WO2025236030A1

WO2025236030A1 - Method for determining likelihood of presence of lung disease

Info

Publication number: WO2025236030A1
Application number: PCT/AU2024/050507
Authority: WO
Inventors: Andreas Fouras
Original assignee: 4DMedical Ltd
Current assignee: 4DMedical Ltd
Priority date: 2024-05-17
Filing date: 2024-05-17
Publication date: 2025-11-20
Anticipated expiration: 2026-11-17

Abstract

A method for aiding the detection or treatment of lung disease using regional ventilation measurements of a lung, comprising: acquiring regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle; a) Selecting a region of the lung for analysis; b) selecting a time period within the breathing cycle; c) calculating both the volume and change of volume in the selected region of the lung over the selected time period from the acquired regional ventilation measurement; d) using the calculated change of volume to determine a flow of air for the selected region of the lung during the selected time period; e) repeating steps b to d for multiple time periods within the breathing cycle; and f) combining the calculated volume and the determined flow from the multiple time periods to determine a flow-volume function between the flow of air and the volume of air for the region of the lung; and using the flow-volume function to aid in diagnosis or treatment of lung disease.

Description

METHOD FOR DETERMINING LIKELIHOOD OF PRESENCE OF LUNG DISEASE

Field of Invention

[0001] The present invention relates to a method for detecting lung disease, in particular to a method for detecting lung disease using regional ventilation measurements of the lung.

Background

[0002] Lung function can be assessed using various tests which capture measurements indicating various functions of the lung. One such lung function test measures airflow in and out of the lung and lung volume. Volume measurements are often taken using a spirometer. In the lung function test volume assessment is performed by requiring a patient to inhale deeply, seal their mouth around a mouthpiece and then inhale/exhale while the measurements are acquired. Measurement equipment measures the flow rate (i.e. the volume of air per second exhaled from the lung). The volume exhaled is also measured. In spirometry, the forced expiratory maneuver is the most common and widely utilized test, essential for measuring key parameters such as Forced Vital Capacity (FVC) and Forced Expiratory Volume in 1 second (FEV1). This maneuver involves the patient taking a deep, maximal breath in, typically achieved through passive inspiration, followed by a rapid and forceful exhalation until no more air can be expelled. This process helps diagnose and monitor obstructive and restrictive pulmonary diseases by assessing the lung's capacity to move air out. Complementing this, spirometry can also be used to perform a forced inspiratory maneuver, which involves a forceful inhalation after complete exhalation, providing valuable insights into the maximum inspiratory flow rates and lung volume. This maneuver is useful for evaluating conditions affecting the upper airways and inspiratory muscle strength.

[0003] A flow-volume loop for the lung is produced by plotting the airflow against lung volume measurements. Figure 7 shows an example of a flow-volume loop in which airflow is shown as a function of lung volume. Volume is plotted on the x-axis, and flow is plotted on the y-axis. Inhalation is measured as negative flow and so appears below the x-axis. Exhalation is measured as positive flow and appears above the x-axis. Several measurements can be obtained from the data, including peak inspiratory flow rate (B), peak expiratory flow rate (D), lung volume change (C). The shape of the loop has been found to be indicative of the condition of the lung, and is used to diagnose obstructive disorders and restrictive disorders. For spirometry, as there is no direct measurement of volume, an arbitrary starting point is chosen, typically the start of inspiration, onto which the measured volume change is added to provide a relative volume measure, which is used to create the flow-volume curve.

Summary of the Invention

[0004] In a first embodiment the invention provides a method for detecting lung disease using regional ventilation measurements of the lung: acquiring a regional ventilation measurement for at least one of a plurality of regions of a lung over a predefined time period; calculating both the volume and change of volume of air in the region of a lung over a predefined time period from the acquired regional ventilation measurement; using the calculated change of volume of air to determine a flow of air for the region of the lung during the predefined time period, determining a flow-volume relationship between the flow of air and the volume of air for the region of the lung; and determining a likelihood of the presence of lung disease in the region of the lung by comparing the determined flow-volume relationship to a predefined function for that region.

[0005] One advantage is that embodiments of the invention acquire flow volume data for regions of the lungs. These regions are smaller than entire lungs. Known functional diagnostic tests and tools provide measurements for the entire lungs and do not provide measurement of regional ventilation. These known measurements provide a global approach to lung function diagnosis (essentially assuming the lung to be homogeneous) and may mask deficiencies present in regions of the lung. Additionally, there is no information about where a condition might be located within the lungs. Patterns of restriction and obstruction lead to differences in static and dynamic properties that are not captured with conventional measurement of lung function. Embodiments of the invention allow regional measurements to be analysed, this allows the location of conditions to be identified and so treatment can be accurately targeted.

[0006] In a further aspect the invention provides a method for aiding the detection or treatment of lung disease using regional ventilation measurements of a lung, comprising: a) acquiring regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle; b) selecting a zone of the lung for analysis, the zone comprising a plurality of the regions of the lung; c) selecting a time period within the breathing cycle; calculating both the volume and change of volume in the selected zone of the lung over the selected time period from the acquired regional ventilation measurement; d) using the calculated change of volume to determine a flow of air for the selected zone of the lung during the selected time period; repeating steps b to d for multiple time periods within the breathing cycle; and combining the calculated volume and the determined flow from the multiple time periods to determine a flow-volume function between the flow of air and the volume of air for the zone of the lung; and using the flow-volume function for the zone of the lung to aid in diagnosis or treatment of lung disease.

[0007] In a further aspect the invention provides a method for aiding the detection of lung disease using regional ventilation measurements of a lung, comprising: acquiring regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle; a) selecting a zone of the lung for analysis, the zone comprising a plurality of the regions of the lung; b) selecting a time period within the breathing cycle; c) calculating both the volume and change of volume in the selected zone of the lung over the selected time period from the acquired regional ventilation measurement; d) using the calculated change of volume to determine a flow of air for the selected zone of the lung during the selected time period; repeating steps b to d for multiple time periods within the breathing cycle; and combining the calculated volume and the determined flow from the multiple time periods to determine a flowvolume function between the flow of air and the volume of air for the zone of the lung; and retrieving a predefined flow-volume function; and comparing the determined flow-volume function for the zone of the lung to a predefined flow-volume function and determining a likelihood of the presence of lung disease in the region in dependence on the comparison.

[0008] In a further aspect the invention provides a system for aiding the detection or treatment of lung disease using regional ventilation measurements of a lung, comprising: network interface configured to acquire regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle; processor configured to: a) Select a zone of the lung for analysis; b) select a time period within the breathing cycle; c) calculate both the volume and change of volume in the selected zone of the lung over the selected time period from the acquired regional ventilation measurement; d) use the calculated change of volume to determine a flow of air for the selected zone of the lung during the selected time period; repeat steps b to d for multiple time periods within the breathing cycle; and combine the calculated volume and the determined flow from the multiple time periods to determine a flow-volume function between the flow of air and the volume of air for the zone of the lung; and use the flowvolume function to aid in diagnosis or treatment of lung disease.

[0009] In a further aspect the invention provides a method for aiding the treatment of lung disease using regional ventilation measurements of a lung, comprising: acquiring a first set of regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle during a patient treatment, and acquiring a second set of regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle outside of a patient treatment; and, for each of the first and second set of the acquired regional ventilation measurements: a) selecting a zone of the lung for analysis, the zone comprising a plurality of the regions of the lung; B) selecting a time period within the breathing cycle; c) calculating both the volume and change of volume in the selected zone of the lung over the selected time period from the acquired regional ventilation measurement; d) using the calculated change of volume to determine a flow of air for the selected zone of the lung during the selected time period; repeating steps b to d for multiple time periods within the breathing cycle; and for each of the first and second set of the acquired regional ventilation measurements combining the calculated volume and the determined flow from the multiple time periods to determine a flow-volume function between the flow of air and the volume of air for the zone of the lung; and comparing the flow-volume functions for the first and second set of the acquired regional ventilation measurements to assess the effects of the patient treatment.

[0010] A method for aiding the detection or treatment of lung disease comprising: acquiring three or more images of a lung to form a dynamic lung image dataset for at least one of a plurality of regions of a lung, for at least part of a breathing cycle; selecting a time period within the breathing cycle, the time period including at least three images from the dynamic lung image dataset; selecting a region of the lung, the region of the lung being smaller than the volume of the entire lung ; calculating both the volume and the flow in the selected region of the lung over the selected time period, thereby creating at least two flow-volume measurements; combining the flow-volume measurements to determine a flow-volume function between the flow of air and the volume of air for the region of the lung; and using the flow-volume function for the region of the lung to aid in diagnosis or treatment of lung disease.

[0011] In a further aspect the invention provides a method for aiding the detection or treatment of lung disease comprising: acquiring lung data at multiple phase points of a breathing cycle to create a lung motion dataset for at least one of a plurality of regions of a lung; selecting a time period within the breathing cycle, the time period being large enough to allow for the calculation of at least two flow-volume measurements from the lung motion dataset; selecting a region of the lung, the region of the lung being smaller than the volume of the entire lung ; calculating both the volume and the flow in the selected region of the lung in the selected time period to create at least two flow-volume measurements; combining the flow-volume measurements to create a flow-volume function between the flow of air and the volume of air for the region of the lung; and using the flow-volume function for the region of the lung to aid in diagnosis or treatment of lung disease. [0012] In a further aspect the invention provides a system for aiding the detection or treatment of lung disease comprising: network interface for acquiring three or more images of a lung to form a dynamic lung image dataset for at least one of a plurality of regions of a lung, for at least part of a breathing cycle; and processor configured to perform the steps of: selecting a time period within the breathing cycle, the time period including at least three images from the dynamic lung image dataset; selecting a region of the lung, the region of the lung being smaller than the volume of the entire lung ; calculating both the volume and the flow in the selected region of the lung over the selected time period, thereby creating at least two flow-volume measurements; combining the flow-volume measurements to determine a flow-volume function between the flow of air and the volume of air for the region of the lung; and using the flowvolume function for the region of the lung to aid in diagnosis or treatment of lung disease.

[0013] In a further aspect the invention provides a system for aiding the detection or treatment of lung disease comprising: network interface for acquiring lung data at multiple phase points of a breathing cycle to create a lung motion dataset for at least one of a plurality of regions of a lung; processor configured to perform the steps of: selecting a time period within the breathing cycle, the time period being large enough to allow for the calculation of at least two flow-volume measurements from the lung motion dataset; selecting a region of the lung, the region of the lung being smaller than the volume of the entire lung ; calculating both the volume and the flow in the selected region of the lung in the selected time period to create at least two flow-volume measurements; combining the flow-volume measurements to create a flow-volume function between the flow of air and the volume of air for the region of the lung; and using the flowvolume function for the region of the lung to aid in diagnosis or treatment of lung disease.

Brief Description of the Figures

[0014] In order that the invention be more clearly understood and put into practical effect, reference will now be made to preferred embodiments of an assembly in accordance with the present invention. The ensuing description is given by way of non- limitative example only and is with reference to the accompanying drawings, wherein:

[0015] Figure 1 shows a schematic diagram outlining the basic design of a CT system.

[0016] Figure 2 shows an example CT image.

[0017] Figure 3 is a system diagram of a lung ventilation data device.

[0018] Figure 4 shows the steps for pre-processing image data.

[0019] Figure 5 shows the steps used for calculating the flow-volume functions for regions of the lungs using CTXV data. [0020] Figure 6 shows how the flow-volume relationships can be used to aid in the diagnosis or treatment of lung disease.

[0021] Figure 7 is an example of flow-volume loop of a region of the lung during a breathing cycle including an inhalation phase and an exhalation phase.

[0022] Figure 8 shows examples of flow-volume loops.

[0023] Figure 9 shows further examples of flow-volume loops.

[0024] Figure 10 shows further examples of flow-volume loops showing the effects of Bilevel Positive Airway Pressure (BiPAP) treatment on ventilation of the lungs.

[0025] Figure 11 is a schematic block diagram of an apparatus for aiding the detection of lung disease.

[0026] Figure 12 shows examples of breathing cycle.

[0027] Figure 13 shows the steps of a method for aiding the detection or treatment of lung disease.

Detailed Description

[0028] Volume and flow measurements for different regions of the lungs are generated by acquiring regional lung ventilation data.

Acquiring regional ventilation measurements

[0029] Regional lung ventilation data can be obtained using various techniques. Several techniques exist for obtaining regional ventilation measurements, and more specifically for non- invasively obtaining regional ventilation measurements. For example, Computed Tomographic X-ray Velocimetry (CTXV) exists for cinefluorograph sequences (a time series of 2D x-ray images), three-dimensional Particle Image Velocimetry (3DPIV) and Deformable Image Registration (DIR) exist for 4DCT sequences (a time series of 3D images) and paired inspiratory-expiratory CTs (two 3D images, one acquired at end-expiration and one acquired at end-inspiration), and analysis exists for nuclear medicine images (analysing an image of inhaled radioactive contrast agent, such as Xenon gas).

[0030] Regional lung ventilation techniques are unique in providing regional lung function measurements, whereas PFTs only provide a global measure of lung function (i.e. it does not provide regional measurements), and HRCT only provides structural information (i.e. it does not provide functional information).

[0031] CTXV measures the displacement of a region between two images, and then use the known time difference between the images to calculate the velocity of the region. It will be understood that, in certain circumstances, these techniques are often used to only measure the displacement of a region (without further calculating the velocity of the region).

[0032] An embodiment of the invention will now be described using the above technique, CTXV (also referred to as XV LVAS commercially). Patient lung data can be captured using CT imaging (e.g. a paired-CT or a 4DCT, on which 3DPIV or deformable image registration techniques can be used) and fluoroscopy techniques (e.g. a number of cinefluorograph time sequences, on which XV LVAS style techniques can be used). These non-invasive techniques provide in vivo internal detailed images of human organs.

Acquiring regional ventilation measurements using CTXV

[0033] In the embodiment described below the lung ventilation data is measured, using XV Lung Ventilation Analysis Software (XV LVAS, also known as CTXV), across a full respiratory cycle (from the beginning of the exhalation phase through the exhale phase and the inhale phase to the beginning of the next exhalation). The invention requires lung ventilation data at multiple time points in order to create the flow-volume function. As such, the lung ventilation data must be measured for more than one time point, and thus paired inspiratory-expiratory CTs and nuclear medicine images are not suitable. The measurements are taken with XV LVAS during tidal breathing, however it is also possible to use XV LVAS to obtain ventilation data during a forced respiratory maneuver, such as a forced expiration maneuver. Tidal volume breathing has different respiratory mechanics and time constant inequalities compared to forced expiratory maneuvers.

[0034] An alternative to XV LVAS/CTXV for acquiring the lung ventilation data is 4DCT (4- Dimensional Computed Tomography). 4DCT is a medical imaging technique that provides a series of images capturing the motion of a patient's internal organs over time (e.g. over a full breath cycle). Creating a 4DCT involves a number of steps, including image acquisition, data synchronization, and volumetric reconstruction.

[0035] The patient lies on a CT scanner table and breathes normally while the scanner captures a sequence of 2D images or slices. The scanner is synchronized with a respiratory monitoring system to ensure that the correspond phases of the patient's breathing cycle is known for each image. These acquired images are then sorted into different respiratory phases based on the synchronized respiratory signal. Finally, the sorted images are combined to create multiple 3DCTs, one for each phase of the breath, thus creating a 4DCT volume (and thus providing a time-resolved 3D representation of the patient's anatomy and organ motion throughout the breathing cycle).

[0036] Referring now to Figure 1 , a schematic diagram outlining the basic design of a CT system is shown. The CT system includes a circular CT scanner 20 including a rotating X-ray tube assembly and a detector array positioned diametrically opposite the X-ray tube assembly on the other side of the circle. The CT system includes an imaging bed 30 for a patient to lie on during the scanning procedure. Imaging beds may include securing wraps 40 or straps to help hold the patient still during scanning. The imaging bed 30 is typically movable along the axis of the CT scanner. During scanning, a patient lies on the imaging bed 30 and is positioned within the CT scanner 20. When the patient is in position, the X-ray tube assembly and detector array rotate around the patient and capture a number of X-ray images of the patient at different angles. The X-ray images are processed using computer algorithms to produce a three- dimensional reproduction of the inside of the patient. Two-dimensional slices through the three- dimensional reproduction of the inside of the patient can be created.

[0037] Figure 2 shows an example CT image. The image shows a cross-sectional slice through the chest of a patient between the neck and the waist. A CT produces an image of the full 3- dimensional volume, the image of Figure 2 is a single slice through that 3-dimensional volume.

[0038] CT images are presented in black and white with varying shades of greyscale in between. These shades between black and white are referred to as the image intensity, but may also be referred to as shade, colour or brightness. The image intensity of a particular tissue feature in the image is determined by the degree to which it absorbs (attenuates) x-rays. Features of higher density have higher attenuation coefficients. The image intensity of a feature displayed in the image is determined by its mean attenuation. Using the Hounsfield Scale, tissues with a high Hounsfield score have a high attenuation coefficient and appear white on the CT image. Those features with a low Hounsfield score have a low attenuation coefficient and appear dark on the CT image. CT scanners are calibrated so a particular density is always represented at the same image intensity. This allows calculations of density for the body parts within the image and comparison between images.

