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WO2010061335A1 - Traitement de données de transfusion myocardiale - Google Patents

Traitement de données de transfusion myocardiale Download PDF

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Publication number
WO2010061335A1
WO2010061335A1 PCT/IB2009/055314 IB2009055314W WO2010061335A1 WO 2010061335 A1 WO2010061335 A1 WO 2010061335A1 IB 2009055314 W IB2009055314 W IB 2009055314W WO 2010061335 A1 WO2010061335 A1 WO 2010061335A1
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WIPO (PCT)
Prior art keywords
myocardium
artery
coronary
processor
specific
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Ceased
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PCT/IB2009/055314
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English (en)
Inventor
Marcel Breeuwer
Javier Olivan Bescos
Maurice A. Termeer
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Koninklijke Philips NV
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Koninklijke Philips Electronics NV
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/56366—Perfusion imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/5601—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/5608—Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels

Definitions

  • the invention relates to a system for processing cardiac data.
  • Three-dimensional (3D) imaging of the heart and the coronary arteries by MRI and CTA is currently entering clinical practice.
  • 3D imaging modalities coronary arteries as well as other cardiac structures such as the left- and right-ventricular myocardium can be dynamically imaged.
  • surface renderings can be made of the heart and the surrounding vessels.
  • Myocardial perfusion can be measured with ECG-triggered Gadolinium- enhanced lst-pass MR perfusion imaging, see e.g. the article "Noninvasive detection of myocardial ischemia from perfusion reserve based on cardiovascular magnetic resonance” by N. Al-Saadi et al., Circulation 101, 2000, pages 1379-1383., followed by quantitative perfusion analysis e.g. with the Philips ViewForum MRcardio software analysis product, see the article "Quantification of atherosclerotic heart disease with cardiac MRI” by M. Breeuwer, Medica Mundi 49(2), 2005, pages 30-38.
  • the measured perfusion is represented in so-called Bulls Eye plots.
  • An example of such a bull's eye plot is shown in Figure 1.
  • the resolution of MR myocardial perfusion imaging in the bull's eye plot is very limited, viz. only three to five slices for the complete ventricle.
  • coronary-artery stenosis i.e. narrowing of the coronary-artery, which usually results in myocardial ischemia, i.e. a reduced contraction due to insufficient supply of oxygen-rich blood, and may result in myocardial infarction, i.e. starvation of myocardial tissue due to lack of oxygen.
  • a system for processing cardiac data comprising a processor that is arranged to obtain patient-specific myocardial perfusion data; model a surface geometry of a patient-specific myocardium division; and integrate the myocardium division model and the myocardial perfusion data into a single model.
  • myocardial perfusion data can easily be interrelated to the surface geometry of the patient's myocardium.
  • clinicians can more easily identify myocardium areas having reduced perfusion.
  • underperfused myocardial areas can thus be identified.
  • the system according to the invention can be used for supporting the diagnosis of coronary-artery disease and/or for monitoring the effect of treatment such as ballooning, stenting and/or by-pass surgery.
  • the single model comprises a 2D and/or a 3D representation, e.g. a bull's eye plot and/or a 3D surface rendering of the myocardium, thereby providing an integrated model that might be visualized in a way that is familiar to clinicians.
  • a 3D representation e.g. a bull's eye plot and/or a 3D surface rendering of the myocardium
  • the processor is arranged for modeling a patient-specific coronary-artery anatomy, wherein the integrating step further comprises integrating the coronary-artery anatomy model in the single model.
  • the myocardial perfusion data interrelated to the surface geometry of the patient's myocardium can be also interrelated to a coronary- artery anatomy causing the blood supply.
  • a coronary-artery structure might be identified causing underperfused myocardial areas.
  • the processor is arranged for assigning a blood supply parameter to a myocardium surface location on the basis of the myocardial perfusion data, the value of the parameter being associated with a corresponding blood supply value, so that blood supply information can be visualized in relation with the myocardium surface.
  • the parameter value is chosen in a color range, e.g. ranging from light gray to dark red, thereby avoiding visual clutter due to too many colors. Obviously, also other color ranges can be applied.
