System and method for quantifying coronary artery microvascular disease by processing electronic images

Abstract

A computer implementation method for processing electronic images to quantify coronary microvascular disease may include receiving imaging data of one or more captured electronic images. A first set of imaging data may be captured before the administration of one or more drugs, and a second set of imaging data may be captured thereafter. The method may further include providing the imaging data and sets of patient data to a machine learning model. The machine learning model may be trained to identify coronary microvascular disease (CMD) features in the captured imaging data and sets of patient data and to output one or more CMD measurements and / or predicted CMD end types. The method may further include transmitting one or more CMD measurements and / or predicted CMD end types to a user device.

Claims

1. A computer implementation method for quantifying coronary artery microvascular disease by processing electronic images, wherein the method is: Receiving image data of one or more captured electronic images by one or more processors of an image processing system, wherein a first set of image data is captured before administering one or more drugs to the imaging target, and a second set of image data is captured after administering the one or more drugs to the imaging target, The provision of the image data and patient data set to a machine learning model by one or more processors, the machine learning model being trained to use one or more sets of collected and / or simulated image data and one or more sets of collected and / or simulated patient data to identify coronary microvascular disease (CMD) features in the captured image data and patient data set, and to output one or more CMD measurements and / or predicted CMD end types, The computer implementation method, comprising transmitting the one or more CMD measurement values ​​and / or the predicted CMD end type to a user device using one or more processors.

2. The computer implementation method according to claim 1, further comprising using the captured imaging data to determine the microvascular resistance reserve (MRR) using the one or more processors.

3. The computer implementation method according to claim 2, wherein the set of patient data includes the MRR.

4. The computer implementation method according to claim 1, wherein the identified CMD features include one or more identified differences between the first set of captured image data and the second set of captured image data.

5. The computer implementation method according to claim 4, further comprising determining vasodilatory capacity based on the one or more identified differences using the one or more processors.

6. The computer implementation method according to claim 1, wherein the set of patient data includes one or more biomarkers of the patient.

7. The computer implementation method according to claim 1, wherein the one or more electronic images include one or more CT angiography (CCTA) images.

8. The computer implementation method according to claim 1, further comprising generating a common vascular tree using the first set of captured image data and the second set of captured image data by one or more processors.

9. An image processing system for quantifying coronary artery microvascular disease by processing electronic images, wherein the system is A data storage device that stores instructions for processing the aforementioned electronic image, A processor that executes the instruction, Receiving image data of one or more captured electronic images by one or more processors of an image processing system, wherein a first set of image data is captured before administering one or more drugs to the imaging target, and a second set of image data is captured after administering the one or more drugs to the imaging target, The provision of the image data and patient data set to a machine learning model by one or more processors, the machine learning model being trained to use one or more sets of collected and / or simulated image data and one or more sets of collected and / or simulated patient data to identify coronary microvascular disease (CMD) features in the captured image data and patient data set, and to output one or more CMD measurements and / or predicted CMD end types, The image processing system includes the processor, which is configured to perform an operation that includes transmitting the one or more CMD measurements and / or the predicted CMD end type to a user device.

10. The image processing system according to claim 9, wherein the operation further comprises the processor determining the microvascular resistance reserve (MRR) using the captured imaged data.

11. The image processing system according to claim 10, wherein the set of patient data includes the MRR.

12. The image processing system according to claim 9, wherein the identified CMD features include one or more identified differences between the first set of captured image data and the second set of captured image data.

13. The image processing system according to claim 12, wherein the operation further comprises the processor determining vasodilatory capacity based on the one or more identified differences.

14. The image processing system according to claim 9, wherein the set of patient data includes one or more biomarkers of the patient.

15. The image processing system according to claim 9, wherein the one or more electronic images include one or more CT angiography (CCTA) images.

16. The image processing system according to claim 9, wherein the operation further comprises the processor generating a common vascular tree using the first set of captured image data and the second set of captured image data.

17. A non-temporary computer-readable medium that stores instructions for one or more processors of an image processing system to execute a computer implementation method for processing electronic images to quantify coronary microvascular disease, wherein the method is Receiving image data of one or more captured electronic images by one or more processors of an image processing system, wherein a first set of image data is captured before administering one or more drugs to the imaging target, and a second set of image data is captured after administering the one or more drugs to the imaging target, The provision of the image data and patient data set to a machine learning model by one or more processors, the machine learning model being trained to use one or more sets of collected and / or simulated image data and one or more sets of collected and / or simulated patient data to identify coronary microvascular disease (CMD) features in the captured image data and patient data set, and to output one or more CMD measurements and / or predicted CMD end types, The non-temporary computer-readable medium includes transmitting the one or more CMD measurements and / or the predicted CMD end type to a user device by the one or more processors.

18. The method further comprises determining the microvascular resistance reserve (MRR) using the captured imaging data by one or more processors, according to claim 17, for a non-temporary computer-readable medium.

19. The set of patient data is a non-temporary computer-readable medium according to claim 18, including the MRR.

20. The non-temporary computer-readable medium according to claim 17, wherein the identified CMD features include one or more identified differences between the first set of captured image data and the second set of captured image data.

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