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 this https URL

通过深度卷积神经网络和算法展开，我们提出了一种新颖的学习图像重建方法，用于加速心脏 MRI，基于多个接收线圈，与许多现有的学习 MR 图像重建技术相比，我们的方法避免了明确的线圈灵敏度图（CSM）估计，并隐含地捕捉和学习了图像之间的不同线圈之间的关系。