  • the parameter value is normalized with a maximum blood supply value, so that relative blood supply information is provided.
  • absolute parameter values can be provided, e.g. if an accurate algorithm for evaluating blood perfusion data is applied.
  • the processor is arranged for classifying a myocardium surface location as underperfused if the corresponding blood supply value is below a selectable threshold. The resultant underperfused locations can advantageously be visualized in a particular way, e.g. by a striped pattern or in a specific color.
  • the processor is arranged for interlinking myocardium surface locations having corresponding blood supply values that substantially coincide, thereby offering the possibility that myocardium surface locations that have substantially the same blood perfusion are shown in relation with each other, e.g. by displaying an isocontour as a dashed line.
  • the processor is arranged for visualizing the single model, thereby providing integrated information in a suitable way to clinicians.
  • the processor is arranged for dividing the myocardium surface into coronary artery territories, based on a corresponding principal supplying artery. As a result, insight can be obtained into which region is supplied by which coronary artery.
  • different techniques can be applied, e.g. showing territory border lines, e.g. by determining an equiperfusion contour where the supply of a primary and a secondary feeding artery is substantially equal.
  • the processor is arranged for assigning the blood supply parameter to myocardium surface locations in a specific coronary artery territory, thereby providing information as to which particular myocardium surface area a particular coronary artery is supplying blood.
  • a specific coronary artery territory might be depicted using the assigned blood supply parameter values, while no blood supply information relating to other coronary artery territories is shown.
  • the processor is arranged for assigning parameter values to artery structures, based on corresponding relative blood supply to a specified area of interest on the myocardium surface.
  • the myocardium division relates to the left ventricle and/or the right ventricle, thereby providing blood perfusion data related to one or both ventricles.
  • a method of processing cardiac data comprises the steps of obtaining patient-specific myocardial perfusion data, modeling a surface geometry of a patient-specific myocardium division; and integrating the myocardium division model and the myocardial perfusion data into a single model.
  • a computer program product for processing cardiac data comprises instructions for causing a processor to perform the steps of obtaining patient-specific myocardial perfusion data, modeling a surface geometry of a patient-specific myocardium division; and integrating the myocardium division model and the myocardial perfusion data into a single model.
  • the method may be applied to multidimensional image data, e.g., to 3-dimensional (3-D) or 4-dimensional (4-D) images, acquired by various acquisition modalities such as, but not limited to, standard X-ray Imaging, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), and Nuclear Medicine (NM).
  • acquisition modalities such as, but not limited to, standard X-ray Imaging, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), and Nuclear Medicine (NM).
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • US Ultrasound
  • PET Positron Emission Tomography
  • SPECT Single Photon Emission Computed Tomography
  • NM Nuclear Medicine
  • Fig. 1 shows a schematic view of a bull's eye plot
  • Fig. 2 shows a schematic perspective view of a myocardium geometry
  • Fig. 3 shows a first integrated model
  • Fig. 4a shows a second integrated model
  • Fig. 4b shows a third integrated model
  • Fig. 5a shows a fourth integrated model
  • Fig. 5b shows a fifth integrated model
  • Fig. 5c shows a sixth integrated model
  • Fig. 5d shows a seventh integrated model
  • Fig. 6 shows an eighth integrated model
  • Fig. 7a shows a ninth integrated model
  • Fig. 7b shows a tenth integrated model
  • Fig. 8a shows an eleventh integrated model
  • Fig. 8b shows a twelfth integrated model
  • Fig. 9a shows a thirteenth integrated model
  • Fig. 9b shows a fourteenth integrated model
  • Fig. 10 shows a flow chart of an embodiment of the method according to the invention.
  • Fig. 11 shows a schematic view of an embodiment of a system according to the invention.
  • the method of processing cardiac data according to the invention is based on heart imaging data, such as computed tomography angiography (CTA), magnetic resonance imaging (MRI) and/or X-ray, and in future possibly echocardiography (cardiac ultrasound) and/or nuclear medicine imaging (NM). Analysis of the heart measurements may provide numerical values of parameters representative of contraction, perfusion and viability of the myocardium.
