Keywords: Analysis/Processing, Low-Field MRI, AI, Test-Time Training, Self-Supervised Learning, In-Vivo, Brain Imaging
Motivation: Low-field MRI, while portable and cost-effective, suffers from low SNR, limiting its clinical use.
Goal(s): To investigate self-supervised test-time training as a fine-tuning method for low-field MRI denoising models, particularly for in-vivo data with different contrasts.
Approach: A denoising model was pretrained with simulated low-field T2-weighted MRI data and further refined using self-supervised test-time training on in-vivo data. Model performance was assessed with and without test-time training across T2- and PD-weighted data.
Results: The proposed fine-tuning by self-supervised test-time training provides the best compromise between denoising performance and preservation of structural details.
Impact: Noise-free reference data for supervised training of low-field MRI denoising models does not exist. Using supervised pretraining on simulated data combined with self-supervised test-time training narrows the performance gap in low-field MRI denoising models when training and testing data differs.
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