[0039] In the example image of Figure 2, the bone of the ribs is shown at 210 and appears white in the image due to its high attenuation coefficient. The body is shown at 220 and appears light grey, having a lower attenuation coefficient. The lung 230 appears in various degrees of dark grey having a low density. Airway 240 is the darkest feature of the image having the lowest density due to the high air content. [0040] Referring back to XV Lung Ventilation Analysis Software, XV LVAS (CTXV, also sometimes referred to as simply ‘XV’) is a software-based image processing technology. The software analyses cinefluorograph images to quantify ventilation of pulmonary tissue. This imaging technique is non-invasive. The system measures the tissue motion of the lung, at all locations/regions throughout the lung, and at all phases of the breath. It uses these motion measurements to calculate the four-dimensional (4D) ventilation of lung tissue. Measurement of ventilation during tidal volume breathing may be more sensitive than methods based on full exhalation (e.g., Pulmonary Function Tests (PFTs) and High Resolution CT (HRCT)), as the variability of the measurement during forced exhalation may hide subtle regional differences in typical lung ventilation.

[0041] The core function of XV LVAS is to analyze the motion of the lung tissue in three dimensions as it occurs over the breathing cycle (3D in space, plus time, thus 4D). This is achieved in software using a method similar to the Simultaneous Algebraic Reconstruction Technique (SART), whereby the projection function uses cross-correlations rather than attenuation functions to match the lung motion to the measured data. One of the main outputs of XV LVAS is ventilation results, and more specifically regional lung ventilation measurements, which can be used in the described invention to determine the likelihood of the presence of lung disease.

[0042] Referring now to acquiring images for XV LVAS for determining regional lung motion (and from that regional ventilation), a technician uses a cinefluorograph to obtain a time series (or sequence) of 2D images of the lung.

[0043] The time series of 2D images of the lung may include a single time series of 2D images of the lung captured from one angle (or perspective) relative to the lung during all or a portion of a respiration cycle. A single time series of 2D images of the lung at a particular angle may include a series or sequence of 2D images, where each respective image in the sequence is captured at a respective different time during (or phase of) inspiration or expiration or during an entire breath (both inspiration and expiration). Additional description of the foregoing acquisition of a time series or a sequence of 2D images is included in U.S. Patent No. 10,674,987, titled “Method of Imaging Motion of an Organ.”

[0044] The time series of 2D images of the lung may include a plurality of time series of 2D images of the lung, where each of the plurality of time series of 2D images is captured from a different angle or perspective relative to the lung, and during all or a portion of a respiration cycle. In this case, each of the plurality of time series of 2D images of the lung include a series of 2D images captured at a unique angle and at spaced apart times during inspiration or expiration. In one configuration, each of the plurality of time series of 2D images of the lung are captured from at least three different angles (in order to create a spread of angles). For example, the 2D images of the lung may be acquired from four angles or five angles, but in any case, preferably no more than ten different angles. Each of the plurality of time series of 2D images of the lung can be captured asynchronously within the same breath, simultaneously, or during different breaths, or any combination thereof.

[0045] The time series of 2D images of the lung may be obtained by a measurement acquisition module from an imaging apparatus that relies on X- rays to capture the images. For example, the imaging apparatus may be fluoroscopy device, capable of capturing a time-series of 2D x- ray images. Alternatively, the 2D images may be obtained from other suitable types of 2D medical imaging apparatuses, such as a projection MRI imaging apparatus, a mm-wave imaging apparatus, an infrared imaging apparatus, a four-dimensional CT imaging apparatus, or a positron emission tomography (PET) imaging apparatus. Additional description of the foregoing capturing of a time series or a sequence of 2D images is included in U.S. Patent No. 10,674,987, titled “Method of Imaging Motion of an Organ”, the entirety of which is incorporated herein by reference.

[0046] After acquisition of the 2D images, the measurement acquisition module analyzes the images to calculate a measurement, e.g., ventilation, of the lungs. The motion of a region of a lung can be calculated by the measurement acquisition module using any suitable technique, however in one embodiment it is measured using XV LVAS (sometimes also referred to as Computer Tomographic X-ray Velocimetry (CTXV),a cross-correlation technique, as described in U.S. Patent No. 9,036,887 B2, titled “Particle Image Velocimetry Suitable for X-ray Projection Imaging”, the entirety of which is incorporated herein by reference). XV LVAS uses X-ray images taken from multiple projection angles in order to measure regional three- dimensional motion of the object, in this case the lungs. The motion tracking in XV LVAS is based on a well- known technique called particle image velocimetry (PIV), in which the displacement of a region is calculated by selecting a region in the first image of a time series and statistically correlating the selected region to the second image in the time series. The motion measurements can therefore be 2D or 3D measurements of displacement, velocity, expansion (or ventilation), or any other suitable motion measurement. The flow in the airways can also be calculated from the motion measurements. [0047] Generally, using the cross-correlation technique, as described in U.S. Patent No. 9,036,887, a first measurement or a second measurement for a region of the lung is calculated by reconstructing motion measurements for each of the plurality of regions of the lung from the plurality of time series of 2D images of the lung, and then deriving a volume or expansion measurement from one or more motion measurements associated with that region for each of the plurality of regions of the lung. In one embodiment, the reconstructing of motion measurements includes reconstructing 3D motion measurements without first reconstructing a 3D image.

[0048] In a preferred embodiment, the technique uses X-ray fluoroscopy technology that is commonly used in clinical imaging to capture physical structures in motion. Fluoroscopy images are acquired during one full respiratory cycle (end-exhalation through the beginning of the next exhalation) at 5 angles: direct anterior-posterior (AP), +36 and -36 degrees relative to AP, and +72 and -72 degrees relative to AP. The subject is required to remain in the same position for each of the five fluoroscopy imaging sequences.

[0049] The XV Reconstruction is a motion reconstruction, rather than an intensity (structural) reconstruction, and therefore the reconstructed data consists of velocities rather than intensities. The velocities are defined at fixed node locations through the volume. Velocities for locations between nodes are determined via trilinear interpolation. As the XV Reconstruction is a motion reconstruction, it can be performed without first reconstructing a 3D image.

[0050] In order to reconstruct velocities, the cinefluorograph images are first converted to a series of cross-correlations. These cross-correlations (from cinefluorograph images) can be considered to be a two-dimensional Probability Density Function (PDF) of the velocities. The measured velocity PDFs are then compared to synthetically generated velocity PDFs that are obtained by computing a forward model. The forward model considers the changes to the crosscorrelations that result from the motion at each point interacting with the light source as it traverses the path (ray) from the source to the detector. From these comparisons the field of motion of the lung tissue is established.

Lung Ventilation Analysis:

[0051] The overall system is shown in Figure 3 including data input 310 and the Lung Ventilation Data Device 320, including a Core Device 330 and an Output Generator 340. The data input is the CT and 2D cineflourograph patient images. The input data may be received across a communications network. This may be a wired or wireless network. The core software device 320 can be divided into three main modules, each may be a separate processor, a different part of a common processor, or on a single processor - specifically, a first component for preprocessing the image data 332, a second component for measurement of the motion 334 and a third component for ventilation computation 336. These steps performed by these modules are linked together in a sequential analysis workflow, the output of one component is used as an input to the next component in the workflow.

[0052] Preprocessing the input image data is made up of multiple steps, shown in more detail in Figure 4, such as initial review of the data for suitability at acceptance gateways, and filtering to remove noise and enhance contrast in preparation for the motion measurements.

[0053] Motion Measurement module 434 is supplied with preprocessed data, and uses this data to calculate the motion of pulmonary tissue in three-dimensions (between the two phase points in the breath that it has been provided). For XV LVAS (CTXV), multiple reconstructions (one for each point in time of the breath) are computed in order to reconstruct the four-dimensional motion of the pulmonary tissue. Alternatively, 3DPIV or DIR could be used on a 4DCT to extract the motion of pulmonary tissue in three-dimensions (between the two phase points in the breath that it has been provided).

[0054] Using this lung tissue motion field, the Ventilation is calculated by taking the divergence of the integrated velocities, resulting in a scalar field of specific ventilation at each node. For XV LVAS these values can be compared across different points in time and summed to determine the change in oxygen in the lungs.

[0055] The device 440 then computes metrics and visualizations derived from the ventilation. A report may be produced at 450.

