JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction

• URL: http://arxiv.org/abs/2311.15856v1
• Date: Mon, 27 Nov 2023 14:23:36 GMT
• Title: JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction
• Authors: George Yiasemis, Nikita Moriakov, Clara I. S\'anchez, Jan-Jakob Sonke, Jonas Teuwen
• Abstract summary: JSSL (Joint Supervised and Self-supervised Learning) is a novel training approach for deep learning-based MRI reconstruction algorithms. Our results showcase a substantial improvement over conventional self-supervised training methods.
• Score: 7.043932618116216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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