US-20260127735-A1 - DEEP LEARNING ESTIMATION OF VASCULAR FLOW AND PROPERTIES

US20260127735A1US 20260127735 A1US20260127735 A1US 20260127735A1US-20260127735-A1

Abstract

A method for characterizing a blood vessel includes receiving anatomical information of the blood vessel and receiving four-dimensional flow data of multiple blood vessels from multiple subjects. The method further includes, using the four-dimensional flow data to train a flow model to determine blood vessel properties, and using the anatomical information of the blood vessel as an input to the trained flow model to estimate one or more properties of the blood vessel.

Inventors

Bharath AMBALE VENKATESH
Joao Lima

Assignees

THE JOHNS HOPKINS UNIVERSITY

Dates

Publication Date: 20260507
Application Date: 20221103

Claims (20)

1 . A method for characterizing a blood vessel, comprising: receiving anatomical information of the blood vessel; receiving four-dimensional flow data of a plurality of blood vessels from a plurality of subjects; using the four-dimensional flow data, training a flow model to determine blood vessel properties; and using the anatomical information of the blood vessel as an input to the trained flow model, estimating one or more properties of the blood vessel.
2 . The method of claim 1 , wherein the anatomical information comprises shape information of the blood vessel.
3 . The method of claim 1 , wherein the anatomical information of the blood vessel is generated from a segmentation process applied to one or more images of the blood vessel that are acquired using one of a doppler ultrasound scanner, a computed tomography scanner, and a magnetic resonance scanner.
4 . The method of claim 1 , wherein the one or more properties of the blood vessel comprise at least one of a wall shear stress of the blood vessel expressed in units of force per unit area, a wall stiffness of the blood vessel expressed in units of pulse wave velocity, a vorticity of the blood vessel expressed in units of inverse time, a velocity of blood within the blood vessel expressed in units of distance over time, and a flow of blood within the blood vessel expressed in units of volume over time.
5 . The method of claim 1 , wherein the blood vessel is one of an aorta, a coronary artery, a carotid artery, a superior vena cava, and an inferior vena cava.
6 . The method of claim 1 , further comprising receiving one or more two-dimensional flow measurements of blood that is one of entering and exiting the blood vessel, wherein the one or more two-dimensional flow measurements are a further input to the trained flow model.
7 . The method of claim 1 , wherein the four-dimensional flow data of the plurality of blood vessels comprises data acquired from the plurality of subjects using magnetic resonance imaging.
8 . The method of claim 1 , wherein the four-dimensional flow data of the plurality of blood vessels comprises simulated data based on the plurality of subjects.
9 . A system for characterizing a blood vessel, comprising: an imaging system; and a data processor configured to communicate with said imaging system to receive anatomical information of the blood vessel, said data processor being further configured to: receive four-dimensional flow data of a plurality of blood vessels from a plurality of subjects; using the four-dimensional flow data, train a flow model to determine blood vessel properties; and using the anatomical information of the blood vessel as an input to the trained flow model, estimate one or more properties of the blood vessel.
10 . The system of claim 9 , wherein the anatomical information comprises shape information of the blood vessel.
11 . The system of claim 9 , wherein the anatomical information of the blood vessel is generated from a segmentation process applied to one or more images of the blood vessel that are acquired using the imaging system.
12 . The system of claim 9 , wherein the imaging system comprises an image processor and at least one of a doppler ultrasound scanner, a computed tomography scanner, and a magnetic resonance scanner.
13 . The system of claim 9 , wherein the one or more properties of the blood vessel comprise at least one of a wall shear stress of the blood vessel expressed in units of force per unit area, a wall stiffness of the blood vessel expressed in units of pulse wave velocity, a vorticity of the blood vessel expressed in units of inverse time, a velocity of blood within the blood vessel expressed in units of distance over time, and a flow of blood within the blood vessel expressed in units of volume over time.
14 . The system of claim 9 , wherein the blood vessel is one of an aorta, a coronary artery, a carotid artery, a superior vena cava, and an inferior vena cava.
15 . The system of claim 9 , wherein the data processor is further configured to receive from the imaging system one or more two-dimensional flow measurements of blood that is one of entering and exiting the blood vessel, wherein the one or more two-dimensional flow measurements are a further input to the trained flow model.
16 . The system of claim 9 , wherein the four-dimensional flow data of the plurality of blood vessels comprises data acquired from the plurality of subjects using magnetic resonance imaging.
17 . The system of claim 9 , wherein the four-dimensional flow data of the plurality of blood vessels comprises simulated data based on the plurality of subjects.
18 . A non-transitory machine-readable medium storing a program for characterizing a blood vessel, which when executed by a data processor, configures said data processor to: receive anatomical information of the blood vessel; receive four-dimensional flow data of a plurality of blood vessels from a plurality of subjects; using the four-dimensional flow data, train a flow model to determine blood vessel properties; and use the anatomical information of the blood vessel as an input to the trained flow model, estimating one or more properties of the blood vessel.
19 . The machine-readable medium of claim 18 , wherein the anatomical information comprises shape information of the blood vessel.
20 . The machine-readable medium of claim 18 , wherein the anatomical information of the blood vessel is generated from a segmentation process applied to one or more images of the blood vessel that are acquired using one of a doppler ultrasound scanner, a computed tomography scanner, and a magnetic resonance scanner.

