NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps
This addresses the challenge of efficient cardiac MRI reconstruction for medical imaging, offering a novel approach that eliminates the need for explicit sensitivity maps, though it is incremental as it builds on existing unrolled deep learning methods.
The paper tackled the problem of accelerated cardiac MRI reconstruction by proposing a method that avoids explicit coil-sensitivity map estimation, instead learning inter-coil relationships implicitly, achieving PSNR values of 34.89 and 35.56 and SSIM values of 0.920 and 0.942 in validation leaderboards.
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