  • CTA computed tomography angiography
  • MRI magnetic resonance imaging
  • NM nuclear medicine imaging
  • Analysis of the heart measurements may provide numerical values of parameters representative of contraction, perfusion and viability of the myocardium.
  • the method according to the invention focuses especially on processing data regarding the left ventricle.
  • a surface geometry of a patient-specific myocardium division is modeled.
  • a bull's eye plot is a visualization primitive that is commonly used in cardiac medicine. Its main goal is to provide a two-dimensional overview of the left ventricle 4 and optionally the right ventricle 5.
  • the classical approach to generating a bull's eye plot is to segment the myocardium into a stack of long-axis slices and map these segments to a set of concentric rings.
  • a bull's eye plot can also be constructed as a continuous unfolding of the left ventricle along its long axis 2.
  • Figure 2 shows a schematic perspective view of a myocardium geometry 1.
  • the segmentation of the heart that is used in this context is an unstructured grid of vertices along the epicardium 7. Each of these vertices can be specified by three parameters: an angle ⁇ with the short axis 3 on a plane perpendicular to the long axis 2, the distance h to the apex 8 along the long axis 2 and the distance r to the long axis 2.
  • any point on the epicardium 7 can then be projected, symbolically indicated by the arrows P, onto a cylinder by interpreting ⁇ and h as cylindrical coordinates. Since the surface will be projected on a plane 9, 10, the information provided by r is superfluous. The parameters ⁇ and h are sufficient as polar coordinates to form a circle. The segmentation covers the entire left and right ventricles 4, 5. Since the projection of the top of both ventricles does not provide useful information and only clutters the projection, we do not project vertices that have a value of h exceeding a predefined threshold. This threshold is based on the extent of h of the septum 6, i.e. the myocardial wall shared by the left and right ventricles. As an additional constraint for the right ventricle, any triangles that would cause overlap in the projection are also not included in the bull's eye plot 9, 10.
  • the right ventricle 5 Since the right ventricle 5 is also part of the processing, this is also depicted in the bull's eye plot. While several approaches to visualize the right ventricle in a two- dimensional manner exist, the most common approach is to represent it by a half circle. This approach is based on the idea that the right ventricle 5 is essentially a half left ventricle 4. This approach is common in clinical practice. To realize a half-circle unfolding, firstly the same parameterization to the right ventricle 5 is applied as has been applied to the left ventricle 4. In the bull's eye plot, the right ventricle is translated along the short axis 3 to prevent overlap with the projection of the left ventricle 4. Next, the angle ⁇ is normalized to the range [- Vi ⁇ ; Vi ⁇ ] to form a half circle. This step also eliminates any inter-patient shape variations of the right ventricle.
  • Both the 3D myocardium geometry 1 and the 2D bull's eye plot form a model of the surface geometry of a patient-specific myocardium division.
  • a patient-specific coronary- artery anatomy is modeled.
  • patient-specific myocardial perfusion data are provided. Such data can be generated by various myocardial perfusion measurement and/or modeling algorithms, e.g. by modeling the influence of a vessel diameter on the blood flow by representing the coronary artery tree as a network of resistors. Obviously, more advanced algorithms can be applied for generating the patient-specific myocardial perfusion data.
  • a primary outcome of a computational model of myocardial perfusion data is the blood supply present at each point in the myocardium.
  • FIG. 3 shows a first integrated model 20.
  • myocardial perfusion data are visualized, e.g. by color encoding or by gray scale encoding, both in the 2D representation and in the 3D representation.
  • a blood supply parameter is assigned to a myocardium surface location, wherein the value of the parameter is associated with a corresponding blood supply value.
  • the blood supply parameter value is chosen in a color range, viz. ranging from a light gray to dark red color.
  • isocontours 22 are added as white dashed lines. These contours 22 represent linked myocardium surface locations having corresponding blood supply values that substantially coincide. Thus, the contours 22 delineate borders where the supply of blood is equal. Further, a striped pattern 24 is applied to underperfused regions, i.e., regions where the supply is below a user-specified threshold. Additionally or alternatively, a specific pattern can be applied to well perfused regions, i.e. regions where the supply is above a user-specified threshold.
