Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network

Abstract

The deep learning model was built on the enhanced super-resolution generative adversarial inline neural network,trained with use of retrospectively identified cine images and evaluated in participants prospectively enrolled from September 2021 toSeptember 2022. The model was applied to breath-hold electrocardiography (ECG)-gated segmented and free-breathing real-time cineimages collected with reduced spatial resolution with use of generalized autocalibrating partially parallel acquisitions (GRAPPA) or CS.The deep learning model subsequently restored spatial resolution. For comparison, GRAPPA-accelerated cine images were collected.Diagnostic quality and artifacts were evaluated by two readers with use of Likert scales and compared with use of Wilcoxon signed-ranktests. Agreement for left ventricle (LV) function, volume, and strain was assessed with Bland-Altman analysis.

Salah Assana

Research Assistant ll

I am a Artificial Intelligence and Healthcare enthusiast with a background in machine learning, signal processing and medical imaging.