Magnetic Resonance Imaging represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully sampled k-space data under motion in clinical scenarios such as abdominal, cardiac, and prostate imaging. In the absence of fully sampled acquisitions, which can serve as ground truth data, training deep learning algorithms in a supervised manner to predict the underlying ground truth image becomes an impossible task. To address this limitation, self-supervised methods have emerged as a viable alternative, leveraging available subsampled k-space data to train deep learning networks for MRI reconstruction. Nevertheless, these self-supervised approaches often fall short when compared to supervised methodologies. In this paper, we introduce JSSL (Joint Supervised and Self-supervised Learning), a novel training approach for deep learning-based MRI reconstruction algorithms aimed at enhancing reconstruction quality in scenarios where target dataset(s) containing fully sampled k-space measurements are unavailable. Our proposed method operates by simultaneously training a model in a self-supervised learning setting, using subsampled data from the target dataset(s), and in a supervised learning manner, utilizing data from other datasets, referred to as proxy datasets, where fully sampled k-space data is accessible. To demonstrate the efficacy of JSSL, we utilized subsampled prostate parallel MRI measurements as the target dataset, while employing fully sampled brain and knee k-space acquisitions as proxy datasets. Our results showcase a substantial improvement over conventional self-supervised training methods, thereby underscoring the effectiveness of our joint approach. We provide a theoretical motivation for JSSL and establish a practical "rule-of-thumb" for selecting the most appropriate training approach for deep MRI reconstruction.

翻译：摘要：磁共振成像是一种重要的诊断手段，但其固有的缓慢采集过程在腹部、心脏和前列腺成像等临床场景中，获取完全采样的k空间数据面临挑战。当全采样数据（可作为真实标签）不可用时，通过监督学习方式训练深度学习算法以预测潜在的真实图像便成为一项不可能的任务。为克服这一限制，自监督方法作为一种可行的替代方案应运而生，它利用可用的欠采样k空间数据训练深度学习网络进行MRI重建。然而，这些自监督方法的性能往往不及监督学习方法。本文提出JSSL（联合监督与自监督学习），这是一种针对基于深度学习的MRI重建算法的新型训练方法，旨在当目标数据集（包含全采样k空间测量数据）不可用时提升重建质量。该方法通过同时进行两种训练实现：一方面利用目标数据集的欠采样数据进行自监督学习，另一方面利用其他可获取全采样k空间数据的数据集（称为代理数据集）进行监督学习。为验证JSSL的有效性，我们以欠采样前列腺并行MRI测量数据作为目标数据集，同时使用全采样的脑部和膝盖k空间采集数据作为代理数据集。结果表明，与传统自监督训练方法相比，本方法取得了显著改进，凸显了联合方法的有效性。我们还为JSSL提供了理论依据，并建立了选择深度MRI重建最优训练方案的实用经验法则。