i.e. , misaligned.

- Combining the sharpness and heterogeneity to identify zones such as sharpest and most heterogeneous alignment zones. These zones are more likely to be abnormal -i.e., misaligned.

- Combining the location of alignment zone with an alignment feature such the sharpness to further and characterized the alignment zone. This may allow identification for examples of sharpest and contiguous alignment zones as an example.

In addition, the Characterizer of Alignment Zones 564 may compute the above- mentioned indices or their combination on the entire or whole object of interest, or selected part(s) or portion(s) of it.

It should also be noted the selection of the characteristics of the alignments to be quantified Characterizer of Alignment Zones 564 is a user controllable setting in Processor Module 13 allowing the user to select the one or more features, or a combination of features to better and comprehensively characterize each alignment zones, the entire object or part(s) of it.

Again, still referring to Figure 7, after characterizing alignment zones including quantifying their features, next at step 565, Ranker of alignment zones ranks or classifies the alignment zones. The purpose of ranking is to highlight alignment zones with some distinct features (or properties). This includes for example, highlighting alignment zones with features most likely to be result of an abnormality alignment (misalignment). To do so, the Ranker of alignment zones 565 may employ many criteria depending on the purpose of the classification.

Any strategy for assigning ranks may be used by The Ranker of alignment zones 565. This may include ranking alignment zones in increasing order (highest to lowest), in decreasing order (lowest to highest) of a magnitude for a selected feature (or combination thereof).

Ordinarily, the Ranker of alignment zones 565 includes an alignment features (such as the peak, maximum, sharpness, magnitude....) or any combination of features in the ranking.

When a combination of features is entered in ranking, a weighting factor may be assigned to each feature. Furthermore, the order in which the features have selected and included in the ranking may determine the ultimate rank of each alignment zone.

The order selection and inclusion of features in the ranking by the user may be based on prior knowledge such as the role of the feature in the disease or condition likely to cause abnormal alignment (misalignments).

Still in embodiments in which many features are used for ranking, the Ranker of alignment zones 565 may classify alignment zones based on an aggregated (or overall) ranking score. The aggregation of ranks for different zones, and different features may be done by using many operations. This may comprise calculating scores such as the average rank, the median, or the mode. As example, a zone (Z) may be ranked first (1) for Peak, second (2) for heterogeneity, third (3) for sharpness. Thus, the average rank, for zone (Z) for these two features is second (2) (i.e. , 1 +2+3/3= 2)

The ranking criteria used by Misalignment Measurer 12 may be a preset feature stored in instruction sets 281 of Memory 28 of Processor Module 13 and used by the Ranker of alignment zones 565 to automatically rank alignments zones.

The ranking criteria is also a user controllable setting in Processor Module 13 allowing the user to select the criteria including features to the used by the Ranker of alignment zones 565 to rank alignments zones, the order for Ranking, and any combination of features to be used for the Ranking.

Again, still referring to Figure 7, after the characterizing and ranking alignment zones to highlight those with distinct features in particular those likely to be abnormal (misalignedO, at step 566, the Selector of Misaligned Zones selects between alignments zones, those with alignment features that are abnormal (or likely to be so). In other words, selects misaligned zones amongst alignment zones. Many processes may be used to select abnormal misalignment zones. In some embodiments, selecting or identifying alignment zone as an abnormal zones (misaligned) may comprise selecting one or zones with a given alignment feature or combination thereof (e.g., peak, sharpness, magnitude ....) above a selected predefined value (threshold). As example, identifying as abnormal (misaligned), an alignment zone with a peak alignment value exceeding 70% (in a normalized scale) (see Figure 10C as an illustration). The selection of the predefined may be based on the value the alignment feature in normal a population of individuals known to have no alignment abnormality (no misalignment).

In other embodiments, selecting or identifying an alignment zone as an abnormal zones (misaligned) may comprise selecting one or more zones with an alignment value for given an alignment feature or combination thereof (e.g., peak, sharpness, magnitude ....) above that of a reference population known to have normal value of the given alignment feature (s). Values considered as abnormal (indicative of misalignment) could be as examples, a value above said the 95^th percentile, or below said the 5^th percentile, above or below 2.5 Standard Deviation from the normal reference population.

Still in other embodiments, selecting or identifying an alignment zone as an abnormal zones (misaligned) may comprise selecting one or more zones with an alignment value (for a given alignment feature, or combination thereof) that deviates significantly from that of other alignment zones within the object (or material). Just as a few examples this may comprise:

- Selecting or identifying an alignment zone as an abnormal zone (misaligned) comprising selecting one or more zones with an alignment value (for a given alignment feature, or combination thereof) 1.5 folds below the first quartile, or 1.5 folds above the third quartile, relative to the value of that feature in other alignment zones within the object.

- Selecting or identifying an alignment zone as an abnormal zones (misaligned) comprising selecting one or more zones with an alignment value (for a given alignment feature, or combination thereof) 3 Standard Deviations (SDs) above or 3 Standard Deviations below the mean value of the object of interest for that trait (or feature). (It should be appreciated that any number standard deviation above or below the mean may be used as the threshold used for the classification- A value 2.5 SD could also be used).

Still in other embodiments, selecting or identifying an alignment zone an abnormal zones (misaligned) may comprise selecting the zone with the highest aggregate ranking score for a selected number of alignment features. As an example, a zone may be classified as abnormal (i.e. , misaligned) if it ranks first for more than any number of selected features (e.g., 5 features).

Still in other embodiments, in normally symmetrical structures (objects, or materials), identifying and quantifying misalignment may involve quantifying the extent to which the alignment is unequal (differs) between conventionally (or normally) symmetrical parts or aspects of the structures (or objects). In order words, quantifying the magnitude of alignment asymmetry for one or more selected alignment features or their combination.

Still in other embodiments, an object (or part of it) may be treated or considered as an alignment zone. Similar criteria used above to identify an alignment zone as abnormal zone (misaligned) may then be used to identify the object as abnormal (misaligned).

Still in other embodiments wherein the entire an object (or part of it) is assessed for misalignment, the entire object (or part of it) may be identified and classified as abnormal (misaligned) if the mean value (a median, a mode, or any central tendency) of one or more alignment features in an object (or part of it) deviates from a preset, expected or normal reference value.

In this embodiment, the normal or expected alignment may be predefined may be based on the observed alignment value in a normal population.

The Selector of Misaligned Zones 566 may select any number of alignments zones, using any desired ranking. This has many uses including allowing the user to better visualize or appreciate any abnormal zones. The number of selected alignment zones, and the criteria for selecting of abnormal alignment zones (misaligned) may be a preset feature stored in instruction sets 281 of Memory 28 of Processor Module 13 and used by The Selector of Misaligned Zones 566 to automatically select abnormal alignments zones (or likely to be).

The number of selected alignments of zones likely to abnormal (misaligned) may be a preset feature stored in instruction sets 281 of Memory 28 of Processor Module 13. This may also be a user controllable setting in Processor Module 13. Any number of alignments zones may be selected - e.g., the top five alignments with the highest sharpness. This may help the user more easily select zones likely to be abnormal

Again, referring to Figure 3, after quantifying alignments abnormalities, next, Misalignment Measurer 12 of Process Module 13 displays the results of the alignments measurement at step 57.

The Displayer of Alignments Results 57 is in data communication with the user interface 6 via the Controller of alignment Results 58. This allows the user to select and control the type of analysis to be performed by Misalignment Measurer 12 using user controllable features described above.

The alignment results may be displayed as an image, a curve, a table, a graph or any other form of display. When displayed as a curve, the position or location within the object may be shown on the x-axis and the alignment value at each position on the y-axis.

Figure 8B. is an example of a display of alignments results displayed as a curve by the Displayer of Alignments Results 57. The results displayed is that of quantification of the alignment of the femur in relation to the acetabulum by Misalignment Measurer 12 in the implementation of the system of figure 1B according to one embodiment of the present invention. Also shown is an example of the Controller of Alignment Results 58 with some user controllable features. The alignment. Figure 8A is the original Image.

Figure 10C is an example of a display of alignments results displayed as a curve by the Displayer of Alignments Results 57. Displayed and clearly visible are abnormal alignments (misalignments) with very high peaks (maximum) alignment values in some alignment zones (dotted ellipse). The misalignment is weighted abnormal alignment due to irregularity (abnormality) shown in Figure 10A and more clearly in Figure 10A. This is the results of the measurement of the alignment of the femur in relation to the acetabulum by Misalignment Measurer 12 in the implementation of the system of figure 1 B according to one embodiment of the present invention. Figure 9B shows for comparison a display of the alignment results on the same objects (femur in relation to the acetabulum) but without the abnormality on the femur. It can be seeing that no abnormal alignment is visible on the screen of Displayer of Alignments Results 57. Figure 9A shows the original image when no abnormality is present.

When displayed as a curve or graph (as shown as examples in Figure 8 and 9). The misalignment at each position of the object may be referred to as an alignogram. The curve (alignogram) may be static or dynamic (changing).

In the embodiments when the curve (alignogram) is dynamic (changing), Displayer of Alignments Results 57 is configured to automatically change the alignment analysis by Misalignment Measurer 12 and does so by automatically changing alignment measurement settings such as for examples, the type alignments, alignments zone ranking criteria. This is done by using the instructions stored in instruction sets 281 of Memory 28 of Processor Module 13. By doing so, Displayer of Alignments Results 57 displays an alignment curve that automatically changes the alignment pattern over time showing different, changing alignments graphs akin or comparable to an oscilloscope.

In embodiments wherein the alignment result curve is dynamic, Displayer of Alignments Results 57 may be configured to stop at portions of the curve that fulfil criteria for abnormal alignment (misalignment) as identifying automatically using preset criteria, or criteria inputted by the user via user interface 6. A stop or pause of the alignment curve may be signaled using various signals such as a flashing light, noise, or sounds stored in Data Storage 283 of Memory 28 of Processor Module 13.

Referring again to Figure 3, At step 59, the outputter of Final Results 59 may output the final results. This may involve printing the results, downloading or transferring the analysis results to other devices. The method and apparatus for measuring the alignments between objects (or materials), identifying and quantifying abnormal alignments (misalignments) according to the embodiments of the present invention has several applications. It has the potential to revolutionize approaches to the quantification of characteristics of objects in several fields or disciplines.

In particular, in the field of medicine, many disease processes, conditions or treatments change alignment features. This involves changing the morphology (such as size, shape....) of organs and tissues, or changing the spatial arrangement of the materials or components forming these organs and tissues.

Changes in alignment features can be produced by a wide range of medical diseases and conditions including but not limited to cancers, benign conditions such as tumors, inflammatory processes such as due to infection (e.g., abscesses,...), autoimmune processes (e.g., arthritis..), metabolic conditions (metabolic bone diseases such as osteoporosis, Paget disease, Hypophosphasia... ), injuries or damages, degenerative processes, vascular diseases such as stroke or ischaemic heart. Accordingly, quantification of the alignments and its properties according to the embodiments of the present invention has the potential to revolutionize the management of these conditions including their prevention, diagnosis, investigations, and treatments.

In some embodiments, the present invention is a method and apparatus for providing a quantitative measure of the alignment of bones (and its components). More particularly, the alignment of the femur vis-a-vis the acetabulum as used an example in this embodiment. This has several applications as many bone diseases and conditions may be due alignment abnormalities (misalignment) as described above.

One of the applications of the present invention is to use the measurement of femoral alignment and its abnormality (misalignment) to identify diseases or conditions affecting the femur and more bones.

An example of the applications of the current invention is the use of femoral (or misalignment) for the prevention, diagnosis, treatment or monitoring of a particular type of femoral fracture referred to as atypical femoral fractures (AFFs). This an unusual type of fracture that occurs sometimes during upright stance with minimal to no obvious trauma in patients taking bone active drugs called antiresorptives amongst which are denosumab and bisphosphonates such as alendronate, risedronate, and zoledronic acid. We recently observed that the orientation of the femur (and its alignment) in relation to the acetabulum plays a critical role in the occurrence of these fractures. A good alignment between of the femur andd the acetabulum has a protective role whereas in contrast, a poor or bad alignment of the femur increasing the risk for these fractures. Thus, the quantification of the alignment of femur vis-a-vis the acetabulum according to some embodiments of the present invention provides tools for the diagnosis, prevention, and the monitoring this condition. Furthermore, it may serve as a guide for therapeutic decision with for example, people with poor alignment (misalignment) avoiding, or not receiving of antiresorptives

This has important public health applications and significance. Antiresorptives, in particular, Denosumab and biphosphonates (BPs) are widely used drugs with well- established anti-fracture efficacy. However, their use is currently threatened by the fear of this rare but devastating side-effect - atypical femur fractures (AFFs). Public concern has been linked to a greater than 50% decrease in biphosphonate use. Therefore, reducing or alleviating the concern about AFFs, and reducing the incidence of AFFs is a major public health issue. It is expected that one of the applications of the present invention is its use to assess to the alignment of the femur to assist in the identification of patients who do not sustained AFFs despite being on antiresorptives therapy. These patients with no femoral alignment abnormality; a feature quantifiable with in some embodiments of the present invention. Patients with no femoral alignment abnormalities can be reassured to continue antiresorptives therapy (E.g., Denosumab, Alendronate, Zoledronic acid, risedronate ...) thereby addressing this growing public health problem. In those not yet on treatment, a good alignment may provide reassurance that antiresorptive can be safely initiated. Conversely, in those with misalignment at the initiation of therapy and/or who develop misalignment during therapy, antiresorptives could be stopped and alternative therapies such as osteoanabolic osteoporosis therapies could be considered. Osteoanabolic therapies may comprise teriparatide (intermittent parathyroid hormone), or Parathyroid hormone-related protein (PTHrp) agonist such as abaloparatide or Humanized anti-sclerostin monoclonal antibody such as Romosozumab.

Besides its use as an aid in the management patients at risk for atypical fractures, the measurement of alignment has several other applications. In particular, a misalignment may also result in extra forces and frictions on joints leading to diseases such as osteoarthritis (e.g., hip osteoarthritis). Furthermore, a misalignment of hips may affect joints below the hip joint, or joints whose function depends on the hip such as spinal joints, knee, ankle, and feet joints by imposing unusual or excessive stress and strain on those joints. This may result in conditions such as osteoarthritis of those joints also. Thus, misaligned femurs can cause conditions such as back pain, sciatica, knees, shoulders or knees pain, muscle pain (myalgia). Conversely, properly alignment of the femur results in less stress and strain on the spine, knee joint, or more distal joints. Thus, less risk for diseases affecting these locations.

Accordingly, it is anticipated that the measurement of the misalignment, in particular that of the femur in the relation to the acetabulum, according to one of the embodiments of the present of the current invention will be use to assess and/or monitor the effect diseases and treatments on the spine, knee joint, ankle and feet joints, muscles and other soft tissues.

Poor alignment of bones (such as a misalignment of the femur with the acetabulum) may also contribute to other diseases including as examples:

(i.) Fragility fractures, arthritis and other diseases. Misalignment may contribute to this condition through various mechanisms. As examples, misalignment of the hip may alter the body centre of gravity increasing the risk of fall. Misalignment may also cause abnormal, ineffective, incoherent transfer of load (forces) through the bones. This may produce damages, wear and tear. If the condition (i.e., misalignment) persists, the accumulation of damages may ultimately make bone weak, brittle and thus, produce fragility fractures, and damages and deterioration of cartilages.

Fragility fractures identifiable by the measurement of alignment according to some embodiments of the current invention may include fracture due to ageing (e.g., osteoporosis), but also those without osteoporosis (normal bone density or osteopenia). Misalignment (disorganization) may also cause fractures such as stress fractures, atypical fractures, or fractures due to genetic or metabolic conditions (diabetes, kidney disease, Gaucher Disease, Fabry, Hypophosphatasia, osteomalacia, Paget disease, rickets, periostitis, osteomyelitis...).

Regarding joints and cartilage diseases, misalignment may produce or increase damages on the cartilages, wear and tear, and thus diseases and conditions such as osteoarthritis.

(ii.) Treatments failures such those used to treat joints, bones or cartilage conditions. This includes procedures such as arthroplasty (hip, knee...), vertebroplasty, and spinal fusion. Abnormally aligned bones or cartilage (or their components)-i.e., disorganization- may leads to abnormal stresses after these procedures.

Thus, measurement of alignment or disorganization according to some embodiments of the current invention may provide a mean for identifying individuals likely to have a good response to these procedures. Furthermore, this measurement may provide a guide on the strategy or how the procedure should be performed without producing a misalignment, and/or correcting any existing misalignment.

(iii.) Fractures healing. Proper alignment of the bones (and its components) is essential for fracture healing, in particular good fracture union. A misalignment can negatively impact fracture healing with consequences such as delayed union, or nonunion, or malunion of fractures.

Thus, measurement of alignment or disorganization according to some embodiments of the current invention may provide a mean assessing and monitoring (in particular quantitatively) the process of fracture healing. Moreover, this measurement provides a mean for identifying a delayed fracture union, a non-union or a malunion.

(iv.) Fractures Diagnosis. A normal bone has correctly alignment structure or components at various scales (macro, micro, and nonoscales). Any fracture produces misalignment. Accordingly, quantification of bone alignment or disorganization as described in some embodiments of the present invention can be used to assess diagnose, and monitor fractures.

Quantification of misalignment of bone such as described in some embodiments of the present invention can be used as an aid in therapeutic decisions. Alignment measurement may help design more comprehensive and appropriate therapeutic strategies. As a few examples:

(i.) The use the misalignment measurement may assist in the design of devices or treatment aimed at correcting misalignment such as orthotics or prosthetics.

(ii.) Exercise and physical therapies. The use of the measurement of alignment or disorganization may to assist in designing and implementing a more appropriate and more effective regimens. As an example, using the alignment measurement to avoid exercise regimens that may be harmful and maximising those that will be most beneficial.

In some particular embodiments, measurement of alignment according to some embodiments of the current invention may be used to assess and monitor the effects of therapies such those used for the management of arthritis. This includes Biological Disease-modifying Anti-Rheumatic Drugs (DMARDs) such as infliximab, etanercept, rituximab, or abatacept. This also conventional DMARDs such as methotrexate, leflunomide, sulfasalazine, hydroxychloroquine or other drugs and interventions used to treat diseases producing bone erosion and/or cartilage damage. The reduction of cartilage damage, and the reduction of localized and generalized bone loss due to therapies may result in the changes in the spatial organization of bone or cartilage tissue, and thus the alignments. The resulting changes in the alignment are uniquely quantifiable by System 1 or by Processor Module 13 according to some embodiments of the present invention.

Still in some particular embodiments, measurement of alignment according to some embodiments of the current invention may be used to assess and monitor the effects of therapies such those used for the management of bone diseases. This includes drugs used to prevent fractures (anti-fractures medications) and approaches used to enhance fracture healing or drugs used to improve bone quality such as asfotase alfa in patients with Hypophosphatasia. These therapies may act by improving (or enhance) the spatial organization, arrangement of the bone matrix (tissue or components) of bone, thus its alignment. Regarding drugs used to prevent fracture. These are drugs such as antiresorptives therapies (denosumab, alendronate ....) or anabolic therapies (teriparatide, abaloparatide, romosozumab...). These drugs acts by preventing bone loss and/or increasing the bone tissue mineralization by promoting the formation of new bone. These processes will produce changes in the alignments between constituents or components of bone matrix. The results changes in the alignment can be measured using the assessment of System 1 or Processor Module 13 according to some embodiments of the present invention.

