论文标题
使用插件方法的自由呼吸心血管MRI与博学的Denoiser
Free-breathing Cardiovascular MRI Using a Plug-and-Play Method with Learned Denoiser
论文作者
论文摘要
心脏磁共振成像（CMR）是一种非侵入性成像方式，可对心血管系统进行全面的评估。但是，CMR的临床实用性受到长期收购时间的阻碍。在这项工作中，我们提出并验证一种从无效的多型芯数据数据中进行CMR重建的插件（PNP）方法。为了充分利用CMR固有的丰富图像结构，我们将PNP框架与基于深度学习（DL）的DeNoiser配对，该框架是使用高质量，呼吸控制心脏Cine图像的时空斑块训练的。由此产生的“ PNP-DL”方法迭代了数据一致性和降级子例程。我们将PNP-DL的重建性能与使用八个呼吸和十个实时（RT）自由呼吸心脏的CINE数据集的重建性能与压缩传感（CS）的重建性能进行了比较。我们发现，对于呼吸数据集，PNP-DL比常用CS方法具有多个DB的优势。对于RT自由呼吸数据集（如果没有地面真相，PNP-DL在定性评估中获得更高的分数。结果突出了PNP-DL加速RT CMR的潜力。
Cardiac magnetic resonance imaging (CMR) is a noninvasive imaging modality that provides a comprehensive evaluation of the cardiovascular system. The clinical utility of CMR is hampered by long acquisition times, however. In this work, we propose and validate a plug-and-play (PnP) method for CMR reconstruction from undersampled multi-coil data. To fully exploit the rich image structure inherent in CMR, we pair the PnP framework with a deep learning (DL)-based denoiser that is trained using spatiotemporal patches from high-quality, breath-held cardiac cine images. The resulting "PnP-DL" method iterates over data consistency and denoising subroutines. We compare the reconstruction performance of PnP-DL to that of compressed sensing (CS) using eight breath-held and ten real-time (RT) free-breathing cardiac cine datasets. We find that, for breath-held datasets, PnP-DL offers more than one dB advantage over commonly used CS methods. For RT free-breathing datasets, where ground truth is not available, PnP-DL receives higher scores in qualitative evaluation. The results highlight the potential of PnP-DL to accelerate RT CMR.