  • the striped pattern is implemented using h, i.e. the distance to the apex along the long axis part of the parameterization. All parts where the fractional part of ⁇ • h is larger than 1/2 are made opaque, the others transparent. This leads to stripes perpendicular to the long axis in the three-dimensional view, and circles or arcs in the two-dimensional projection.
  • An advantage of this pattern is that it is rotation invariant. The orientation of the pattern is perceived equally throughout the entire mesh and the two-dimensional projection.
  • the coronary arteries 21 are displayed in the three-dimensional view as well as on the bull's eye plot. In the three-dimensional view, the coronary arteries 21 are displayed as a tube with a radius corresponding to the actual artery radius.
  • the shown radius of the coronary arteries is fixed.
  • the positions of the arteries have been modified.
  • the computational modeling uses unmodified positions.
  • the coronary arteries are first projected onto the myocardium 4, 5 and subsequently translated a constant distance along the surface normal. This causes each coronary artery to have a constant distance to the myocardium.
  • a color encoding is used to show the relative outflow at each point in the coronary artery tree 21.
  • the coronary arteries 21 are also projected onto the bull's eye plot, using the same projection technique as described with respect to the myocardium geometry.
  • a point along an artery should be projected on the projection of the left or right ventricle 4, 5 is based on which ventricle is closest. A similar color encoding showing relative outflow on the projected coronary arteries is used.
  • the coronary artery territories are visualized. To this end, the coronary artery 21 is divided into three groups: left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA). This division is common in clinical practice and is also recommended by the AHA.
  • the coronary territories are visualized by drawing black dotted equi-perfusion lines 23.
  • each coronary territory are controlled by a user-specified threshold. If the supply from a coronary artery group at a certain point is above this threshold, that point is considered to be part of the coronary territory belonging to that group.
  • Figures 4a-b show that, using this approach, the regions supplied by each coronary artery group 21a-c as well as regions supplied by multiple arteries can be clearly identified.
  • Figures 5a-d show an example of the separate visualization of coronary artery groups by showing four bull's eye plots.
  • Figure 5a shows a fourth integrated model wherein the supply of all three coronary artery groups 21a-c has been combined, while Figures 5b, 5c and 5d show the supply of the LAD 21a, LCX 21b and RCA 21c, respectively.
  • the result is obtained by assigning the blood supply parameter to myocardium surface locations in a specific coronary artery territory. Due to the separation, more detailed visualization techniques can be applied than when visualizing the coronary territories in a combined fashion, as was described above.
  • a combined visualization of the coronary territories allows assessing the relation between them, while separate visualization allows a more comprehensive analysis of a particular group.
  • a user indicates a specific area of interest by specifying a point on either the three-dimensional mesh or the bull's eye plot and optionally a radius to determine the size of the region. Then, a relative supply from each of the three coronary artery groups 21a-c is computed. As a result, one or a plurality of parameter values are assigned to one or a plurality of specific artery structures of the coronary-artery anatomy, based on a corresponding relative blood supply from the specific artery structure to the specified area of interest on the myocardium surface. To visualize the parameter values, arrows 30a, 30b are drawn from the point of each coronary artery group closest to the specified point. The relative supply by each group is expressed in the width of each arrow 30a, 30b.
  • This approach allows a quick identification of the supplying coronary arteries in a visualization where this may not be directly apparent.
  • An example thereof could be a query as to the coronary arteries supplying an underperfused region 24 in visualizations as described above.
  • CT Current provides a better resolution than MRI. Therefore, it has been decided to use CT scans for the experiment.
  • MRI has advanced sufficiently to allow accurate segmentation of the coronary arteries, MRI is preferable over CT.
  • MRI allows the acquisition of additional data that show the functioning of the heart, which can lead to a more comprehensive diagnosis.
  • a stenosis often causes a perfusion defect in the area normally supplied by the coronary artery that contains the stenosis. When modeling a stenosis, this perfusion defect is expected to be observed in the resulting visualizations.
  • Figures 7a and 7b show a comparison of a healthy case, Figure 7a showing a ninth integrated model, and the same case with an artificially induced stenosis blocking part of the upper LAD by approximately 70%, Figure 7b showing a tenth integrated model.
