ArticleIEEE transactions on bio-medical engineering2025
Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.
Article in IEEE transactions on bio-medical engineering, 2025. 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.
- Methods for Uncertainty Quantification in Dictionary Matching to Advance Reliability of Quantitative MRI.Magnetic resonance in medicine · 2026Article
- Contrast-optimized basis functions for self-navigated motion correction in quantitative MRI.Magnetic resonance in medicine · 2026Article
- Magnetization transfer explains most of the TMagnetic resonance in medicine · 2025Article
- Magnetization transfer explains most of theArXiv · 2025Article
- Bias-reduced neural networks for parameter estimation in quantitative MRI.Magnetic resonance in medicine · 2024Article
- Sensitivity of unconstrained quantitative magnetization transfer MRI to Amyloid burden in preclinical Alzheimer's disease.medRxiv : the preprint server for health sciences · 2024Article
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- Unconstrained quantitative magnetization transfer imaging: Disentangling TImaging neuroscience (Cambridge, Mass.) · 2024Article
- Sensitivity of unconstrained quantitative magnetization transfer MRI to amyloid burden in preclinical Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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Abstract
Objective: We extend the traditional framework for estimating subspace bases in quantitative MRI that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. Methods: To this end, we introduce an Results: Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with minimal cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications. Conclusion: The proposed method permits subspace reconstruction with a more compact basis, thereby offering significant computational savings. Significance: Efficient subspace reconstruction facilitates the validation and translation of advanced quantitative MRI techniques, e.g., magnetization transfer and diffusion.
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