Zero-Shot Self-Supervised Learning for MRI Reconstruction

• URL: http://arxiv.org/abs/2102.07737v4
• Date: Wed, 29 Nov 2023 03:43:13 GMT
• Title: Zero-Shot Self-Supervised Learning for MRI Reconstruction
• Authors: Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Ak\c{c}akaya
• Abstract summary: We propose a zero-shot self-supervised learning approach to perform subject-specific accelerated DL MRI reconstruction. The proposed approach partitions the available measurements from a single scan into three disjoint sets. In the presence of models pre-trained on a database with different image characteristics, we show that the proposed approach can be combined with transfer learning for faster convergence time and reduced computational complexity.
• Score: 4.542616945567623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but often necessitates a database of fully-sampled measurements for training. Recent self-supervised and unsupervised learning approaches enable training without fully-sampled data. However, a database of undersampled measurements may not be available in many scenarios, especially for scans involving contrast or translational acquisitions in development. Moreover, recent studies show that database-trained models may not generalize well when the unseen measurements differ in terms of sampling pattern, acceleration rate, SNR, image contrast, and anatomy. Such challenges necessitate a new methodology to enable subject-specific DL MRI reconstruction without external training datasets, since it is clinically imperative to provide high-quality reconstructions that can be used to identify lesions/disease for \emph{every individual}. In this work, we propose a zero-shot self-supervised learning approach to perform subject-specific accelerated DL MRI reconstruction to tackle these issues. The proposed approach partitions the available measurements from a single scan into three disjoint sets. Two of these sets are used to enforce data consistency and define loss during training for self-supervision, while the last set serves to self-validate, establishing an early stopping criterion. In the presence of models pre-trained on a database with different image characteristics, we show that the proposed approach can be combined with transfer learning for faster convergence time and reduced computational complexity. The code is available at \url{https://github.com/byaman14/ZS-SSL}.

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