NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit
  sensitivity maps
Felix Frederik Zimmermann Andreas Kofler
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