Classifications

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  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
  • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
  • A61B6/503—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of the heart
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
  • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
  • A61B6/507—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for determination of haemodynamic parameters, e.g. perfusion CT
• • G—PHYSICS
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  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
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  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10072—Tomographic images
  • G06T2207/10081—Computed x-ray tomography [CT]
• • G—PHYSICS
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  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20021—Dividing image into blocks, subimages or windows
• • G—PHYSICS
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  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30004—Biomedical image processing
  • G06T2207/30048—Heart; Cardiac
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30004—Biomedical image processing
  • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
  • G06T2207/30104—Vascular flow; Blood flow; Perfusion

Definitions

• the present disclosure generally relates to methods and systems for visualization, in particular, for visualization of myocardial perfusion measure and coronary artery.
• Computed Tomography and, in particular, Coronary Computed Tomography Angiography (CCTA) have been used to assess coronary arteries in terms of their morphology and geometry and predict the associated future risk for Major Adverse Cardiovascular Events (MACE).
• Spectral CT acquisitions have potential to allow to determine local myocardial perfusion by directly measuring an iodine content in the myocardial tissue, which yields a functional assessment of heart muscle.
• MACE Adverse Cardiovascular Events
• the description provided in the background section should not be assumed to be prior art merely because it is mentioned in or associated with the background section.
• the background section may include information that describes one or more aspects of the subject technology.
• One embodiment of the present disclosure may provide a method for visualization.
• the method may include: obtaining data of a first perfusion measure of myocardial tissues of a patient; obtaining data of a geometry of a coronary artery of the patient; obtaining data of a second perfusion measure of the coronary artery; obtaining data of a flow impediment measure along the coronary artery based on the data of the second perfusion measure of the coronary artery; and visualizing, on a single image, the first perfusion measure of the myocardial tissues and the coronary artery, the coronary artery being overlaid with the first perfusion measure on the single image, the visualized coronary artery representing the geometry of the coronary artery and the flow impediment measure along the coronary artery.
• the visualization system may include: a memory that stores a plurality of instructions; and processor circuitry that couples to the memory.
• the processor circuitry may be configured to execute the instructions to: obtain data of a first perfusion measure of myocardial tissues of a patient; obtain data of a geometry of a coronary artery of the patient; obtain data of a second perfusion measure of the coronary artery; obtain data of a flow impediment measure along the coronary artery based on the data of the second perfusion measure of the coronary artery; and visualize, on a single image, the first perfusion measure of the myocardial tissues and the coronary artery, the coronary artery being overlaid with the first perfusion measure on the single image, the visualized coronary artery representing the geometry of the coronary artery and the flow impediment measure along the coronary artery.
• Another embodiment of the present disclosure may provide a non- transitory computer-readable medium having one or more executable instructions stored thereon, which, when executed by processor circuitry, cause the processor circuitry to perform a method for visualization.
• the method may include: obtaining data of a first perfusion measure of myocardial tissues of a patient; obtaining data of a geometry of a coronary artery of the patient; obtaining data of a second perfusion measure of the coronary artery; obtaining data of a flow impediment measure along the coronary artery based on the data of the second perfusion measure of the coronary artery; and visualizing, on a single image, the first perfusion measure of the myocardial tissues and the coronary artery, the coronary artery being overlaid with the first perfusion measure on the single image, the visualized coronary artery representing the geometry of the coronary artery and the flow impediment measure along the coronary artery.
• FIG. 1 illustrates an example of a three dimensional surface rendering of four chambers of a human heart according to one embodiment of the present disclosure.
• FIG. 2A illustrates a schematic diagram of a system according to one embodiment of the present disclosure.
• FIG. 2B illustrates a schematic diagram of a system according to one embodiment of the present disclosure.
• Fig. 4A illustrates a left ventricular bull's eye plot sector according to one embodiment of the present disclosure.
• Fig. 4B illustrates myocardial slices and sectors corresponding to Fig. 4A.
• Fig. 6 illustrates a visualized perfusion measure and visualized coronary arteries using a bull’s eye plot according to one embodiment of the present disclosure.
• FIG. 7 illustrates a flowchart of a method according to one embodiment of the present disclosure.
• the present disclosure may visualize, on a single image, a perfusion measure of myocardial tissues and a coronary artery, where the coronary artery is overlaid with the perfusion measure on the single image, and the visualized coronary artery may represent a geometry of the coronary artery and a flow impediment measure along the coronary artery.
• the perfusion measure of the myocardial tissues allows for quantifying local blood and oxygen supply, as well as perfusion defects of the myocardium.
• the geometry of the coronary artery may relate the observed perfusion and the blood supply as well, and may be used to compute a common coordinate system.
• the flow impediment measure along the coronary artery may then be used to quantify a local effect of the coronary geometry on an actual blood flow.
• these three items may be computed from a single spectral CCTA scan and mapped into a variation on a common bull’s eye visualization, allowing a user to judge the relative contribution of each of the complementary aspects in a unified graphical figure.
