Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.

ABSTRACT:

Purpose

This study aims to develop an automated framework for operator-independent assessment of cardiac ventricular function from highly accelerated images.

Methods

We introduce a deep learning framework that generates reliable ventricular volumetric parameters and strain measures from fully sampled and retrospectively accelerated MR images. This method integrates image registration, motion-compensated reconstruction, and segmentation in a synergetic loop for mutual refinement. The evaluation was performed on an in-house dataset of healthy and cardiovascular-diseased subjects. We examined the performance of the underlying tasks, including registration and segmentation, and their impact on derived parameters related to ventricular function.

Results

The proposed approa

SUBMITTER: Ghoul A 

PROVIDER: S-EPMC12746400 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature