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

• URL: http://arxiv.org/abs/2311.15856v2
• Date: Tue, 30 Jul 2024 14:49:15 GMT
• Title: JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction
• Authors: George Yiasemis, Nikita Moriakov, Clara I. Sánchez, Jan-Jakob Sonke, Jonas Teuwen,
• Abstract summary: Joint Supervised and Self-supervised Learning (JSSL) is a novel training approach for deep learning-based MRI reconstruction algorithms. JSSL operates by simultaneously training a model in a self-supervised learning setting, using subsampled data from the target dataset. We demonstrate JSSL's efficacy using subsampled prostate or cardiac MRI data as the target datasets.
• Score: 7.018974360061121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: MRI represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully-sampled k-space data under motion. In the absence of fully-sampled acquisitions, serving as ground truths, training deep learning algorithms in a supervised manner to predict the underlying ground truth image becomes challenging. To address this limitation, self-supervised methods have emerged as a viable alternative, leveraging available subsampled k-space data to train deep neural networks for MRI reconstruction. Nevertheless, these approaches often fall short when compared to supervised methods. Methods: We propose Joint Supervised and Self-supervised Learning (JSSL), a novel training approach for deep learning-based MRI reconstruction algorithms aimed at enhancing reconstruction quality in cases where target datasets containing fully-sampled k-space measurements are unavailable. JSSL 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 datasets with fully-sampled k-space data, referred to as proxy datasets. We demonstrate JSSL's efficacy using subsampled prostate or cardiac MRI data as the target datasets, with fully-sampled brain and knee, or brain, knee and prostate k-space acquisitions, respectively, as proxy datasets. Results: Our results showcase substantial improvements over conventional self-supervised methods, validated using common image quality metrics. Furthermore, we provide theoretical motivations for JSSL and establish rule-of-thumb guidelines for training MRI reconstruction models. Conclusion: JSSL effectively enhances MRI reconstruction quality in scenarios where fully-sampled k-space data is not available, leveraging the strengths of supervised learning by incorporating proxy datasets.

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