  • Figure 7b shows an increased underperfused region near the lower segment of the LAD, indicated by an arrow 31.
  • the shape of the isocontours also expressed the change in supply in the affected area.
  • a stenosis is also expected to change the shape of the territory of the respective coronary artery.
  • Figures 8a and 8b showing an eleventh integrated model and a twelfth integrated model, respectively, compare the coronary artery territories of a normal case and the same case with an artificially induced stenosis in the RCA, respectively.
  • Figure 8b the severe reduction in size of the RCA territory can clearly be observed, especially the posterior and septal parts of the left ventricle are affected.
  • the territory of the LAD has slightly increased. A stenosis in an artery can thus have a global effect on the distribution of blood.
  • FIGS 9a and 9b showing a thirteenth integrated model and a fourteenth integrated model, respectively, compare the same region of interest in a healthy case and in a case with an artificially induced stenosis in the LAD. While the region is primarily supplied by the LAD in the healthy case, it is primarily supplied by the LCX and at the border of an underperfused region in the stenosed case.
  • the computational modeling presented herein is simple, but sufficient to demonstrate the feasibility of visualization of the results.
  • the 17- segment model from the AHA is used as a reference for the relation between the coronary arteries and the myocardium.
  • This model is not based on patient-specific information. It has been extended by including patient-specific information and by applying a computational model to obtain a more detailed relation between the coronary arteries and the myocardium. While the model is rather simple, it gives more detailed information than is currently available in medical practice.
  • the computational blood perfusion data model described above computes the way blood perfuses throughout the myocardium. In current clinical practice, this is measured by nuclear medicine and ultrasound. Perfusion scans can also be made with MRI, which provide similar information. The computational approach can be comprehensively combined with measurements from these imaging techniques to evaluate the correlation to these techniques. If the computational approach turns out to have good prognostic value, the difficult, time-consuming and expensive perfusion scans may be avoided.
  • the described computational approach may require an accurate segmentation of the coronary artery tree with accurate artery diameter measurements. For this reason, CT data has been used to demonstrate the techniques, as current MRI technology seems to be inadequate to image coronary arteries with the required accuracy.
  • Figure 10 schematically shows a flowchart of an exemplary embodiment of a method according to the invention.
  • the method comprises the steps of obtaining (100) patient-specific myocardial perfusion data, to model (110) a surface geometry of a patient- specific myocardium division, to model (120) a patient-specific coronary-artery anatomy and to integrate (130) the myocardium division model, the coronary-artery anatomy and the myocardial perfusion data into a single model.
  • the method does not comprise the step of modeling the patient- specific coronary-artery anatomy and integrating it in the single model.
  • Figure 11 schematically shows a computer system 200 for processing cardiac data.
  • the system 200 comprises a processor 210 that is arranged to obtain patient- specific myocardial perfusion data, to model a surface geometry of a patient-specific myocardium division, and to integrate the myocardium division model and the myocardial perfusion data into a single model.
  • the method of processing cardiac data can be performed using dedicated hardware structures, such as FPGA and/or ASIC components.
  • the method can also at least partially be performed using a computer program product comprising instructions for causing the processor 112 to perform the above described steps of the method according to the invention.

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Abstract

L'invention concerne un système de traitement de données cardiaques. Le système comprend un processeur agencé de manière à obtenir des données de transfusion myocardiale spécifiques à un patient. Le processeur est également agencé pour modéliser la géométrie de surface de la division du myocarde particulier au patient. En outre, le processeur est agencé pour intégrer le modèle de division du myocarde et les données de transfusion myocardiale en un modèle unique.
PCT/IB2009/055314 2008-11-28 2009-11-24 Traitement de données de transfusion myocardiale Ceased WO2010061335A1 (fr)

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US11107587B2 (en) 2008-07-21 2021-08-31 The Board Of Trustees Of The Leland Stanford Junior University Method for tuning patient-specific cardiovascular simulations
US11147516B2 (en) 2018-06-18 2021-10-19 Analytics For Life Inc. Methods and systems to quantify and remove asynchronous noise in biophysical signals
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