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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