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：摘要：我们提出了一种基于深度卷积神经网络（CNN）与算法展开技术的新型学习型图像重建方法，用于加速多接收线圈心脏磁共振成像。与许多现有需要将线圈灵敏度图（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，本文方法避免了显式的CSM估计。该模型通过隐式捕获并学习利用图像间的线圈关系来替代上述步骤。本方法包含一系列新型学习型图像块与k空间块：通过特征级调制（FiLM）实现潜在信息共享并自适应采集参数，同时结合线圈级数据一致性（DC）模块。在MICCAI STACOM CMRxRecon挑战赛的电影序列与参数映射验证排行榜中，本方法分别取得了PSNR值为34.89和35.56、SSIM值为0.920和0.942的成绩，在投稿时位列各参赛团队第4名。代码将发布于https://github.com/fzimmermann89/CMRxRecon