• Fig. 1 illustrates a three dimensional (3D) surface rendering of four chambers of a human heart 810, including a myocardium wall 820 (e.g., a left ventricle in the illustrated example) and a portion of coronary arteries 830 (i.e., blood vessels surrounding the heart 810).
• the muscle tissues that form the myocardium wall 820 are called myocardium, or myocardial tissues 823.
• the heart muscle tissues needs oxygen to operate. Oxygen may be supplied by the coronary arteries 830.
• the volumetric image data may include a contrast-enhanced volumetric image of a cardiac region in the patient’s body and a baseline volumetric image of the cardiac region, where the contrast-enhanced volumetric image may convey anatomical information regarding coronary artery anatomy of the patient.
• an iodine contrast agent may be used.
• the processing device 100B may include processor circuitry 111 and a memory 113.
• the memory 113 may store a plurality of instructions.
• the processor circuitry 111 may couple to the memory 113 and may be configured to execute the instructions.
• the processing device 100B may receive the image data 311 of the patient from the imaging device 100A.
• the block 131 may segment the image data 311 of the patient in accordance with a segment model of the myocardium wall 820 to provide segmented data 321 of the myocardium wall 820.
• the segmentation may be conducted manually by the user or by (semi)automatic segmentation.
• the myocardium wall 820 may be automatically segmented using a convolutional neural network (CNN) trained on manually annotated data.
• CNN convolutional neural network
• the block 133 may obtain data 323 of a first perfusion measure 711 of the myocardial tissues 823 of the patient.
• the obtaining of the data 323 of the first perfusion measure 711 of the myocardial tissues 823 may be based on the segmented data 321 of the myocardium wall 820.
• the data 323 of the first perfusion measure 711 may be contained, for example, in a three dimensional volumetric image.
• the block 141 may segment the image data 311 of the patient in accordance with a segment model of the coronary arteries 830 to provide segmented data 331 of the coronary arteries 830.
• the block 141 may be adapted to automatically extract vessel centerlines of the coronary arteries 830 and lumina within an image.
• Various algorithms for automatic or semiautomatic tracing of centerlines may be used. Any method for tracking the centerlines of the coronary vessels can be used. A method for extracting centerlines for coronary arteries is described, for example, in D.
• the block 143 may obtain data 333 of a geometry 713 of the coronary arteries 830 based on the segmented data 331 of the coronary arteries 830.
• the geometry 333 of the coronary arteries 830 may be represented in terms of the centerlines of the coronary arteries 830 and the lumina of the coronary arteries 830.
• An image used to obtain the data 333 of the geometry 713 of the coronary arteries 830 may be the same as, or may be different from, an image used to obtain the data 323 of the first perfusion measure 711 of the myocardial tissues 823.
• the data 333 of the geometry 713 of the coronary arteries 830 may be obtained by using a different angiographic modality to extract coronary arteries or complement and refine the coronary arteries.
• the block 145 may obtain data 341 of a second perfusion measure 715 of the coronary arteries 830.
• the obtaining of the data 341 of the second perfusion measure 715 of the coronary arteries 830 may be based on the segmented data 331 of the coronary arteries 830.
• the second perfusion measure 715 of the coronary arteries 830 may correspond to a blood flow of the coronary arteries 830.
• a blood flow of the coronary arteries 830 may be a functional quantity providing information about the perfusion downstream.
• the block 147 may obtain data 343 of the flow impediment measure 717 along the coronary arteries 830 based on the data 341 of the second perfusion measure 715 of the coronary arteries 830.
• the obtaining of the data 323 of the first perfusion measure 711, the obtaining of the data 333 of the geometry 713 of the coronary arteries 830, and the obtaining of the data 343 of the flow impediment measure 717 along the coronary arteries 830 may be based on the same coronary computed tomography angiography data.
• the block 147 may determine a flow deviation 345 of the second perfusion measure 715 from a reference value 347 along the coronary arteries 830. Values represented by solid lines in Fig. 3A may correspond to the second perfusion measure 715 of the coronary arteries 830.
• the flow deviation 345 may be determined by robustly fitting a linear tapering model in the effective radius domain along the centerline. The linear model cross- sectional area serves as the reference value 347. Then, the block 147 may process a stenosis assessment 348 (see Fig.
• processing the stenosis assessment 348 along the coronary arteries 830 may include determining a plurality of stenosis rates 349 (e.g., percent stenosis, which may be the relative local loss in cross-sectional area) along the coronary arteries 830 based on the flow deviation 345.
• the stenosis rates 349 may include a measure to quantify the local flow impediment induced by a coronary stenosis.
• peaks may indicate strong narrowings of the coronary arteries 830.
• the block 147 may process a cumulative sum of the stenosis rates 349 to obtain the data 343 of the flow impediment measure 717 of the coronary arteries 830.
• data of a flow impediment measure may be obtained by using other measures such as cumulative loss in radius/diameter, pressure drop (fractional flow reserve (FFR)), volumetric blood flow, blood flow velocity, etc.
• FFR fractional flow reserve
• a more advanced vessel tapering model can be used. Additional information, such as population statistics, prior images of the same patient, etc. may be used to obtain data of a flow impediment measure.