Description

CROSS-REFERENCE OF RELATED APPLICATION This application claims priority to U.S. Provisional Application No. 63/277,408, filed Nov. 9, 2021, which is incorporated herein by reference in its entirety. BACKGROUND 1. Technical Field Embodiments of this disclosure relate to systems and methods for measurement of vascular flow, and more particularly quantitative properties of blood vessels. 2. Discussion of Related Art Atlas-based morphometry, a concept that refers to the computational analysis of form, is a method of quantifying the major determinants of remodeling at the regional and global levels and may aid in the precise quantification of subclinical disease.1-3 Although these methods are well established in neuroimaging to identify brain anatomical and functional regions, application to the dynamic cardiovascular system has been limited.4 Shape captures an ensemble of aspects associated with the coupling of alterations in cardiac structure with function that can be used to phenotype disease.5 Elevated central arterial stiffness, a hallmark of aging, is associated with adverse clinical outcomes, including coronary heart disease, stroke, and cardiovascular disease (CVD) mortality.6-8 Human studies have shown that greater arterial stiffness is closely related with both systolic and diastolic dysfunction,9-11 and individuals with heart failure (HF) have elevated arterial stiffness.12-14 Comprehensive prospective studies relating arterial stiffness to HF risk in large populations are sparse. The pathogenesis of aortic stiffness-related CVD may be due but also potentiate abnormal aortic shape, particularly in patients with HFpEF.15 Aortic shape has been analyzed before in the setting of specific aortopathies,16-18 and is seen to be a key influencer of blood flow patterns through the aorta.19 Abnormal blood flow patterns and increased wall shear stress are associated with LV remodeling. One recent study has shown that the magnitude of ascending aorta backward flow increases with age and its onset occurs earlier in the cardiac cycle, during LV ejection, with aging.20 Such phenomena are strongly associated with geometric changes of the aortic arch such as dilation and elongation that lead to changes in local gradients in pressure and wall shear stress.19,21 Currently, there are two common methods to perform a comprehensive assessment of vascular flow and stiffness. One technique involves imaging the vascular anatomy followed by the use of computational fluid dynamics (CFD) to assess vascular tissue (wall) properties through the use of time-consuming simulations, which require additional specialized software and may also require specially trained engineering specialists. Another technique involves a comprehensive imaging strategy (4D flow) which is followed by a time-consuming image analysis procedure. 4D flow MRI has greatly expanded the capability to study the pathophysiologic pathways associated with arterial and venous flow and remodeling. The advantage of 4D flow MRI is that full information of flow mechanics is obtained, including full spatial-temporal coverage of the aorta and the estimation of intra-aortic pressure gradients, and flow:22-24 Full 3D wall shear stress estimates can be generated as well. Abnormal blood flow patterns and increased wall shear stress of the aorta (specifically) are associated with LV remodeling and may form important aspects of intervention in heart failure. However, 4D flow MRI has a number of disadvantages. These include long acquisition times (e.g., 5-20 min), which can cause problems with breathing patterns and movement. There is no online post-processing (e.g., on the scanner), and instead needs special software. The post-processing also requires large memory as there needs to be ability to deal with large datasets. Furthermore, 4D flow MRI has lower temporal resolution as compared to 2D phase-contrast MRI. The complexity of 4D flow post-processing requirements have resulted in limited use, particularly in large population studies, in spite of recent advances to both improve post-processing time as well as resolution.25-27 For these reasons, it is difficult to assess the quality of scan when the patient is on the table. SUMMARY An embodiment of the present invention is a method for characterizing a blood vessel. The method includes receiving anatomical information of the blood vessel and receiving four-dimensional flow data of multiple blood vessels from multiple subjects. The method further includes using the four-dimensional flow data to train a flow model to determine blood vessel properties, and using the anatomical information of the blood vessel as an input to the trained flow model to estimate one or more properties of the blood vessel. Another embodiment of the present invention is a system for characterizing a blood vessel, including an imaging system and a data processor. The data processor is configured to communicate with the imaging system to receive anatomical in