Super-MoCo-MoDL: A combined super-resolution and motion-corrected undersampled deep-learning reconstruction framework for three-dimensional whole-heart cardiac magnetic resonance imaging.

ABSTRACT:

Background

Cardiac magnetic resonance (CMR) is a well-established imaging modality for the assessment of cardiovascular diseases. However, attainable image resolution remains lower than that of X-ray computed tomography (CT) due to long scan times and the need for respiratory motion correction. In this work, we combine a previously proposed motion-corrected model-based deep-learning reconstruction for undersampled three-dimensional (3D) whole-heart CMR with data-consistent super-resolution to enable high-resolution 3D whole-heart CMR from significantly shortened scans.

Methods

Our proposed framework, Super motion-corrected (MoCo) model-based deep-learning (MoDL), utilizes two neural networks; the first estimates non-rigid respiratory motion from zero-padded and zero-filled b

SUBMITTER: Phair A 

PROVIDER: S-EPMC12805380 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature