Method for generating a personalized 4d cardiac model

Patent metadata

Field	Value
Publication number	US-2025272833-A1
Application number	US-202519058763-A
Country	US
Kind code	A1
Filing date	Feb 20, 2025
Priority date	Feb 23, 2024
Publication date	Aug 28, 2025
Grant date	—

Abstract

Planning cardiac surgery is a complex task. Current heart models are mostly static and provide no functional information or personalization apart from structural information from imaging modalities like MRI, CT etc. The embodiments of present disclosure addresses unresolved problem of generating a personalized four dimensional cardiac model that integrates blood flow velocity, pressure profile and volume on three-dimensional cardiac structure, providing a comprehensive tool for pre or post-surgical planning and analysis. Unlike existing techniques, which often rely on separate assessments of cardiac anatomy and hemodynamics, using invasive methods, the present disclosure provides a 4D cardiac model that captures the person specific dynamic interaction between cardiac structures and blood flow throughout the cardiac cycle. Further, the 4D cardiac model enables clinicians to visualize and quantify complex flow patterns and pressure distributions within the region of interest in the heart, facilitating precise surgical planning and outcome prediction especially in pediatric heart surgery.

First claim

What is claimed is:

1 . A processor-implemented method ( 200 ) for generating a four-dimensional (4D) cardiac model, the method comprising:
receiving, via one or more hardware processors, a plurality of multi-modal images of a heart of a subject under observation, wherein the plurality of multi-modal images includes Magnetic Resonance Imaging (MRI) and echocardiography (ECHO) images; generating, via the one or more hardware processors, a three-dimensional (3D) cardiac structure from the plurality of multi-modal images using segmentation; selecting, via the one or more hardware processors, a region of interest (ROI) and estimating a reference blood flow velocity from the 3D cardiac structure; measuring, via the one or more hardware processors, a blood flow velocity profile and a pressure profile throughout the ROI using modified Lattice Boltzmann Method based computation model (LBM model); and generating, via the one or more hardware processors, a four-dimensional (4D) cardiac model by registering the blood flow velocity profile and the pressure profile with the 3D cardiac structure using optical flow algorithms, wherein the 4D cardiac model is calibrated using the 3D cardiac structure, and the reference blood flow.

2 . The method of claim 1 , wherein obtaining the reference blood flow velocity from the 3D cardiac structure comprises:
obtaining an optical flow of blood within the ROI by analyzing pixel intensities; obtaining a flow velocity per pixel based on the optical flow; identifying a peak flow velocity from the flow velocity per pixel values; estimating a plurality of scale factors corresponding to systole and diastole phases of the ROI from the peak flow velocity and an actual peak; applying the scale factors to calibrate the flow velocity from the optical flow; calculating a set of flow vectors corresponding to the calibrated flow velocity within the ROI; estimating magnitude and angle of each of the set of flow vectors; calculating speed of blood flow within the ROI based on the magnitude and angle of the set of flow vectors; correcting/calibrating the speed of blood flow using an actual speed of blood flow obtained from the plurality of multi-modal images; and estimating the reference blood flow velocity based on the corrected speed of blood flow within the ROI.

3 . The method of claim 2 , wherein the plurality of scale factors is obtained based on systolic and diastolic phases of the ROI, and wherein the actual peak velocity is obtained from the plurality of multi-modal images.

4 . The method of claim 1 , wherein computing the blood flow velocity profile comprises:
computing wall velocity and pressure profile of blood flow within the ROI based on structural analysis of the 3D cardiac structure; concurrently obtaining a set of boundary conditions for the ROI; wherein the set of boundary conditions includes an inlet velocity, and an inlet pressure of the blood flow captured from a lumped zero-dimensional (0D) hemodynamic model; and wherein the lumped 0D hemodynamic model is a closed-loop, real-time, cardiovascular simulation model representing dynamics of four cardiac chambers, heart valves, and lumped pulmonary and systemic circulation, and calculating the blood flow velocity profile and pressure profile within the ROI based on the wall velocity, and the set of boundary conditions using the modified LBM model.

