An All-in-one Approach for Accelerated Cardiac MRI Reconstruction

TL;DR

This paper introduces a deep learning-based, stepwise reconstruction method for accelerated cardiac MRI that effectively handles highly undersampled data, improving image quality and diagnostic accuracy.

Contribution

It presents a novel Patch-GAN based reconstruction approach tailored for multi-contrast and multi-view CMR imaging, validated on a challenging dataset.

Findings

Achieved SSIM scores of 99.07 and 97.99 on challenge tasks.

Outperformed previous reconstruction methods.

Enabled faster, high-quality cardiac MRI imaging.

Abstract

Cardiovascular magnetic resonance (CMR) imaging is the gold standard for diagnosing several heart diseases due to its non-invasive nature and proper contrast. MR imaging is time-consuming because of signal acquisition and image formation issues. Prolonging the imaging process can result in the appearance of artefacts in the final image, which can affect the diagnosis. It is possible to speed up CMR imaging using image reconstruction based on deep learning. For this purpose, the high-quality clinical interpretable images can be reconstructed by acquiring highly undersampled k-space data, that is only partially filled, and using a deep learning model. In this study, we proposed a stepwise reconstruction approach based on the Patch-GAN structure for highly undersampled k-space data compatible with the multi-contrast nature, various anatomical views and trajectories of CMR imaging. The proposed approach was validated using the CMRxRecon2024 challenge dataset and outperformed previous studies. The structural similarity index measure (SSIM) values for the first and second tasks of the challenge are 99.07 and 97.99, respectively. This approach can accelerate CMR imaging to obtain high-quality images, more accurate diagnosis and a pleasant patient experience.