Method and device for determining myocardial strain parameters based on 4Dcta images

By using a Mamba-based neural network and affine registration network for myocardial segmentation and motion artifact correction, the problem of low segmentation efficiency in myocardial strain analysis is solved, achieving efficient and accurate determination of myocardial strain parameters, which is applicable to the diagnosis of various heart diseases.

CN122289103APending Publication Date: 2026-06-26SECOND AFFILIATED HOSPITAL OF COLLEGE OF MEDICINEOF XIAN JIAOTONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SECOND AFFILIATED HOSPITAL OF COLLEGE OF MEDICINEOF XIAN JIAOTONG UNIV
Filing Date
2024-12-25
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies for myocardial strain analysis suffer from low segmentation efficiency, complex motion artifact correction, and cumbersome operation procedures, resulting in low efficiency in myocardial strain processing.

Method used

Myocardial segmentation is performed using a Mamba-based neural network, combined with an affine registration network to eliminate motion artifacts. Myocardial strain parameters are determined through motion tracking and hierarchical block segmentation. Mamba modules are used to capture local fine-grained and long-range dependencies, reducing computational complexity.

Benefits of technology

It achieves efficient and accurate myocardial segmentation and strain parameter determination, improves processing speed and accuracy, simplifies operation procedures, and is suitable for multiple tests and the diagnosis of various heart diseases.

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Abstract

This application discloses a method and apparatus for determining myocardial strain parameters based on 4DCTA images. The method includes: using a Mamba-based neural network to separate a first myocardial region image sequence from an original image sequence; performing motion correction on the myocardial region images in the first myocardial region image sequence to eliminate motion artifacts, resulting in a second myocardial region image sequence; obtaining the deformation field of the left ventricular myocardium region between consecutive myocardial region image frames in the second myocardial region image sequence through motion tracking; performing layering and block segmentation on the myocardial region images in the second myocardial region image sequence; and determining myocardial strain parameters using the deformation field of the left ventricular myocardium region between consecutive myocardial region image frames in the second myocardial region image sequence and the layering and block segmentation results of the myocardial region images in the second myocardial region image sequence. This application solves the technical problem of low efficiency in myocardial strain processing in related technologies.
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