Joint image reconstruction and segmentation of real-time cardiovascular magnetic resonance imaging in free-breathing using a model based on disentangled representation learning.

ABSTRACT:

Background

To investigate image quality and agreement of derived cardiac function parameters in a novel joint image reconstruction and segmentation approach based on disentangled representation learning, enabling real-time cardiac cine imaging during free-breathing.

Methods

A multi-tasking neural network architecture, incorporating disentangled representation learning, was trained using simulated examinations based on data from a public repository along with cardiovascular magnetic resonance (CMR) scans specifically acquired for model development. An exploratory feasibility study evaluated the method on undersampled real-time acquisitions using an in-house developed spiral balanced steady-state free precession pulse sequence in eight healthy participants and five patients wi

SUBMITTER: Wech T 

PROVIDER: S-EPMC11874730 | biostudies-literature | 2025 Summer

REPOSITORIES: biostudies-literature