• the block 151 may reformat the data 323 of the first perfusion measure 711 of the myocardial tissues 823, the data 333 of the geometry 713 of the coronary arteries 830, and the data 343 the of the flow impediment measure 717 along the coronary arteries 830.
• the reformatting may be made for visualization on a volumetric bull's eye plot (VBEP).
• VBEP volumetric bull's eye plot
• the block 151 may reformat the data 323 of the first perfusion measure 711 of the myocardial tissues 823, the data 333 of the geometry 713 of the coronary arteries 830, and the data 343 the of the flow impediment measure 717 along the coronary arteries 830, to fit the reference shape 20 (see Fig. 5). Then, the block 151 may obtain reformatted data 325 of the myocardial tissues 823, reformatted data 335 of the geometry 713 of the coronary arteries 830, and reformatted data 345 of the flow impediment measure 717 along the coronary arteries 830. [0042] As shown in Fig.
• the reference shape 20 is in the form of a truncated ellipsoid.
• the true anatomical location of the data may be fitted to match the reference shape 20.
• the segmentation may result in the positioning of the inner and outer reference surfaces 22 and 23.
• the reference shape 20 includes the long axis 21 of the left ventricle, the inner reference surface 22 (e.g., the inner wall or endocardium) and the outer reference surface 23 (e.g., the outer wall or epicardium).
• the inner reference surface 22 e.g., the inner wall or endocardium
• the outer reference surface 23 e.g., the outer wall or epicardium
• the block 153 may then map, to the target shape 201, the reformatted data 325 of the myocardial tissues 823, the reformatted data 335 of the geometry 713 of the coronary arteries 830, and the reformatted data 345 of the flow impediment measure 717 along the coronary arteries 830. Then, the block 153 may obtain mapped data 327 of the myocardial tissues 823, mapped data 337 of the geometry 713 of the coronary arteries 830, and mapped data 347 of the flow impediment measure 717 along the coronary arteries 830.
• each contour 24 of the reference shape 20 may constitute a section of the reference shape 20, and the contours 24 are projected to a single concentric ring of a two dimensional (2D) plane 26, in the form of a 2D plane of a cross-section of the target shape 201.
• 2D two dimensional
• the left ventricle may be mapped onto the target shape 201 in the form of a cylinder and where the dimension along the cylinder axis represents the thickness of the myocardial wall.
• the inner reference surface 22 e.g., the inner wall or endocardium
• the and the outer reference surface 23 e.g., the outer wall or epicardium
• the tissue between the endocardium and the epicardium is projected onto the planes extending from the bottom to the top of the cylinder.
• the complementary imaging features i.e., the perfusion measure 711 and the coronary arteries 830
• the intensity in the plane may be an aggregated value of the first perfusion measure 711 throughout the myocardial wall 820.
• the aggregation can be done by averaging, for example.
• the width of the coronary arteries 830 may correspond to the effective local cross-sectional area such as to allow for visual stenosis assessment.
• the color of the coronary arteries 830 may encode the flow impediment measure 717.
• the single image 700 may show the first perfusion measure 711 of the myocardial tissues 823, the geometry 713 of the coronary arteries 830, and the flow impediment measure 717 of the coronary arteries 830, the relative contribution of each of the complementary aspects may be judged by the user in a unified graphical figure.
• the image data 311 of the patient may be segmented in accordance with the segment model of the coronary arteries 830 to provide the segmented data 331 of the coronary arteries 830.
• the data 333 of the geometry 713 of the coronary arteries 830 of the patient may be obtained based on the segmented data 331 of the coronary arteries 830.
• the data 341 of the second perfusion measure 715 of the coronary arteries 830 may be obtained based on the segmented data 331 of the coronary arteries 830.
• the data 343 of the flow impediment measure 717 along the coronary arteries 830 may be obtained based on the data 341 of the second perfusion measure 715 of the coronary arteries 830.
• the data 323 of the first perfusion measure 711 of the myocardial tissues 823, the data 333 of the geometry 713 of the coronary arteries 830, and the data the of the flow impediment measure 717 along the coronary arteries 830 may be reformatted to fit the reference shape 20.
• the reformatted data 325 of the myocardial tissues 823, the reformatted data 335 of the geometry 713 of the coronary arteries 830, and the reformatted data 345 of the flow impediment measure 717 along the coronary arteries 830 may be mapped to the target shape 201.
• the first perfusion measure 711 of the myocardial tissues 823 and the coronary arteries 830 may be visualized on the single image 700.
• the coronary arteries 830 may be overlaid with the first perfusion measure 71.
• the methods according to the present disclosure may be implemented on a computer as a computer implemented method, or in dedicated hardware, or in a combination of both. Executable code for a method according to the present disclosure may be stored on a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc.
• the computer program product may include non-transitory program code stored on a computer readable medium for performing a method according to the present disclosure when said program product is executed on a computer.
• the computer program may include computer program code adapted to perform all the steps of a method according to the present disclosure when the computer program is run on a computer.
• the computer program may be embodied on a computer readable medium.

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