NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps

TL;DR

This paper introduces a deep learning-based cardiac MRI reconstruction method that unrolls an iterative process without requiring explicit coil-sensitivity map estimation, effectively capturing inter-coil relationships.

Contribution

It proposes a novel CNN-based unrolled reconstruction approach that implicitly learns coil relationships and adapts to acquisition parameters without explicit sensitivity maps.

Findings

Achieved high PSNR and SSIM scores on MICCAI CMRxRecon Challenge

Ranked 4th among competing teams in the challenge

Code will be publicly available for reproducibility

Abstract

We present a novel learned image reconstruction method for accelerated cardiac MRI with multiple receiver coils based on deep convolutional neural networks (CNNs) and algorithm unrolling. In contrast to many existing learned MR image reconstruction techniques that necessitate coil-sensitivity map (CSM) estimation as a distinct network component, our proposed approach avoids explicit CSM estimation. Instead, it implicitly captures and learns to exploit the inter-coil relationships of the images. Our method consists of a series of novel learned image and k-space blocks with shared latent information and adaptation to the acquisition parameters by feature-wise modulation (FiLM), as well as coil-wise data-consistency (DC) blocks. Our method achieved PSNR values of 34.89 and 35.56 and SSIM values of 0.920 and 0.942 in the cine track and mapping track validation leaderboard of the MICCAI STACOM CMRxRecon Challenge, respectively, ranking 4th among different teams at the time of writing. Code will be made available at https://github.com/fzimmermann89/CMRxRecon