Determining biological properties of atherosclerotic plaque, coronary artery disease, or vasculopathy
Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.
1. A system for implementing artificial intelligence and a layered analytics framework, the system comprising:
a processor configured to:
post-process received imaging data of Computed Tomography (CT), Magnetic resonance (MR), Ultrasound (US), or Positron Emission Tomography (PET) of a patient;
determine, based on the received imaging data, a first set of segmentations of a vessel's lumen, a vessel's wall, or a combination thereof;
determine, based on the received imaging data and the first set of segmentations, a second set of segmentations including one or more cross sectional slices of the vessel;
determine, based on the first set of segmentations and the second set of segmentations, a set of quantities that identify, characterize or both atherosclerotic plaque; and
output the identification, characterization or both of the atherosclerotic plaque.
2. The system of claim 1 wherein the processor the determination of the quantities is based on machine learning.
3. The system of claim 1 wherein the processor is further configured to determine hemodynamic properties based on the set of segmentations.
4. The system of claim 1 wherein the processor is further configured to determine one or more of blood pressure, blood flow velocity, flow reserve, or vessel wall shear stress based on the set of segmentations.
5. A method for implementing artificial intelligence and a layered analytics framework, the method comprising:
post-processing received imaging data of Computed Tomography (CT), Magnetic resonance (MR), Ultrasound (US), or Positron Emission Tomography (PET) of a patient;
determining, based on the received imaging data, a first set of segmentations of a vessel's lumen, a vessel's wall, or a combination thereof;
determining, based on the received imaging data and the first set of segmentations, a second set of segmentations including one or more cross sectional slices of the vessel;
determining, based on the first set of segmentations and the second set of segmentations, a set of quantities that identify, characterize or both atherosclerotic plaque; and
outputting the identification, characterization or both of the atherosclerotic plaque.
6. The method of claim 5 wherein determining the quantities further comprises applying one or more machine learning algorithms.
7. The method of claim 5 further comprising determining hemodynamic properties based on the set of segmentations.
8. The method of claim 5 further comprising determining one or more of blood pressure, blood flow velocity, flow reserve, or vessel wall shear stress based on the set of segmentations.
9. One or more non-transitory computer-readable storage media comprising instructions that are executable to cause one or more processors to:
post-process received imaging data of Computed Tomography (CT), Magnetic resonance (MR), Ultrasound (US), or Positron Emission Tomography (PET) of a patient;
determine, based on the received imaging data, a first set of segmentations of a vessel's lumen, a vessel's wall, or a combination thereof;
determine, based on the received imaging data and the first set of segmentations, a second set of segmentations including one or more cross sectional slices of the vessel;
determine, based on the first set of segmentations and the second set of segmentations, a set of quantities that identify, characterize or both atherosclerotic plaque; and
output the identification, characterization or both of the atherosclerotic plaque.
10. The one or more non-transitory computer-readable storage media of claim 9 where the instructions when executed further cause one or more processors to determine the quantities based on machine learning.
11. The one or more non-transitory computer-readable storage media of claim 9 where the instructions when executed further cause one or more processors to determine hemodynamic properties based on the set of segmentations.
12. The one or more non-transitory computer-readable storage media of claim 9 where the instructions when executed further cause one or more processors to determine one or more of blood pressure, blood flow velocity, flow reserve, or vessel wall shear stress based on the set of segmentations.