Calculation of Flow and Lung Volume and defining the regions of the lung:

[0056] Ventilation is calculated for different regions of the lung. The regions may be divided arbitrarily. Ventilation can be calculated over small regions, for example on a voxel-by-voxel basis on the image or averaged over larger regions of the lung including multiple voxels. Regions of the lung may be defined based on anatomical structure of the lung (e.g. lobes or sub-lobes). The left lung includes two lobes, namely the upper (superior) lobe and the lower lobe, separated by a fissure. The right lung is divided into three lobes, namely the upper (superior) lobe, middle lobe, and lower lobe, separated by two fissures. The lobes contain sublobes (also referred to as segments). Alternatively, regions may be defined based on individual voxels, groups of voxels, lobes or sub-lobes defined within the images. Groups of voxels may be combined to define a region of the lung, for example a lobe, sub lobe, or other region of the lung. For example, the volume of a lobe may be calculated by adding together the volumes of group of voxels within the lobe. Regions of the lung may be defined geometrically. Other arbitrary techniques for defining the regions of the lungs may be used. Examples of other techniques include geometric-type techniques dividing the images by blocks or curves.

[0057] As described above, the ventilation of the lung is calculated. Measurements of airflow (flow) and volume are calculated at multiple points in time during the breathing cycle.

[0058] On a regional level, flow is calculated as the rate of change of volume. For a given region (which could be as small as a voxel, but which is typically multiple voxels, like a 3x3x3 voxel), between two time points, time point A and time point B, the volume of the region at first time point A is calculated and the volume of the same region, which has deformed (ie expanded or shrunk) due to either inspiration or expiration, is calculated a later time point B. These volume measurements at time A and time B can then be used to calculate the change in volume between time points A and B. As the time between time points A and B is known (from the imaging parameters, namely frame rate), flow can be derived as the rate of change of volume across the time period between time A and time B, and defines the airflow into or out of the region during the time period between A and B.

[0059] Figure 5 shows the steps used for calculating the flow-volume functions for regions of the lungs using CTXV data. At 510 time-based ventilation data for the lung is obtained. Preferably the time-based ventilation data is provided for each region across the duration of a measured breathing cycle. The ventilation data is provided for thousands of regions. In some embodiments, the regional ventilation data is obtained by receiving the regional ventilation data at network interface 910.

[0060] At 520 the lungs are divided into zones. The lung may be divided into zones, with each zone representing a particular volume of lung tissue. Each zone is made up of one or more regions, preferably two or more regions. The zones may be anatomically based volumes that are physiologically meaningful (such as lobes or sub-lobes), or they may be arbitrary geometric volumes. For each zone, the group of voxels making up that zone are identified, (the zone can be defined within the CTXV data by the voxels, with each voxel being allocated to a zone). These zones may include groups of hundreds of regions, which themselves may include thousands of voxels. In a preferred embodiment the lung is divided into five zones, with each zone representing each of the five lobes in the lung, being the Right Upper Lobe (RUL), Right Middle Lobe (RML), Right Lower Lobe (RLL), Left Upper Lobe (LUL) and Left Lower Lobe (LLL). [0061] At 530 the breathing cycle is split into time periods. For each time period the volume and flow for each region is calculated. Each time period is defined by identifying a starting time and a finish time within the captured breathing cycle, which can often be gained from the image metadata, in particular the imaging frame rate for a fluoroscopy device. Preferred embodiments split the breathing cycle into at least four time periods (i.e. two during the inhale and two during the exhale) and acquire volume and flow measurements for each of the time periods. Figure 12a shows an example of a breathing cycle having a first time period t1 extending from the start of the inhale to the midpoint of the inhale, a second time period t2 from the midpoint of the inhale to the end of the inhale, a third time period t3 from the start of the exhale to the midpoint of the exhale and a fourth time period t4 from the midpoint of the exhale to the end of the exhale (see Figure 12a). However, splitting the breathing cycle into shorter time periods provides more incremental volume and flow measurements, which in turn provides additional detail on the flow volume relationship. For example, as shown in Figure 12b the inhale phase and exhale phase may each be split into five, six, seven, eight, nine or ten time periods. Time periods may have equal duration or different durations. For example, time periods may have unequal duration so that each time period has a similar volume change. Preferably the time periods are consecutive time periods within the breathing cycle so the time periods follow one another and cover the full duration of the breathing cycle. The time periods may start before or finish after the breathing cycle, this can be helpful to capture the transition between the inhalation and exhalation.

Preferably the time cycles are aligned with specific points of the breathing cycle, for example the start of the inhale and I or the start of the exhale. But this alignment is not essential.

[0062] At 540, a region is selected for measurement. The system identifies the group of voxels which make up the zone. The system acquires ventilation measurements for each region. Volume measurements are computed at the starting time and end time for each time period, for each zone.

[0063] At 550 a time period within the breathing cycle is selected for measurement.

[0064] At 560 the volume of the region is calculated at the start time and the end time of the time period. The volume of the region at each time is the total sum of the ventilation for all the voxels within the region at that time.

[0065] At 570, the system calculates flow for the zone during the selected time period. The flow is calculated for the zone during the time period by calculating the difference between the volume at the starting time and the volume at the finish time of the time period to obtain a change in volume of the zone during the time period, and then dividing the change in volume by the duration of the time period to calculate the flow associated with the zone during that time period. The flow is associated with the volume of the zone and also to the time period.

[0066] At 580 the system repeats the process to calculate volume measurements and flow measurements for each zone for each time period of the breathing cycle. In some cases volume and flow measurements are taken across more than one breathing cycle.

At 590 the flow and volume values of each zone for each calculated time period during the breathing cycle are combined to determine a flow-volume relationship for each zone during the breathing cycle. Flow-volume relationships can be used to aid in the diagnosis or treatment of lung disease at 595.

[0067] Figure 6 shows how the flow-volume relationships can be used to aid in the diagnosis or treatment of lung disease. As is well known from spirometry, flow-volume relationships can provide an indication of lung health. However, the global nature of spirometry (each measurement represents the overall function of the entire lung, rather than specific regions of the lung) can allow the lungs ability to use one region to compensate for an underperforming region to hide disease. Particular flow-volume functions may represent particular lung conditions. For example, regions of the lung with asthma may have a specific flow-volume function. At 620, a comparison of measured flow-volume relationships for regions of the lung with the predefined flow-volume functions may provide an indication of the presence, or the absence, of particular lung conditions. For example, a healthy lung may be represented by a particular flow-volume relationship. The comparison may be made automatically, for example via a computer system, or the comparison may be made by a clinician. The regional measurements allow the lung health of different regions of the lungs to be assessed independently. This allows the regional assessment of functional abnormalities that is not possible with conventional pulmonary function tests (such as spirometry, which only provides a single, global measure for the entire lung, rather than regional information) or static radiographic assessments (such as CT, which only provides regional data on the structure of the lungs, rather than on the function of the lungs). The techniques described here allow for assessment of regional flow-volume relationships, thereby allowing the detection of abnormalities not evident in global lung measurements.

[0068] The flow-volume relationship for different regions of the lung can provide information about lung health, for example detecting abnormalities. Measured flow-volume relationships may be compared with predefined flow-volume functions to assess the health of each region for the lung. For example, the flow-volume relationship for some regions may match the flowvolume function for a healthy lung. Other regions may exhibit a flow-volume function which differs from a healthy lung. This may indicate poor function of the lung in that region.

[0069] Different diseases or conditions may impact lung function differently and generate different signature flow-volume characteristics. The measured flow-volume function for a patient may be used to diagnose different lung diseases or conditions by comparing a patient’s flowvolume function with predefined flow-volume characteristics associated with different diseases. By obtaining independent flow-volume measurements for each region of the lung, the health of different regions, and the presence of different diseases within those regions, can be assessed independently. This provides great benefits in the diagnosis of lung disease. The location of diseased tissue can be identified due to the regional results, for example region A may be healthy but lung condition x may be detected in region B. The regional assessment also allows conditions to be detected that may otherwise be masked in a global lung measurement, due to the increased volume and lack of regional information.

[0070] Flow-volume relationships may be measured from patient data (e.g. CT and fluorograph images) captured at different times to track changes in lung function I performance at 930. In one example, flow-volume relationships may be measured before and after surgery. In one example use case, described below, pulmonary function tests to obtain flow-volume measurements may be taken on a patient before and after inserting an endobronchial valve (EBV) into a patient’s lung. Regional flow-volume measurements may be taken before surgery to assess the function of different regions of the lungs. After surgery, when the endobronchial valve has been inserted (which is performed to stop airflow downstream of the valve), regional flow-volume measurements may be made to assess the function of the different regions of the lung after surgery. A comparison of the flow-volume measurements before and after surgery can provide an indication of whether the surgery was successful, to assess whether the valve is working or if the valve is leaking. In cases where endobronchial valves are inserted to block off diseased disease in the lung, the regional flow-volume measurements show whether air is entering the closed off region of the lung.

