Real-time cardiac cine MRI -- A comparison of a diffusion probabilistic model with alternative state-of-the-art image reconstruction techniques for undersampled spiral acquisitions

TL;DR

This study compares a novel diffusion probabilistic model with existing techniques for undersampled spiral cardiac MRI, demonstrating comparable image quality and improved sharpness, enabling high-quality free-breathing cardiac imaging in under a minute.

Contribution

The paper introduces a diffusion probabilistic model for real-time cardiac MRI reconstruction and compares it with state-of-the-art methods, highlighting its potential advantages.

Findings

Diffusion model offers slightly increased image sharpness.

Real-time MRI enables high-quality, free-breathing cardiac imaging in under a minute.

Diffusion model has longer inference times, posing a challenge for clinical use.

Abstract

ECG-gated cine imaging in breath-hold enables high-quality diagnostics in most patients, arrhythmia and inability to hold breath, however, can severely corrupt outcomes. Real-time cardiac MRI in free-breathing leverages robust and faster investigations regardless of these confounding factors. With the need for sufficient acceleration, adequate reconstruction methods, which transfer data into high quality images, are required. Undersampled spiral real-time acquisitions in free-breathing were conducted in a study with 16 healthy volunteers and 5 patients. Image reconstructions were performed using a novel score-based diffusion model, as well as a variational network and different compressed sensing approaches. The techniques were compared by means of an expert reader study, by calculating scalar metrics and difference images with respect to a segmented reference, and by a Bland-Altman analysis of cardiac functional parameters. In participants with irregular RR-cycles, spiral real-time acquisitions showed superior image quality with respect to the clinical reference standard. Reconstructions using the diffusion model, the variational network and l1-wavelets offered an overall comparable image quality, however sharpness was slightly increased by the diffusion approach. While slightly larger ejection fractions for the real-time acquisitions were exhibited with a bias of 1.6% for healthy subjects, differences in the data acquisition procedure resulted in uncertainties of 7.4%. The proposed real-time technique enables free-breathing acquisitions of spatio-temporal images with high-quality, covering the entire heart in less than one minute. Evaluation of the ejection fractions using the segmented reference can be significantly corrupted due to arrhythmias and averaging effects. Prolonged inference times of the diffusion model represent the main obstacle to overcome for clinical translation.