Approaches used to promote fracture healing may include drugs such as Growth factors, Bone morphogenetic proteins (BMPs), selective prostaglandin agonists, biophysical enhancement such as electromagnetic fields, or any osteogenic material. These approaches produce changes in the alignments between constituents or components of bone matrix; alignment changes can also be measured using the assessment of System 1 or Processor Module 13 according to some embodiments of the present invention.

When used to monitor diseases, or treatments changes in alignments characteristics overtime may be used.

Quantification of alignments as done by System 1 or Processor Module 13 has many other applications, especially in the field of medicine. The diseases and therapies mentioned above are just examples, and are by no meaning an exhaustive list of the use of the analysis method and apparatus.

Referring to figure 3 again, Misalignment Measurer 12 of Processor Module 13, may (if desired) through Outputter of Object Analysis Results 59 outputs the results of the analysis of the objects. The results may include the characteristics of the alignments between materials (objects or structures) and/or illustrative images and graphics. Again, it should be noted that these could be results for entire object (or tissue, or organ, or material), or selected part (s) of objects. It should also be noted that the selection of the results to be outputted is a user controllable setting in Processor Module 13 allowing the user to select the results to be outputted or Display by Displayer of Alignment Results 57.

Again, referring to Figure 3, Final Results Outputter 59 of Misalignment Measurer 12 outputs the final analysis. The results outputted by Results Outputter 59 may comprise analysis results, graphics, and/or images. Results may be outputted as an excel spreadsheet, a a comma-separated values file, a word document, a pdf document or any other document. In addition (if desired), the analysis results may be displayed on the control screen 8 of user interface 6 of Misalignment Measurer 12 (Figure 1B).

Still referring figure 3, Displayer of Alignments Results 57 may display images or graphs depicting for example, alignment graphs for visualization and/or illustration as for examples of the effects of diseases and treatments the alignment of organs or tissues.

In one embodiment, the invention is a medical device such as an imaging device with an in-built image processing software implementing the processing steps of Processor Module 13.

In another embodiment, the invention is a set of executable instructions or software (embodied in a computer readable medium with the executable instructions imbedded or permanently stored in it), that, when executed by a computer, or processor of a computer, makes or triggers the computer or processor of the computer to measure the misalignments between components of a material (materials, objects or structures) as described above, and to output the outputs described above.

Still in another embodiment, the invention is a medical device with an internet connection allowing remote control of System 1 or Processor Module 13 of Figure 1B, thereby enabling, for example, remote, (fully automated if desired) online of acquisition of images (or alignment data) and/or analysis of images to output alignment data, analyse the alignment data to perform the required alignment measurements, and output the analysis results. The results of the analysing steps would generally be outputted or Display on a screen so that they can be used to characterize the alignment between materials or objects. When applied to the medical field, the analysis results may be used to assist in the prevention, diagnosis and treatments of diseases. The results may also be outputted to a memory, or memory medium for later used.

It should be noted that when applied in the medical field, the present invention is not limited to bone or cartilage. The invention is described above in the context of an unspecify object, structure (or materials) of unknown constituents in an unspecify Alignment Data Source. It is therefore, envisaged that the ability to identify and quantify the alignments between materials, and quantify its characteristics (automatically if desired), will have applications in many disciplines.

The ability to measurement the alignment, and thus detect any abnormal (disorganized) spatial arrangement of materials (structures) or changes thereof, is therefore a feature useful in virtually all disciplines.

In other areas of medicine, the applications of the invention include but are not limited to its use to assist in the diagnosis and monitoring of the effects of treatments and interventions on diseases such as calcifications, myocardial infraction, stroke, tumors, cancers, abscesses. Identification and characterization of the alignments between materials as described above, in this invention, may be critical for the assessment of these conditions.

Outside of the field of medicine, measurement of the alignment between materials or structures could be used to characterized the spatial arrangements and thus, the quality of many materials and structures including but not limited to the quality of rocks in petroleum engineering; the quality of construction materials; materials used in various other industries; and in the pharmaceutical industry, the mechanical or material quality of tablets. Moreover, the pattern alignments quantified as described in the current invention can be used in areas such as computer vision, artificial intelligence, machine learning & pattern recognition.

It will be appreciated that the embodiments described above are cited by way of examples, and many modifications, substitutions, changes, and equivalents within the scope of the invention may be readily effected by those skilled in the art. While various embodiments have been shown and described, it will be understood that there is no intent to limit the invention by the disclosures above.

In the claims that follow and in the description of the invention above, the word “comprise” or variations such as “comprises” or “comprising” is used in an inclusive sense, that is, to specify the presence of the stated features but does not preclude the presence or addition of further features in various embodiments of the invention. The words alignment, misalignment, disorganization, disarrangement, disarray, instability (in the context of alignment) are used interchangeably. The words density, attenuation, or intensity are used interchangeably. Also, the words objects, structures, organs, tissues, materials, constituents, components are used interchangeably.