[0071] In some examples, regional flow-volume measurements may be used to assess the effect of medication on different regions of the lung to aid with diagnosis. For example, if a patient is suspected of having asthma and emphysema, typically both conditions would affect the function of the lungs. The lungs of a patient suffering from both asthma and emphysema would be affected by both conditions and so the flow-volume relationship may include features of both conditions. A physician might run pulmonary function tests (to obtain flow-volume measurements) on a patient before and after administering asthma medication, for example using a bronchial dilator (which would be expected to increase the diameter of the bronchi, and allow easier breathing). Before administering asthma medication, the flow-volume function includes a lung function component relating to asthma and a lung function component relating to emphysema. The medication may alleviate the symptoms of asthma and so change the lung function. This change in lung function will change the flow-volume relationship since the flowvolume function taken after administering the medication might not include the effects of asthma. A comparison between the pre- and post- medication flow-volume measurements can be used to identify regions of the lung affected by asthma and also help to identify regions affected by emphysema.

[0072] The regional flow-volume measurements may also be used to tune respiratory treatment for a patient at 640, for example by acquiring the regional flow-volume measurements at different levels of drug delivery (e.g. during a clinical trial). By tracking flow-volume function produced at medication levels, a clinician can select a desired treatment effect, for example a desired volume for a region of the lung, and use that level of medication.

[0073] The system can include signal outputs which output the detection of lung disease or assessment of the treatment of lung disease. The output may be an alarm which is configured to output a signal, for example an audio alert or a visual alert, based on the detection of lung disease. The signal output may be a display screen which displays a visual output based on detection of lung disease or the assessment of the treatment of lung disease. The signal may be a warning written on a report that is provided back to the requesting physician. The signal might involve adding the patient (or the patient’s regional flow-volume measurements) to a list to be reviewed by a doctor, or more specifically by a pulmonary specialist. The output signal provides a communication to indicate the results from the comparison or the assessment of the effects of patient treatment. The output signal may also be provided in situation where it is only suspected, rather than quantifiably determine, that there is a lung health concern. In addition, the regional flow-volume measurements may be used in screening of large populations, in which the alarm can be that further assessment is required as part of the screening (for example in wide-spread lung cancer screening, or in the case of investigating deployment related respiratory disease suffered by veterans). [0074] Figure 13 shows a method for aiding the detection or treatment of lung disease. At 1210 the system acquires lung data at multiple phase points of a breathing cycle to create a lung motion dataset for at least one of a plurality of regions of a lung. At 1220 a time period is selected within the breathing cycle. The time period is large enough to allow for the calculation of at least two flow-volume measurements from the lung motion dataset. At 1230 a region of the lung is selected for analysis. The region of the lung is smaller than the volume of the entire lung. The region may be as small as a voxel.

[0075] At 1240 the volume and the flow are calculated in the selected region of the lung in the selected time period. The system calculates at least two flow-volume measurements within the selected time period. At 1250 the flow-volume measurements are combined to create a flowvolume function between the flow of air and the volume of air for the region of the lung. At 1260 the flow-volume function for the region of the lung is used to aid in diagnosis or treatment of lung disease.

[0076] Phase points of a breathing cycle are points during the breathing cycle. These may be defined in terms of time, for example time since the start of the inhalation phase, or they may be defined in terms of volume, for example point at which the lung has inhaled a particular volume of air, or they may be defined in other ways.

[0077] The lung data at multiple phase points can be data captured from a series of medical images. Three or more images of a lung may be acquired to form a dynamic lung image dataset. The images may be CT images, or other image types. For example, they may be fluoroscopy images to for a 2D time series, or multiple fluoroscopy image sets, taken at different projection angles, suitable for XV LVAS analysis. The lung motion dataset that is created from the lung data at multiple phase points can be a 4D ventilation dataset (i.e. regional ventilation measurements).

[0078] The specific method for the step of calculating the volume and the flow in the selected region will depend on the type of medical images that are used, and so the type of data that is acquired.

[0079] For example, if fluoroscopy images are used, and a technique such as XV LVAS (CTXV) is relied on, then the steps for calculating the volume and volume change will include: reconstructing a 4D motion field of the lung tissue (i.e. a 3D vector field for each pair of time points, with the vector field representing the displacement of the lung tissue between the first time point and the second time point); choosing a region in the first image; determining the size (volume) of the same region in the second image (by using the displacement vector field); calculating a change in volume and using the know time difference between the images to calculate the flow (volume/time).

[0080] For data acquired from 4DCT, the system creates a 4D motion field using 3DPIV or Deformable image registration (DIR); and then, choosing a region in the first image; determining the size (volume) of the same region in the second image (by using the displacement vector field); calculating a change in volume and using the know time difference between the images to calculate the flow (volume/time).

[0081] Alternatively, for data acquired from 4DCT, the system performs lobe segmentation of the first CT (i.e. dividing the lung into its five lobes, performs lobe segmentation of the second CT, and then calculating a change in volume and using the know time difference between the images to calculate the flow (volume/time).

[0082] The flow-volume function may be calculated for multiple regions of the lung. This allows lung function to be compared between different lung regions. This is useful in identifying localized lung conditions. Regional analysis also allows the function of different parts of the lung to be compared during the same time period. This can provide useful information about whether a condition in one part of the lung affects the function of another part of the lung, for example if expansion is reduced in one part of the lung, whether the lung compensates for this reduction in expansion by increasing expansion in another part of the lung.

[0083] Preferably the time period extends across a full respiratory cycle allowing the function to be identified across the full breathing cycle. The flow-volume measurements can be taken at intervals throughout the full breathing cycle allowing regional lung function to be calculated throughout the breathing cycle.

[0084] At least 8 flow-volume measurements may be taken during the time period.

[0085] Flow-loop measurements may be output to a display. One useful way to display the data is graphically in a two-dimensional graph displaying volume vs flow. This graph may be referred to as a flow-volume loop. [0086] Regions of the lung can form part of a larger zone, where a zone is a group of regions. For example, a zone may be a lobe. A zone is smaller than the entire lung. Preferably, the system calculates one flow-volume loop for 5 zones, with each zone being one of the 5 lobes.

[0087] Flow-volume relationships may be presented graphically. This graphical representation may be referred to as a flow-volume loop.

[0088] Figure 7 is an example of flow-volume loop of a region of the lung during a breathing cycle including an inhalation phase and an exhalation phase. Figure 7 plots volume of air in the lung (x-axis) increasing from right to left against lobar flow (volume/second) (y-axis). Inhalation is represented as a negative flow, expiration is represented as a positive flow.

[0089] The starting point of the breathing cycle is at point A. In this example the flow-volume loop is for a single lobe, for example the Left Upper Lobe. At point A the lobe is fully exhaled and the volume of the lobe is at its lowest point. This may be labelled as zero volume (in particular for relative volume measurements, where the lobe size isn’t known), or if the absolute volume of the lobe is known this may be labelled as the volume of the lobe. The inhalation phase is between points A and C. At A, the inhalation phase commences. Between point A and B the patient inhales, increasing the air in the lungs from zero and producing a negative flow. The patient continues to inhale to point C. The flow changes during the inhalation phase reaching a maximum at point B. At point C the patient has completed the inhale portion of the breathing cycle and the volume of air in the lung is at a maximum. At point C the breathing cycle transitions from the inhale to the exhale, represented by a change in polarity of the flow as the patient stops breathing air into the lungs and commences breathing air out of the lungs. The exhalation phase is represented between point C and E.

[0090] A maximum exhalation flow occurs at point D (signified by the peak in the exhalation flow measurement). The patient continues to exhale until point E at which point the lungs are fully exhaled (i.e. the volume of air in the lungs has returned to zero) and the breathing cycle can commence again at point A.

[0091] The following sections present examples of how regional flow-volume relationships may be used to aid in the diagnosis or treatment of lung disease:

Example 1: Comparison of flow-volume function before and after surgery.

[0092] Time based regional ventilation data may be acquired from a patient before and after surgery, for example using the CTXV technique described above. Flow-volume relationships can be calculated for multiple regions of the lung from the data. A comparison of the flowvolume relationships before and after surgery can be used to assess the success and I or the effect of the surgery on lung function. Such comparisons are useful in monitoring an ongoing lung condition or for monitoring the response of the lung to treatment. The regional measurements provide information to aid an assessment of the effects of the surgery on different parts of the lung.