5 . The method of claim 1 , wherein the 4D cardiac model provides a personalized functional and structural model of the heart.

6 . A system, comprising:
a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
receive a plurality of multi-modal images of a heart, wherein the plurality of multi-modal images includes Magnetic Resonance Imaging (MRI) and echocardiography (ECHO) images;
generate a three-dimensional (3D) cardiac structure from the plurality of multi-modal images using segmentation;
select a region of interest (ROI) and estimating a reference blood flow velocity from the 3D cardiac structure;
measure a blood flow velocity profile and a pressure profile throughout the ROI using modified Lattice Boltzmann Method based computation model (LBM model); and
generate a four-dimensional (4D) cardiac model by registering the blood flow velocity and the pressure profile with the 3D cardiac structure using optical flow algorithms, wherein the 4D cardiac model is calibrated using the 3D cardiac structure, and the reference blood flow.

7 . The system as claimed in claim 6 , wherein the one or more hardware processors are configured to obtain the reference blood flow velocity is from the 3D cardiac structure by performing the following steps:
obtaining an optical flow of blood within the ROI by analyzing pixel intensities, obtaining a flow velocity per pixel based on the optical flow; identifying a peak flow velocity from the flow velocity per pixel values; estimating a plurality of scale factors corresponding to systole and diastole phases of the ROI from the peak flow velocity and an actual peak; applying the scale factors to calibrate the flow velocity from the optical flow; calculating a set of flow vectors corresponding to the calibrated flow velocity within the ROI; estimating magnitude and angle of each of the set of flow vectors; calculating speed of blood flow within the ROI based on the magnitude and angle of the set of flow vectors; correcting/calibrating the speed of blood flow using an actual speed of blood flow obtained from the plurality of multi-modal images; and estimating the first reference blood velocity based on the corrected speed of blood flow within the ROI.

8 . The system as claimed in claim 7 , wherein one or more hardware processors are configured to obtain the plurality of scale factors based on systolic and diastolic phases of the ROI and wherein the actual peak velocity is obtained from the plurality of multi-modal images.

9 . The system as claimed in claim 6 , wherein one or more hardware processors are configured to compute the blood flow velocity profile by performing:
computing wall velocity and pressure profile of blood flow within the ROI based on structural analysis of the 3D cardiac structure; concurrently obtaining a set of boundary conditions for the ROI,
wherein the set of boundary conditions includes an inlet velocity and an inlet pressure of the blood flow captured from a lumped zero-dimensional (0D) hemodynamic model; and
wherein the lumped 0D hemodynamic model is a closed-loop, real-time, cardiovascular simulation model representing dynamics of four cardiac chambers, heart valves, and lumped pulmonary and systemic circulation.
calculating the blood flow velocity profile and pressure profile within the ROI based on the wall velocity, and the set of boundary conditions using the modified LBM model.

10 . The system as claimed in claim 6 , wherein the one or more hardware processors are configured to calibrate the 4D cardiac model using the 3D cardiac structure, the reference blood flow and the blood flow velocity profile, and wherein the 4D cardiac model provides a personalized functional and structural model of the heart.

11 . One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:
receiving a plurality of multi-modal images of a heart, wherein the plurality of multi-modal images includes Magnetic Resonance Imaging (MRI) and echocardiography (ECHO) imag

Assignees

• Tata Consultancy Services Ltd

Inventors

• Kanakatte Gurumurthy Aparna
• Ghose Avik
• Bhatia Divya Manoharlal
• Mazumder Oishee
• Sinha Aniruddha
• Agarwal Swapna
• Khandelwal Sundeep
• Chaudhury Rupam
• Banerjee Shilajit
• Das Sudip Kumar
• Chandel Vivek

Classifications

• A61B6/503

  for diagnosis of the heart · CPC title

• A61B6/5247

  combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound · CPC title

• A61B8/0883

  for diagnosis of the heart · CPC title

• G06T2207/30048

  Heart; Cardiac · CPC title

• A61B5/02028

  Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction · CPC title