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

翻译：本文提出了一种基于深度卷积神经网络（CNN）与算法非迭代的新型学习型图像重建方法，用于多接收线圈加速心脏磁共振成像。与许多现有需要将线圈灵敏度映射（CSM）估计作为独立网络组件的学习型磁共振图像重建技术不同，本方法避免了显式的CSM估计，而是通过隐式捕捉并学习利用图像间的线圈关联性来实现重建。该方法由一系列具有共享潜在信息的新型学习型图像与k空间模块组成，通过特征级调制（FiLM）适应采集参数，并包含线圈级数据一致性（DC）模块。在MICCAI STACOM CMRxRecon挑战赛的电影序列赛道和定量标测赛道验证排行榜中，本方法分别取得了34.89与35.56的峰值信噪比（PSNR）以及0.920与0.942的结构相似性指数（SSIM），截至撰稿时在参赛团队中位列第四。代码将在https://github.com/fzimmermann89/CMRxRecon 公开。