[0093] Endobronchial valve (EBV) placement has been increasingly utilized for lung volume reduction in patients with emphysema and increased residual volumes. These one-way valves are positioned in lungs during surgery to shut off poorly functioning parts of the lung. The oneway EBV allows expiration of air from the shut off area of the lung but prevents re-inflation. This reduces the volume of the lobe including the EBV since part of the lung tissue can no long inflate.

[0094] Although EBV placement has been demonstrated to improve FEV1 , symptoms score and six-minute walk testing, there is a wide variation in response rate. Regional ventilation measurements before and after EBV placement can be used to assess changes in regional ventilation, and serve as a way to determine if the valve is functioning properly (i.e. if it is preventing air flowing downstream).

[0095] In the examples, X-ray Velocimetry Lung Ventilation Analysis Software (XV-LVAS) (4DMedical Limited, Australia) was utilized to derive regional changes in ventilation. Testing was performed before EBV placement surgery and testing was repeated up to eight weeks after valve placement.

[0096] Figure 8a shows the results from a patient with severe COPD. EBV placement surgery was undertaken and the right superior and middle lobes were occluded. Figure 8 shows flowvolume functions before surgery for Right Superior Lobe, Right Middle Lobe, Right Lower Lobe, Left superior Lobe, Left Lower Lobe (i.e. for five zones of the lung, with each zone corresponding to one of the lobes). Figure 8b shows flow-volume functions after surgery for Right Superior Lobe, Right Middle Lobe, Right Lower Lobe, Left superior Lobe, Left Lower Lobe. Figure 8c superimposes the corresponding before and after flow loops for an easier comparison.

[0097] Figure 8c demonstrates a marked post-valve increase in ventilation in the left superior lobe (i.e. an increase in the maximum volume in the left superior lobe). The results also show ventilation redistributed to the contralateral side from valve overall. As can be seen in Figure 8c, the flow loops for each lobe change differently, demonstrating the importance of being able to produce these results at a regional level. While some lobes maintain a similar maximum flow rate, their overall maximum volume changes. Alternatively, some lobes show a change in both maximum volume and maximum flow.

[0098] In the example of Figure 9a, the right inferior lobe was occluded. Figure 9 shows flowvolume functions before surgery for Right Superior Lobe, Right Middle Lobe, Right Lower Lobe, Left superior Lobe, Left Lower Lobe (i.e. for five zones of the lung, with each zone corresponding to one of the lobes). Figure 9b shows flow-volume functions after surgery for Right Superior Lobe, Right Middle Lobe, Right Lower Lobe, Left superior Lobe, Left Lower Lobe. Figure 9c superimposes the corresponding before and after flow loops for an easier comparison. In Figure 9c, the right inferior lobe (also known as the Right Lower Lobe, RLL) showed a significant decrease in ventilation after surgery. The other four lobes (zones) also showed a decrease in ventilation post surgery.

[0099] In the examples of Figures 8 and 9, regional flow-volume functions show redistribution of ventilatory changes after valve placement. Analysis using regional flow-volume measurements found that ventilation after EBV redistributed to the contralateral lung but was inconsistently redistributed by lobar evaluation.

[0100] Regional flow-volume data allows the function of each region of the lung to be assessed individually. In the case of lobes being occluded regional flow-volume data allows a clinician to identify whether surgery has been successful in shutting off a region of the lung. It also provides the clinician information about the effect of the value on other regions of the lung, for example due to redistribution of air within the lung.

Example 2: Effect of BiPAP on Flow-Volume Function:

[0101] Figure 10 shows the effects of Bilevel Positive Airway Pressure (BiPAP) treatment on ventilation of the lungs. BiPAP treatment is a noninvasive ventilation therapy often prescribed to patients with respiratory diseases including COPD. BiPAP applies a positive pressure to a patient’s airways to assist the patient with breathing by helping to keep a patient’s airway open and help to move air in and out of the lungs. Airflow is provided at two levels (bilevel), namely an inhalation positive airway pressure and an exhalation positive airway pressure.

[0102] Figure 10 shows the flow-volume loops for the five lobes of the lung during a breathing cycle. Separate flow-volume loops are measured for quiet (unsupported) tidal breathing and BiPAP supported breathing. For each lobe, the flow-volume loop for quiet tidal breathing and the flow-volume loop for BiPAP supported breathing are displayed together for comparison.

[0103] The flow and volume measurements of Figure 10 are captured using X-ray Velocimetry (XV) data recording entire breath for quiet tidal breathing and BiPAP supported breathing. The XV data is captured during separate breathing cycles, for example, an XV system captures data on the patient during an unassisted breathing cycle, and at a different time the mask of a BiPAP machine is then fitted to the airways of the patient and the XV system is used to capture data during a BiPAP supported breathing cycle. Data may be captured within a single clinic session or may be captured during multiple sessions. The BiPAP supported data may be captured before or after the quiet tidal breathing data.

[0104] Figure 10 includes separate flow-volume results for each separate lobe of the lungs, Right Upper Lobe (RUL), Right Medium Lobe (RML), Right Lower Lobe (RLL), Left Upper Lobe (LUL), Left Upper Lobe (LUL). For each lobe, and for each of the quiet tidal breathing (standard) and BiPAP breathing cycle, volume I flow values are calculated at 17 points during the breathing cycle. The flow volume data for each data point is calculated by the system using the steps described with respect to Figure 5 (by identifying a time period within the breathing cycle, calculating the volume of the lobe at the start and end of the time cycle and calculating a flow associated with the change in volume during the time period). Each flow-volume data point is represented on the plot in Figure 10 (10041 10042 10043 etc). The dashed lines represent ‘best-fit’ curves to the data sets for the quiet tidal breathing and BiPAP supported breathing.

[0105] Referring now to the data from the Right Upper Lobe, the flow-volume loop for quiet tidal breathing is represented by 1002, the flow-volume loop for BiPAP supported breathing is represented by 1004. The quiet tidal breathing data shows a maximum air volume of 0.15 litres. The maximum flow during inhale is around -0.13 L/s and the maximum flow during exhale is around 0.08 L/s. The BiPAP supported breathing data shows a maximum air volume of 0.20 litres. The maximum flow during inhale is around -0.20 L/s and the maximum flow during exhale is around 0.15 L/s. Comparison of the flow-volume data shows that during BiPAP supporting breathing, the patient increases inhalation of air into the Right Upper Lobe from 0.15 L to 0.2 L. The patient also increases the maximum flows during both the inhalation phase and the exhalation phase. The overall shape of flow-volume function is similar during quiet tidal breathing and BiPAP supported breathing.

[0106] The flow-volume curves from the RML, RLL, LUL, LLL show similar trends. In each of the lobes the flow-volume loop for BiPAP supported breathing 1014 1024 1034 1044 shows an increased volume, increased maximum inhalation flow and increased maximum exhalation flow, when compared with the quiet tidal breathing, as would be expected.

[0107] By calculating separate flow volume functions for each lobe, the function of each lobe can be assessed independently. This provides information about the regional performance of the lung that cannot be acquired from global measurements on the entire lung. For example, the regional data allows assessment of whether some regions function to compensate for function of other regions. The flow volume for each region can be assessed independently against known functions or trends associated with healthy or diseased lungs to identify local lung conditions.

[0108] In the case of BiPAP supported breathing, the system allows a clinician to see the effects of varying the BiPAP settings (for example the pressure setting) on the function of the lung and use this data to make an informed decision about the best settings for the patient. In addition, this example acts as a strong demonstration of the ability of the techniques to measure and indicate the change in volume and flow when undergoing BiPAP therapy. Flow volume data may be captured for different settings of the BiPAP therapy and then compared to select the appropriate settings for the patient. For example, a clinician may have a target volume for a particular lobe of the lung. The system can capture flow volume data during a breathing cycle with the BiPAP system set to different pressure settings, for example a low, medium and high- pressure setting. The clinician can review the flow volume curves for the lobes at each setting to observe the function of the lobes at each pressure setting. Based on the data the clinician can recommend a pressure setting for the patient.

[0109] In some use cases, the BiPAP data may be captured during a single appointment, for example a patient might be connected to a BiPAP therapy device, and the clinician can change the settings and capture data on the breathing cycle. The data can be analysed in real time to provide the clinician with feedback during the appointment.

[0110] Further data can be obtained from the results including: respiratory rate, Inspiratory time (Tinsp), Total breath time (Ttot), tidal volume (Tv), and instantaneous inspiratory VI and expiratory VE airflow.

Block Diagram of System:

[0111] Figure 11 is a schematic block diagram of an apparatus 11000 for aiding the detection of lung disease using regional ventilation measurements of the lung. [0112] As described above, images of the patient are captured by imaging apparatus 11010 (such as a cinefluoscopy systems or a CT scanner).

