Keywords: MR Fingerprinting, MR Fingerprinting, Deep learning, Neuro, Reconstruction
Motivation: Magnetic Resonance Fingerprinting (MRF) is a powerful quantitative imaging technique, but noise significantly impacts tissue property estimation accuracy.
Goal(s): This study aims to enhance denoising methods for MRF, addressing the critical need for improved image quality in clinical applications.
Approach: We developed a denoising process that integrates randomized SVD projections from MRF data into a deep learning model and utilizes inner products to match the denoised data with a standard MRF dictionary for property estimation.
Results: Our method achieved substantial noise reduction, with quantitative metrics showing improved inner product measures compared to traditional techniques.
Impact: This study provides a novel approach to enhance MRF through advanced denoising techniques, potentially improving diagnostic accuracy and clinical outcomes in quantitative magnetic resonance imaging.
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