In addition, any reference herein to prior art is not intended to imply that such prior art forms or formed a part of the common general knowledge in Australia or any other country.

Claims

1. A method for quantifying (automatically if desired) the alignment between at least one object or material or structure (object of interest) and at least one other object or material (target object), the method comprising:

Identifying the alignment data between at least one object of interest, and one target object from an alignment Data source

Assessing the alignment data. The assessment comprising extracting and separating the alignment data on the two or more objects;

Analysing the alignment data;

Quantifying the alignment between at least one object of interest and one target object.

2. A method as claimed in claim 1, comprising:

Identifying one or more abnormal alignments (misalignment, disorganization) between the at least one object of interest and the at least one target object;

Quantifying the extent or magnitude of the identified one or more abnormal alignments (misalignment, disorganization).

3. A method as claimed in claims 1 & 2, wherein quantifying the alignment, identifying, and quantifying abnormal alignment(s) between the at least one object of interest, and the at least one target object comprises comparing the alignment of one object (or its components) to a referent (comparator) alignment data.

4. A method as claimed in claim 3, wherein the comparator or referent is the alignment data of a phantom custom-tailored to the one or more objects (or materials) of interest.

5. A method as claimed in claim 4, wherein using a referent (or comparator) alignment data comprises performing any mathematical operation as subtracting, dividing, multiplying the alignment of the referent from that of the one or more object of interest.

6. A method as claimed in claim 5, wherein creating a custom-tailored phantom of an object of interest comprises:

Simulating a modification of the morphology (such as size, shape....);

Or simulating a modification of the composition or spatial organization of constituents of the said object of interest;

Or simulating a modification of both the morphology and that of composition or spatial organization of constituents of the said object of interest.

7. A method as claimed in claim 6, wherein creating a custom-tailored phantom of an object to obtain alignment data to serve as comparator (or referent) comprises assigning a fixed attenuation or intensity to the said object.

8. A method as claimed in claims 1 to 7, wherein the quantified alignment is a weighted alignment value.

9. A method as claimed in claim 8, wherein weighting the alignment value involves combining the alignment value with a measure of local material composition such as the local density, the local intensity, or the local attenuation.

10. A method as claimed in claims 1 to 9, wherein the alignment is quantified as changes (or variations), or differences in the alignment between different portions (positions, or locations) within the object.

11. A method as claimed in claim 10, wherein changes (or variations) in the alignment between different portions (positions, or locations) are calculated as the first order derivative.

12. A method as claimed in claims 1 to 9, wherein the alignment is quantified as the rate of changes (or pattern of variations) or changes in the differences in alignment values between different portions (positions, or locations) within the object.

13. A method as claimed in claim 12, wherein the rate of changes or differences (or pattern of variations) in the alignment between different portions (positions, or locations) are calculated using the second order derivative or any other derivatives.

14. A method as claimed in claim 1 wherein quantifying the alignment involves classifying or partitioning an object into alignment zones.

15. A method as claimed in claim 14, wherein partitioning the object into alignment zones involves allocating or assigning to the same alignment zone portions or areas (such as contiguous) of an object which have the same alignment value.

16. A method as claimed in claim 14, wherein partitioning the object into alignment zones involves allocating or assigning to the same alignment zone portions or areas (such as contiguous) of an object which have the same difference in alignment values.

17. A method as claimed in claim 14, wherein partitioning the object into alignment zones involves allocating or assigning to the same alignment zone, portions or areas (such as contiguous) of an object in which changes (or differences) in the alignment value between three or more zones occur in the direction.

18. A method as claimed in claim 17, wherein three zones, a zone 1 (Zi) of Alignment (A_zi), Zone 2 (Z2) of alignment (A_Z2), and Zone 3 (Z3) of alignment (A_z3) are assigned to the same alignment zone if:

Sign(A_Z2 — A_zl) = Sign (A_Z3 — A_z2)

19. A method as claimed in claim 1 to 18, wherein quantifying misalignment comprises quantifying misalignment characteristics (or features) in an alignment zone such as:

The maximum (peak) misalignment value,

The minimum misalignment value,

The range of misalignment values,

The position, or location where the alignment zone,

The size, area or volume zone,

The magnitude or total value of alignments in the zone,

The heterogeneity of the alignments,

Any combination of the misalignment features.

20. A method as claimed in claim 19, wherein quantifying alignment comprises combining the peak (the maximum alignment value) and the total alignment values referred to as sharpness such as:

Sharp r ness = - — - Total alignment

21. A method as claimed in claim 1 to 20, wherein identifying and quantifying abnormal alignment (misalignment) involves quantifying alignment features in alignment zones

22. A method as claimed in claim 1 to 20, wherein identifying and quantifying abnormal alignment (misalignment) involves quantifying in alignment features in an entire object, or part(s) of the said object.