[0113] Apparatus 11000 is configured to perform the methods described to aid in the detection of lung disease using regional ventilation measurements. The components of apparatus 11000 may be co-located or may be distributed and form part of a distributed computing system. In the case of a distributed computing system, components may be connected across communication networks, for example a mobile communication network. The distributed computing system may be referred to as a cloud computing system.

[0114] The apparatus 900 may include one or more network interfaces 920 that may facilitate communication between the apparatus 11000 and one or more other apparatuses using any suitable communications standard. For example, the interface 11020 may enable the receipt of image datasets from imaging apparatus 11010, where the image datasets represent images captured by the imaging apparatus. The interface 920 may also enable the receipt of further information relating to the patient and/or the image datasets. Interface 920 may enable the receipt of time-based regional ventilation data for a lung, for example received from XV Lung Ventilation Analysis Software Device 320. The interface 11020 may be a LAN interface that implements protocols and/or algorithms that comply with various communication standards of the Institute of Electrical and Electronics Engineers (IEEE), such as IEEE 802.11 , while a cellular network interface implement protocols and/or algorithms that comply with various communication standards of the Third Generation Partnership Project (3GPP) and 3GPP2, such as 3G and 4G (Long Term Evolution), and of the Next Generation Mobile Networks (NGMN) Alliance, such as 5G.

[0115] The apparatus 11000 may include one or more processors 11030 configured to access and execute computer-executable instructions stored in at least one memory 11040. The processor 11030 may be implemented as appropriate in hardware, software, firmware, or combinations thereof.

[0116] Processor 11030, implemented in hardware may be a general-purpose processor. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor 11030 may include, without limitation, a central processing unit (CPU), a digital signal processor (DSP), a reduced instruction set computer (RISC) processor, a complex instruction set computer (CISC) processor, a microprocessor, a microcontroller, a field programmable gate array (FPGA), a System-on-a-Chip (SOC), or other programmable logic, discrete gate or transistor logic, discrete hardware components, or any combination thereof, or any other suitable component designed to perform the functions described herein. Processor 930 may also include one or more application- specific integrated circuits (ASICs) or application -specific standard products (ASSPs) for handling specific data processing functions or tasks. Processor 930 may also be implemented as a combination of computing components, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, or any other such configuration.

[0117] Software or firmware implementations of processor 11030 may include computerexecutable or machine-executable instructions written in any suitable programming language to perform the various functions described herein. Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. The software may reside on a computer-readable medium. A computer-readable medium may include, by way of example, a smart card, a flash memory device (e.g., card, stick, key drive), random access memory (RAM), read only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), a general register, or any other suitable non-transitory medium for storing software.

[0118] The memory 11040 may include, but is not limited to, random access memory (RAM), flash RAM, magnetic media storage, optical media storage, and so forth. The memory 11040 may include volatile memory configured to store information when supplied with power and/or non-volatile memory configured to store information even when not supplied with power. The memory 11040 may store various program modules, application programs, and so forth that may include computer-executable instructions that upon execution by the processor 930 may cause various operations to be performed. The memory 11040 may further store a variety of data manipulated and/or generated during execution of computer-executable instructions by the processor 11030.

[0119] Memory 11040 may store various program modules, application programs, and so forth that may include computer-executable instructions that upon execution by the processor 930 may cause various operations to be performed. For example, the memory 11040 may include an operating system module (O/S) that may be configured to manage hardware resources such as network interface 11020 and provide various services to applications executing on the apparatus 11040.

[0120] Memory 11040 includes storage modules. Application storage module 11041 stores applications for aiding in the detection of lung disease. Memory 11040 stores additional program modules which may be called and executed during execution of the application. Additional storage modules may include data storage module 11042 for storing image datasets for comparison.

[0121] Each of the modules includes functions in the form of logic and rules that respectively support and enable the various functions described herein with reference to the Figures. Although illustrated as separate modules in Figure 11 , one or more of the modules may be a part of or a submodule of another module.

[0122] User interface 11050 facilitates user interaction with apparatus 11000. User interface includes a user input module to receive input from the user. The user input module may include a keyboard, touchscreen, touch pad, microphone or other input device. User interface allows a user to select applications for execution, datasets for comparison and other modules for execution by the processor. The system may also include a signal output module 11060. The signal output module 11060 may include a display, microphone, communications connection, or other means for communicating information to a user.

Final Summary:

[0123] Embodiments of the invention allow the health the lung to be assessed using regional flow-volume curves. By using for example, CTXV (also known as XV LVAS or just XV) measurements volume and flow of regions of the lung are calculated. The system provides great flexibility in identifying different regions, for example using the lobes of the lungs, sublobes or even on a voxel by voxel level. Regional flow-volume measurements allow the health of different regions of the lung to be assessed independently in order to identify localized lung malfunction. Such localized lung function may be obscured by global measurements. Regional flow volume results allows analysis of regional heterogeneity on a lobar level, this analysis is not possible using global measurements.

[0124] Embodiments of the invention provide the use of regional flow-volume relationship as a way to identify lung disease. Embodiments provide incremental breakdown of regional performance of the lung and facilitates the location of lung has disease to be detected through regional analysis. The regional data also allows measured regional flow-volume functions to be compared with known functions to identify disease. Regional data can also be used for ongoing monitoring of lung condition in response to medication or surgery and I or as part of regular health checks to detect the onset of lung conditions or a change in behavior of regions of the lung.

[0125] Embodiments open the potential for using CTXV in regional assessment of ventilatory abnormalities that is not possible with conventional pulmonary function tests or static radiographic assessments.

[0126] It is to be understood that, if any prior art publication is referred to herein, such reference does not constitute an admission that the publication forms a part of the common general knowledge in the art, in Australia or any other country.

[0127] In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word "comprise" or variations such as "comprises" or "comprising" is used in an inclusive sense, namely, to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.

[0128] It is to be understood that the aforegoing description refers merely to preferred embodiments of invention, and that variations and modifications will be possible thereto without departing from the spirit and scope of the invention, the ambit of which is to be determined from the following claims.

Claims

Claims:

1. A method for aiding the detection or treatment of lung disease using regional ventilation measurements of a lung, comprising: acquiring regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle; a) selecting a zone of the lung for analysis, the zone comprising a plurality of the regions of the lung; b) selecting a time period within the breathing cycle; c) calculating both the volume and change of volume in the selected zone of the lung over the selected time period from the acquired regional ventilation measurement; d) using the calculated change of volume to determine a flow of air for the selected zone of the lung during the selected time period; e) repeating steps b to d for multiple time periods within the breathing cycle; and f) combining the calculated volume and the determined flow from the multiple time periods to determine a flow-volume function between the flow of air and the volume of air for the zone of the lung; and using the flow-volume function for the zone of the lung to aid in diagnosis or treatment of lung disease.

2. A method according to claim 1 comprising the step of dividing the lung into a plurality of zones, wherein the step of selection of the zone is made from the plurality of zones.

3. A method according to claim 1 wherein the step of using flow-volume function to aid in diagnosis of lung disease is performed by comparing the determined flow-volume function for the zone of the lung to a predefined flow-volume function and determining a likelihood of the presence of lung disease in the region in dependence on the comparison.

4. A method according to claim 3 comprising the step of communicating the likelihood of the presence of lung disease.

5. A method according to claim 1 wherein the step of using the flow-volume function to aid in treatment of lung disease is performed by: acquiring regional ventilation measurements captured during a patient treatment and determining a flow-volume function for the zone of the lung for the patient treatment measurements; acquiring regional ventilation measurements captured outside patient treatment and determining a flow-volume function for the zone of the lung for the outside patient treatment measurements; and, comparing the flow-volume functions to assess the effects of the patient treatment.

6. A method according to claim 5 comprising the step of communicating the assessment of the effects of the patient treatment.

7. A method according to claim 1 wherein the flow-volume function is a flow-volume loop.

8. A method according to claim 1 wherein the regional ventilation measurements are acquired across a full respiratory cycle.

9. A method according to claim 1 wherein the regional ventilation measurements are acquired during an inhalation phase of a respiratory cycle or an exhalation phase of a respiratory cycle.

10. A method according to claim 1 wherein the regional ventilation measurements are acquired during tidal volume breathing.

11. A method according to claim 1 wherein tidal volume breathing is supported breathing or unsupported breathing.

12. A method according to claim 1 wherein the regional ventilation measurements are acquired from multiple CT images and 2D cineflourograph images captured across the respiratory cycle.

13. A method according to claim 1 wherein the regions of a lung for which regional ventilation measurements are acquired are voxels.

14. A method according to claim 1 wherein the selected zone of the lung is a sub-volume of the entire lung.

15. A method according to claim 1 wherein the selected zone of the lung is a lobe of the lung.

16. A method according to claim 1 wherein the selected zone of the lung includes multiple voxels.

17. A method for aiding the detection of lung disease using regional ventilation measurements of a lung, comprising: acquiring regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle; a) selecting a zone of the lung for analysis, the zone comprising a plurality of the regions of the lung; b) selecting a time period within the breathing cycle; c) calculating both the volume and change of volume in the selected zone of the lung over the selected time period from the acquired regional ventilation measurement; d) using the calculated change of volume to determine a flow of air for the selected zone of the lung during the selected time period; e) repeating steps b to d for multiple time periods within the breathing cycle; and f) combining the calculated volume and the determined flow from the multiple time periods to determine a flow-volume function between the flow of air and the volume of air for the zone of the lung; and retrieving a predefined flow-volume function; and comparing the determined flow-volume function for the zone of the lung to a predefined flow-volume function and determining a likelihood of the presence of lung disease in the region in dependence on the comparison.

18. A system for aiding the detection or treatment of lung disease using regional ventilation measurements of a lung, comprising: network interface configured to acquire regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle; processor configured to: a) Select a zone of the lung for analysis; b) select a time period within the breathing cycle; c) calculate both the volume and change of volume in the selected zone of the lung over the selected time period from the acquired regional ventilation measurement; d) use the calculated change of volume to determine a flow of air for the selected zone of the lung during the selected time period; e) repeat steps b to d for multiple time periods within the breathing cycle; and f) combine the calculated volume and the determined flow from the multiple time periods to determine a flow-volume function between the flow of air and the volume of air for the zone of the lung; and use the flow-volume function to aid in diagnosis or treatment of lung disease.

19. A system according to claim 18 comprising the step of dividing the lung into a plurality of zones, wherein the step of selection of the zone is made from the plurality of zones.

20. A system according to claim 18 wherein the step of using flow-volume function to aid in diagnosis of lung disease is performed by comparing the determined flow-volume function for the zone of the lung to a predefined flow-volume function and determining a likelihood of the presence of lung disease in the region in dependence on the comparison.

21 . A system according to claim 20 comprising a signal output for communicating the likelihood of the presence of lung disease.

22. A system according to claim 18 wherein the step of using the flow-volume function to aid in treatment of lung disease is performed by: acquiring regional ventilation measurements captured during a patient treatment and determining a flow-volume function for the zone of the lung for the patient treatment measurements; acquiring regional ventilation measurements captured outside patient treatment and determining a flow-volume function for the zone of the lung for the outside patient treatment measurements; and, comparing the flow-volume functions to assess the effects of the patient treatment.

23. A system according to claim 22 comprising a signal output for communicating the assessment of the effects of the patient treatment.

24. A system according to claim 18 wherein the flow-volume function is a flow-volume loop.

25. A system according to claim 18 wherein the regional ventilation measurements are acquired across a full respiratory cycle.

26. A system according to claim 18 wherein the regional ventilation measurements are acquired during an inhalation phase of a respiratory cycle or an exhalation phase of a respiratory cycle.

27. A system according to claim 18 wherein the regional ventilation measurements are acquired during tidal volume breathing.

28. A system according to claim 18 wherein tidal volume breathing is supported breathing or unsupported breathing.

29. A system according to claim 18 wherein the regional ventilation measurements are acquired from multiple CT images and 2D cineflourograph images captured across the respiratory cycle.

30. A system according to claim 18 wherein the regions of a lung for which regional ventilation measurements are acquired are voxels.

31 . A system according to claim 18 wherein the selected zone of the lung is a sub-volume of the entire lung.

32. A system according to claim 18 wherein the selected zone of the lung is a lobe of the lung.

33. A system according to claim 18 wherein the selected zone of the lung includes multiple voxels.

34. A method for aiding the treatment of lung disease using regional ventilation measurements of a lung, comprising: acquiring a first set of regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle during a patient treatment, and acquiring a second set of regional ventilation measurements for at least one of a plurality of regions of a lung for at least part of a breathing cycle outside of a patient treatment; and, for each of the first and second set of the acquired regional ventilation measurements: a) selecting a zone of the lung for analysis, the zone comprising a plurality of the regions of the lung; b) selecting a time period within the breathing cycle; c) calculating both the volume and change of volume in the selected zone of the lung over the selected time period from the acquired regional ventilation measurement; d) using the calculated change of volume to determine a flow of air for the selected zone of the lung during the selected time period; e) repeating steps b to d for multiple time periods within the breathing cycle; and for each of the first and second set of the acquired regional ventilation measurements combining the calculated volume and the determined flow from the multiple time periods to determine a flow-volume function between the flow of air and the volume of air for the zone of the lung; and comparing the flow-volume functions for the first and second set of the acquired regional ventilation measurements to assess the effects of the patient treatment.

35. A method for aiding the detection or treatment of lung disease comprising: acquiring three or more images of a lung to form a dynamic lung image dataset for at least one of a plurality of regions of a lung, for at least part of a breathing cycle; a) selecting a time period within the breathing cycle, the time period including at least three images from the dynamic lung image dataset; b) selecting a region of the lung, the region of the lung being smaller than the volume of the entire lung; c) calculating both the volume and the flow in the selected region of the lung over the selected time period, thereby creating at least two flow-volume measurements; d) combining the flow-volume measurements to determine a flow-volume function between the flow of air and the volume of air for the region of the lung; and using the flow-volume function for the region of the lung to aid in diagnosis or treatment of lung disease.

36. A method for aiding the detection or treatment of lung disease comprising: acquiring lung data at multiple phase points of a breathing cycle to create a lung motion dataset for at least one of a plurality of regions of a lung; a) selecting a time period within the breathing cycle, the time period being large enough to allow for the calculation of at least two flow-volume measurements from the lung motion dataset; b) selecting a region of the lung, the region of the lung being smaller than the volume of the entire lung; c) calculating both the volume and the flow in the selected region of the lung in the selected time period to create at least two flow-volume measurements; d) combining the flow-volume measurements to create a flow-volume function between the flow of air and the volume of air for the region of the lung; and using the flow-volume function for the region of the lung to aid in diagnosis or treatment of lung disease.

37. A method according to claim 36 wherein the lung data at multiple phase points is a series of medical images.

38. A method according to claim 37 where the medical images are at least one of CT images or fluoroscopy images.

39. A method according to claim 36 comprising repeating steps b to d for multiple regions of the lung.

40. A method according to claim 36 comprising selecting a time period that includes a full respiratory cycle.

41 . A method according to claim 36 wherein at least 8 flow-volume measurements are calculated for each region.

42. A method according to claim 36 comprising using the flow-volume measurements to create a flow-volume loop.

43. A method according to claim 36 wherein multiple regions are grouped into a zone.

44. A method according to claim 36 wherein a zone is a lobe of the lung.

45. A method according to claim 43 wherein the flow-volume function is calculated for multiple zones.

46. A method according to claim 44 comprising calculating one flow-volume loop for each of the 5 lobes of the lung.

47. A method according to claim 36 comprising calculating a volume change, and using the calculated volume change to determine the flow.

48. A system for aiding the detection or treatment of lung disease comprising: network interface for acquiring three or more images of a lung to form a dynamic lung image dataset for at least one of a plurality of regions of a lung, for at least part of a breathing cycle; and processor configured to perform the steps of: a) selecting a time period within the breathing cycle, the time period including at least three images from the dynamic lung image dataset; b) selecting a region of the lung, the region of the lung being smaller than the volume of the entire lung; c) calculating both the volume and the flow in the selected region of the lung over the selected time period, thereby creating at least two flow-volume measurements; d) combining the flow-volume measurements to determine a flow-volume function between the flow of air and the volume of air for the region of the lung; and e) using the flow-volume function for the region of the lung to aid in diagnosis or treatment of lung disease.

49. A system for aiding the detection or treatment of lung disease comprising: network interface for acquiring lung data at multiple phase points of a breathing cycle to create a lung motion dataset for at least one of a plurality of regions of a lung; processor configured to perform the steps of: a) selecting a time period within the breathing cycle, the time period being large enough to allow for the calculation of at least two flow-volume measurements from the lung motion dataset; b) selecting a region of the lung, the region of the lung being smaller than the volume of the entire lung; c) calculating both the volume and the flow in the selected region of the lung in the selected time period to create at least two flow-volume measurements; d) combining the flow-volume measurements to create a flow-volume function between the flow of air and the volume of air for the region of the lung; and e) using the flow-volume function for the region of the lung to aid in diagnosis or treatment of lung disease.

50. A method according to claim 36 wherein the lung motion dataset that is created from the lung data at multiple phase points is a 4D ventilation dataset.