FIELD OF THE DISCLOSURE

The invention generally relates to the fully automated detection of coronary vessels and their branches in angiograms and, more particularly, to the calculation of a diameter of such vessels, detection of stenosis and determination of the percent narrowing of stenosis and functional flow limitation from the stenosis.

BACKGROUND

The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventor, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.

Coronary Artery Disease (CAD) is among the leading causes of death in the United States, affecting more than 15 million Americans. CAD is characterized by plaque build-up from atherosclerosis in the coronary arteries, which results in the narrowing (also known as stenosis) or blockage of coronary arteries and can lead to symptoms such as angina and possibly myocardial infarction.

Herein, ‘state of vessel occlusion’ refers to what is commonly understood as CAD, e.g., narrowing (localized or diffused) of the epicardial coronary arteries as seen in imaging studies and characterized by either anatomical or functional indices. Conversely, ‘state of microvascular disease’ refers to disease in the coronary microcirculation, characterized by the loss of vasodilatory capacity.

The leading invasive diagnostic method for CAD is coronary angiography, in which a contrast agent is injected into a patient's vessels via a catheter and imaged to characterize stenosis severity. This method relies on visualization of anatomic abnormalities and is semi-quantitative, as visual inspection simply approximates the percent area reduction of the lumen. Reductions in diameter that are estimated to be 70% or greater often result in further evaluation or a revascularization procedure, e.g., a coronary stent placement.

A physiologic-based and more quantitative approach to assess coronary stenosis is to compute Fractional Flow Reserve (FFR), a metric defined as the ratio between hyperemic flow in a diseased artery and the expected hyperemic flow in the same artery without stenosis. In the coronary artery, for example, FFR can be expressed as the ratio of a distal coronary pressure to a proximal coronary pressure. For example, a FFR lower than 0.80 indicates the presence of a severe stenosis requiring revascularization due to the compromised flow to the vascular bed distal to the vessel. Revascularization decisions that incorporate FFR have been shown to improve outcomes compared to angiography alone. Determination of FFR is also robust to different patient geometries, accounting for the contributions of collateral vessels to flow and lesion geometry, for example.

Despite its benefits, healthcare professionals often do not measure FFR for a patient, due to the invasive nature of using a catheter-based pressure measurement. Some have found that physicians choose not to perform FFR in nearly two-thirds of all cases, citing the risk to the patient, lack of resources, and additional cost. Another drawback of FFR is its variability due to different hemodynamic conditions, within a patient.

There is a need for more accurate and less-invasive techniques for diagnosis CAD. More specifically, there is a need for a non-invasive, user-independent approach for FFR measurement that would not pose a risk to the patient.

Recently, some have proposed non-invasive computational workflows for determining FFR and assessing CAD severity. These efforts have adopted two distinct approaches. A first approach relies on computed tomography angiography (CTA) data to reconstruct the 3D geometry the coronary tree. The vessel lumen is segmented using edge-detection or machine learning algorithms and the output is manually corrected by an expert. Computational Fluid Dynamics (CFD) simulations are run on the CTA-derived vessel geometry, and FFR is computed on the company's servers or on the cloud. While this non-invasive approach has shown promising results in clinical trials, the use of CTA is severely affected by imaging artifacts due to calcification, which make it difficult to delineate the boundaries of the vessel lumen. CTA is also limited in its ability to detect small serial lesions or to capture fine vessel geometry due to its lower spatial resolution compared to angiography. Lastly, CTA data does not provide information on flow, and thus the boundary conditions (BC) for the CFD analysis of hemodynamics typically rely on morphometric or population data and are thus non-patient-specific.

A second approach, also based on non-invasive computational workflows, relies on multi-plane angiography data to reconstruct the vessel geometry before performing physics-based flow simulations. The main benefit of using angiography is its superior capacity to detect the vessel lumen boundary in the presence of calcified stenoses, and the higher spatial resolution compared to CTA, increasing the sensitivity of the geometry reconstruction algorithm. Furthermore, time-resolved angiography has information on how the contrast agent moves through the vessels and can therefore be used to estimate blood velocity and inform the BC for the CFD analysis. However, angiography-based approaches for FFR quantification have a fundamental challenge, namely the reconstruction of the 3D geometry of the vessels of interest from a set of 2D images which are acquired over time and for different positions of the patient table. Furthermore, all angiography-based approaches for FFR quantification have produced workflows that require substantial inputs by an operator to identify the vessels of interest and to assist with the segmentation. Lastly, all angiography-based approaches have either considered reconstruction of a single coronary artery or used highly simplified methods to model the flow physics. These shortcomings effectively counteract the benefit of using high-resolution angiography data, the most commonly performed procedure for CAD diagnosis.

Regardless of the approach, all computational-derived FFR methods have shown poor predictive performance around the critical diagnostic value of FFR=0.8, due to the aforementioned limitations on image data quality, lack of information on flow, need for operator input, and computational modeling assumptions. Therefore, there is currently no pipeline for accurate FFR computation that could be effectively deployed to any hospital in the nation.

A significant need exists for a more accurate and less-invasive techniques for CAD diagnosis, using a non-invasive approach for FFR measurement that is fully-automated.

SUMMARY OF THE INVENTION

Techniques are provided for anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques. Techniques herein address the shortcomings of conventional CAD assessment, by using novel methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

In example embodiments, the present techniques provide processes to assess both anatomical and functional severity of CAD through customized machine learning and computational modeling methods, from the use of both static and dynamic coronary angiography data, with the final goal of determining FFR in a less risky and more accurate manner.

The use of functional markers of disease has gained significant traction in the last few years, displacing simpler anatomy-based markers. In the field of cardiology, fractional flow reserve (FFR) is a hemodynamic index (i.e., a normalized pressure gradient under conditions of maximum flow) that has shown better diagnostic outcomes than anatomy-based makers. In example embodiments herein, two-dimensional (2D) angiography data is used, in particular dynamic angiography data that offers descriptions of the transport of a contrast agent (dye) down the vessels of interest. The 2D time-resolved angiography data is used to inform computational simulations thereby obtaining more accurate predictions of the FFR than those without dynamic transport of contrast agent down the vessels. Further still, in example embodiments, three-dimensional (3D) geometric models are generated from the 2D angiography data, and those 3D geometric models are used for simulation of hemodynamic conditions, including FFR.

In accordance with an example, a computer-implemented method for assessing CAD, the method comprises: receiving, by one or more processors, angiography image data of a vessel inspection region for a subject, wherein the angiography image data comprises angiography images captured over a sampling time period; applying, by the one or more processors, the angiography image data to a vessel segmentation machine learning model and generating, using the vessel segmentation machine learning model, two-dimensional (2D) segmented vessel images for the vessel inspection region; by the one or more processors, generating from the 2D segmented vessel images a three-dimensional (3D) segmented vessel tree geometric model of vessels within the vessel inspection region; and applying, by the one or more processors, the 3D segmented vessel tree model to a fluid dynamics-machine learning model to assimilate, using the fluid dynamics machine learning model, flow data over the sampling time period for one or more vessels within the vessel inspection region; applying, by the one or more processors, the 3D segmented vessel tree model and the assimilated flow data to a 3D, high-fidelity, computational fluid dynamics model; and determining, by the one or more processors, a state of vessel occlusion for the one or more vessels within the vessel inspection region.

In accordance with an example, a computing device configured to assessing CAD comprises: one or more processors and one or more computer-readable memories storing instructions that when executed cause the one or processors to: receive, by one or more processors, angiography image data of a vessel inspection region for a subject, wherein the angiography image data comprises angiography images captured over a sampling time period; apply, by the one or more processors, the angiography image data to a vessel segmentation machine learning model and generating, using the vessel segmentation machine learning model, two-dimensional (2D) segmented vessel images for the vessel inspection region; by the one or more processors, generate from the 2D segmented vessel images a three-dimensional (3D) segmented vessel tree geometric model of vessels within the vessel inspection region; and apply, by the one or more processors, the 3D segmented vessel tree model to a fluid dynamics-machine learning model to assimilate, using the fluid dynamics machine learning model, flow data over the sampling time period for one or more vessels within the vessel inspection region; apply, by the one or more processors, the 3D segmented vessel tree model and the assimilated flow data to a 3D, high-fidelity, computational fluid dynamics model; and determine, by the one or more processors, a state of vessel occlusion for the one or more vessels within the vessel inspection region.

In accordance with an another example, a computer-implemented method for assessing coronary artery disease, the method comprises: receiving, by one or more processors, a plurality of angiography images of a vessel inspection region for a subject, wherein the angiography images are captured over a sampling time period and wherein the vessel inspection region comprises one or more vessels; applying, by the one or more processors, the angiography images to a vessel segmentation machine learning model and generating, using the vessel segmentation machine learning model, two-dimensional (2D) segmented vessel images for the one or more vessels; by the one or more processors, generating, from the 2D segmented vessel images, a one-dimensional (1D) segmented vessel tree geometric model of the one or more vessels; applying, by the one or more processors, the 1D segmented vessel tree model to a fluid dynamics machine learning model to assimilate, using the fluid dynamics machine learning model, flow data over the sampling time period for the one or more vessels; applying, by the one or more processors, the 1D segmented vessel tree model and the assimilated flow data to a graph-theory based reduced order model based on computational fluid dynamics model; and determining, by the one or more processors, a state of vessel occlusion.

In accordance with an example, a computing device configured to assessing CAD comprises: one or more processors and one or more computer-readable memories storing instructions that when executed cause the one or processors to: receive, by one or more processors, a plurality of angiography images of a vessel inspection region for a subject, wherein the angiography images are captured over a sampling time period and wherein the vessel inspection region comprises one or more vessels; apply, by the one or more processors, the angiography images to a vessel segmentation machine learning model and generate, using the vessel segmentation machine learning model, two-dimensional (2D) segmented vessel images for the one or more vessels; by the one or more processors, generate, from the 2D segmented vessel images, a one-dimensional (1D) segmented vessel tree geometric model of the one or more vessels; apply, by the one or more processors, the 1D segmented vessel tree model to a fluid dynamics machine learning model to assimilate, using the fluid dynamics machine learning model, flow data over the sampling time period for the one or more vessels; apply, by the one or more processors, the 1D segmented vessel tree model and the assimilated flow data to a graph-theory based reduced order model based on computational fluid dynamics model; and determine, by the one or more processors, a state of vessel occlusion.

