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Abstract #0391

Advancing Self-Supervised Learning for Highly Accelerated MRI Reconstruction Through Parallel Imaging Consistency

Yasar Utku Alcalar1,2, Chi Zhang1,2, and Mehmet Akçakaya1,2
1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Accelerated imaging, artificial intelligence, machine learning, self-supervised learning

Motivation: To improve training for self-supervised deep learning (DL) reconstruction in highly accelerated acquisition scenarios.

Goal(s): We present a self-supervised approach that assesses the quality in both k-space and image domain, drawing on consistency ideas from parallel imaging.

Approach: Parallel imaging consistency is achieved through carefully crafted perturbations for R-fold acceleration, designed to be restorable with parallel imaging reconstruction. Outputs for both perturbed and unperturbed inputs are analyzed and used in conjunction with k-space masking.

Results: Proposed method achieves significant aliasing reduction at R=6 and R=8, outperforming state-of-the-art self-supervised methods on fastMRI dataset.

Impact: This work proposes an improved training strategy for self-supervised MRI reconstruction by applying well-designed perturbations to input images. This ensures alignment with parallel imaging techniques and reduces aliasing artifacts, achieving visible improvements at high acceleration rates.

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Keywords