Deep learning reconstruction for fast cardiovascular magnetic resonance imaging protocol: A comparative study with conventional cardiovascular magnetic resonance.

ABSTRACT:

Background

Cardiovascular magnetic resonance (CMR) is a reference-standard modality for diagnosis of cardiac diseases, although its clinical application is restricted by prolonged acquisition times. Recently, artificial intelligence, particularly deep learning (DL), has exhibited the potential to accelerate the CMR acquisition through technological advances. Prospective validation of its diagnostic performance across multiple clinical sequences remains underexplored. This research aims to assess the functions of the compressed sensing artificial intelligence (CSAI) algorithm in accelerating CMR acquisition, enhancing image quality, and maintaining diagnostic accuracy versus conventional sensitivity encoding (SENSE) reconstruction.

Methods

A total of 105 participants schedule

SUBMITTER: Hua Y 

PROVIDER: S-EPMC12811447 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature