Keywords: Analysis/Processing, Analysis/Processing, Self-supervised Denoising
Motivation: Multicontrast MRI offers unique capabilities for diagnosis and tissue characterization, but it often has more limited trade-offs in speed/resolution/SNR, especially in low-field MRI.
Goal(s): We developed a self-supervised denoising method for multicontrast MRI without requiring clean/high-SNR labels.
Approach: An ambient denoising score matching based approach is proposed to denoise the target contrast image by effectively leveraging both its noisy counterparts and noisy images of other contrasts.
Results: The proposed method effectively enhanced SNR while preserving fine details, achieving state-of-the-art performance on the M4Raw dataset for different target contrasts.
Impact: Our method represents a new approach for self-supervised multicontrast MRI denoising. It may offer better trade-offs in SNR, resolution, and speed to benefit many low-field applications.
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