23. A method as claimed in claims 1 to 21, wherein identifying an alignment zone as abnormal (misaligned) involves quantifying the extent to which one or more alignment features in the alignment zone deviates from a preset or reference value.

24. A method as claimed in claims 1 to 22, wherein identifying an object (or part of it) as abnormal (misaligned) involves quantifying the extent to which the mean (a median, a mode, or any central tendency) value of one or more alignment features in an object (or part of it) deviates from a preset or reference value.

25. A method as claimed in claims 1 to 21, wherein quantifying misalignment involve quantifying the extent to which the value of one or more selected alignment features in an alignment zone differ from that observed in other alignment zones within the same object.

26. A method as claimed in claim 25 comprising identifying an alignment zone as an abnormal zone (misaligned) involves selecting one or more zones with an alignment value (for a given alignment feature, or combination thereof) 1.5 folds below the first quartile, or 1.5 folds above the third quartile, relative to the value of that feature in other alignment zones within the object.

27. A method as claimed in claim 25 comprising identifying an alignment zone as an abnormal zones (misaligned) involves selecting one or more zones with an alignment value (for a given alignment feature, or combination thereof) 3 (or any other number of) Standard Deviations above, or 3 (or any other number of) Standard Deviations below the mean value of the object of interest for that trait (or feature).

28. A method as claimed in claims 1 to 21, wherein identifying an object as abnormal (misaligned) comprises quantifying the magnitude of asymmetry in one or more alignment features in a normally symmetry object (or structure), and identifying as abnormal (misaligned) the existence of a difference in one or more alignment feature (or a combination thereof).

29. A method as claimed in claims 1 to 28, comprising displaying one or more of the analysis results in an image displaying device.

30. A method as claimed in claim 29, wherein the displayed results are configured to automatically change over time to display different aspects one or more of the analysis results.

31. A method as claimed in claims 1 and 28, comprising an image displayer for displaying one or more of the analysis results in an image displaying device, the system comprising:

An image displayer configured to automatically display over time different alignment quantification results types for the same object;

And/or highlight alignment portions or alignment types that meets predefined criteria such as criteria of abnormal alignment zones or abnormal alignment.

32. A method as claimed in claim 31 , wherein highlighting alignment portions or types in an image displayer may comprise stopping or pausing the changing display, emitting a sound, or a flashing light.

33. A method as claimed in claims 1 to 28, wherein the alignment data source is an imaging device.

34. A method as claimed in claims 1 to 28, wherein the alignment data source is a set of executable instructions or software.

35. A method as claimed in claims 1 to 28, wherein the two or more objects are bones.

36. A method as claimed in claims 1 to 28, and 35, wherein the two bones are the femur and the acetabulum.

37. A method as claimed in claims 1 to 36, comprising using one or more of the alignment analysis results to assess the effects of diseases or conditions on organ or tissues.

38. A method as claimed in claims 37, wherein the organ (or tissue) is bone, joints, or cartilage.

39. A method as claimed in claim 38, wherein the condition is bone fragility predisposing to an increase risk for fracture.

40. A method as claimed in claim 39, wherein bone fragility is due to osteoporosis.

41. A method as claimed in claim 39, wherein bone fragility presents as fragility fracture in people who do not have osteoporosis.

42. A method as claimed in claim 39, wherein bone fragility presents as fragility fracture in people with diseases that cause fragile bones and fractures such as diabetes, Gaucher Disease, Chronic Kidney Disease, Paget Disease.

43. A method as claimed in claim 39, wherein bone fragility presents atypical fractures such atypical femoral fractures.

44. A method as claimed in claim 39, wherein bone fragility presents as stress fractures.

45. Method as claimed as in claim 38, wherein the abnormalities are diseases affecting cartilage or joint border such as arthritis, rheumatoid arthritis, osteoarthritis, osteonecrosis, edema.

46. A method as claimed in claim 37, wherein the condition is diseased back (spine) predisposing or causing conditions such as back pain, or osteoarthritis of the spine

47. A method as claimed in claims 37 and 38, wherein the condition is a diseased hip joint predisposing or causing conditions such as hip pain, or osteoarthritis of the hip.

48. A method as claimed in claims 37 and 38, wherein the condition is a diseased knee joint predisposing or causing conditions such as osteoarthritis of the knee, or knee pain.

49. A method as claimed in claims 37 and 38, wherein the condition is a diseased ankle knee joint predisposing or causing conditions such as osteoarthritis of the ankle, or ankle pain.

50. A method as claimed in claims 1 to 36, comprising using one or more of the analysis results of the alignment or disorganization measurement as an aid in therapeutic decisions.

51. A method as claimed in claim 50, wherein the therapeutic decision comprises treating patients with antiresorptive treatments such as Denosumab, or bisphosphonates such as Alendronate, Risedronate, and Zoledronic Acid.

52. A method as claimed in claim 50, wherein the therapeutic decision comprises treating patients with anabolic treatments such as teriparatide, romosumab, or abaloparatide.

