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.

该研究提出了一种名为 JSSL（联合监督和自监督学习）的深度学习方法，用于在目标数据集不完全采样的情况下，通过同时利用代理数据集和目标数据集，提高 MRI 重构的质量。实验证明，JSSL 方法相较于传统的自监督训练方法有了明显的改进，从而凸显了该联合方法的有效性。