ArticleMagnetic resonance in medicine2024
Bias-reduced neural networks for parameter estimation in quantitative MRI.
Article in Magnetic resonance in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Quantitative Chemical Exchange Saturation Transfer Imaging With Golden-Angle Radial k-Space Sampling and Locally Low-Rank Reconstruction.NMR in biomedicine · 2026Article
- Contrast-optimized basis functions for self-navigated motion correction in quantitative MRI.Magnetic resonance in medicine · 2026Article
- Quantitative multislice and jointly optimized rapid CEST for in vivo whole-brain imaging.Magnetic resonance in medicine · 2025Article
- Magnetization transfer explains most of the TMagnetic resonance in medicine · 2025Article
- Magnetization transfer explains most of theArXiv · 2025Article
- Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.IEEE transactions on bio-medical engineering · 2025Article
- Sensitivity of unconstrained quantitative magnetization transfer MRI to Amyloid burden in preclinical Alzheimer's disease.medRxiv : the preprint server for health sciences · 2024Article
- Article
- Sensitivity of unconstrained quantitative magnetization transfer MRI to amyloid burden in preclinical Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Unconstrained quantitative magnetization transfer imaging: Disentangling TImaging neuroscience (Cambridge, Mass.) · 2024Article
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Funding
Abstract
purposeTo develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound. THEORY AND
methodsWe generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications.
resultsIn simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least-squares fitting, while state-of-the-art NNs show larger deviations.
conclusionThe proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.
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