53. A method as claimed in claim 50, wherein the therapeutic decision comprises treating, designing, or implementing physiotherapy, exercise or other physical therapies regimens.

54. A method as claimed in claims 50, wherein the treatment is a surgical procedure such arthroplasty or fusion of joints such as a spinal, hip, knee, or foot joints.

55. A method as claimed in claim 50, wherein the treatment are drugs or interventions used to treat joints or cartilage diseases or conditions.

56. Method as claimed in claim 55, wherein the treatment are drugs used to bone, joints or cartilage diseases such as:

Biological Disease-modifying Anti-Rheumatic Drugs (DMARDs) such as infliximab, etanercept, rituximab, or abatacept;

Conventional DMARDs such as methotrexate, leflunomide/teriflunomaide, sulfasalazine, hydroxychloroquine;

Bone antiresorptive agents such as Denosumab, or bisphosphonates such as Alendronate, Risedronate, and Zoledronic Acid.

Osteoanabolic agents such as teriparatide, Romosumab, or abaloparatide.

57. Method as claimed in claim 55, wherein the treatment are drugs or interventions such as:

Growth factors, Bone morphogenetic proteins (BMPs),

Prostaglandin agonists, biophysical enhancement such as electromagnetic fields, or any osteogenic material.

58. A method as claimed in claims 1 to 35, comprising using one or more of the analysis results of the alignment measurement to assess or monitor bone regeneration, bone repair, fracture healing, and treatments or interventions

59. A method as claimed in claims 1 to 35, comprising using one or more of the analysis results of the alignment measurement to assess the effects of treatments or interventions on bone regeneration, bone repair, or fracture healing.

60. A method as claimed in claims 1 to 35, comprising using one or more of the analysis results of the alignment measurement to identify a delayed fracture union, a non-union, or malunion

61. A method and apparatus for quantifying (automatically if desired) the alignment between two or more objects (or structures, or materials), method and apparatus comprising:

Analyzing an image of two or more objects (or materials or structures);

Creating the alignment data of the objects in the images;

Analysing the alignment data including quantifying the alignment between at least one first object (an object of interest) in relation to a second object (a target object)

Identifying and quantifying abnormal alignment (misalignment) of an object of interest (or its constituents) in the relation to a target object.

62. A method as claimed in claim 61 , comprising:

Identifying one or more abnormal alignments (misalignment) between an object of interest and a target object;

Quantifying the extent or magnitude of the identified one or more abnormal alignments (misalignment).

63. A method as claimed in claims 61 & 62, wherein quantifying the alignment, identifying, and quantifying abnormal alignment(s) between an object of interest and a target object comprises comparing the alignment of the said object of interest to a referent alignment data.

64. A method as claimed in claim 63, wherein the comparator or referent is the alignment data of a phantom custom-tailored to the said object of interest.

65. The method as claimed in claim 64, the method comprising analyzing an image of two or more objects (or materials or structures) using an image processing device such as a software, or a set of executable instructions, or any other device to generate (create) alignment data.

66. A method as claimed in claim 61 to 65, wherein acquiring images comprises using imaging devices such a Dual-X ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI), a camera, or any other imaging device.

67. A method as claimed in claims 61 to 66, comprising using one or more analysis results of the alignment measurement to assist in the prevention, diagnosis, and treatments of diseases and conditions.

68. A method and apparatus for quantifying (automatically if desired) the misalignment of the femoral (or part of it) in relation to the acetabular bone (or part of it), the method and apparatus comprising:

Analyzing an image containing the femoral bone (or part of it), and the acetubular bone (or part of it)

Creating the alignment data of the femoral bone in relation to the acetubular bone

Analysing the alignment data, analysing including quantifying the alignment of the femoral bone in the acetabular bone

Identifying and quantifying abnormal alignment (misalignment) of the femoral bone (or part of it).

69. An apparatus for measuring the alignment (or misalignment, or disorganization) between two or more materials (or objects, or structures), comprising: an identifier of alignment data; an analyser of alignment data; a Quantifier of alignment; an identifier of the presence of alignment abnormalities (misalignments); a Quantifier of extent of alignment abnormalities (misalignments); a Displayer of alignment Results; a memory; and an outputter for outputting at least one result.

70. An apparatus configured to implement the method of any one of the preceding claims.

71. Executable instructions or software that, when executed by a computing device or processor of a computing device, triggers the computing device or processor to perform the method of any one of preceding claims.

72. A device or apparatus with an internet connection configured to perform the method of any one of the preceding claims remotely.

73. A computer-readable medium such as a set of instructions or software embedded in a device or apparatus to automatically (if desired) performs the method of any one of the preceding claims for the diagnosis of diseases and/or monitoring of treatments

74. A device or apparatus configured to perform the method of any one of the preceding claims, comprising using the method to assist in the measurement of image such as other disciplines of medicine, mining, constructions, artificial intelligence, feature extraction, machine learning or pattern recognition.