JSSL: Joint Supervised and Self-supervised Learning for MRI
  Reconstruction
George Yiasemis Nikita Moriakov Clara I. Sánchez Jan-Jakob Sonke Jonas Teuwen
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.