ArticleMagnetic resonance in medicine2022
Cramér-Rao bound-informed training of neural networks for quantitative MRI.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- MC BTS: Simultaneously Resolving Magnetization Transfer Effect and Relaxation for Multiple Components.Magnetic resonance in medicine · 2026Article
- Optimization of Pulsed Saturation Transfer MR Fingerprinting (ST MRF) Acquisition Using the Cramér-Rao Bound and Sequential Quadratic Programming.Magnetic resonance in medicine · 2026Article
- Contrast-optimized basis functions for self-navigated motion correction in quantitative MRI.Magnetic resonance in medicine · 2026Article
- Utilizing 3D fast spin echo anatomical imaging to reduce the number of contrast preparations inMagnetic resonance in medicine · 2025Article
- Magnetization transfer explains most of the TMagnetic resonance in medicine · 2025Article
- Fine-Tuning Deep Learning Model for Quantitative Knee Joint Mapping With MR Fingerprinting and Its Comparison to Dictionary Matching Method: Fine-Tuning Deep Learning Model for Quantitative MRF.NMR in biomedicine · 2025Article
- Magnetization transfer explains most of theArXiv · 2025Article
- Acceleration of Simultaneous Multislice Magnetic Resonance Fingerprinting With Spatiotemporal Convolutional Neural Network.NMR in biomedicine · 2025Article
- Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.IEEE transactions on bio-medical engineering · 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
- Article
- Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model.Magnetic resonance in medicine · 2024Article
- Article
- Minimization of eddy current artifacts in sequences with periodic dynamics.Magnetic resonance in medicine · 2024Article
- 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
- Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Frontiers in genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeTo improve the performance of neural networks for parameter estimation in quantitative MRI, in particular when the noise propagation varies throughout the space of biophysical parameters. THEORY AND
methodsA theoretically well-founded loss function is proposed that normalizes the squared error of each estimate with respective Cramér-Rao bound (CRB)-a theoretical lower bound for the variance of an unbiased estimator. This avoids a dominance of hard-to-estimate parameters and areas in parameter space, which are often of little interest. The normalization with corresponding CRB balances the large errors of fundamentally more noisy estimates and the small errors of fundamentally less noisy estimates, allowing the network to better learn to estimate the latter. Further, proposed loss function provides an absolute evaluation metric for performance: A network has an average loss of 1 if it is a maximally efficient unbiased estimator, which can be considered the ideal performance. The performance gain with proposed loss function is demonstrated at the example of an eight-parameter magnetization transfer model that is fitted to phantom and in vivo data.
resultsNetworks trained with proposed loss function perform close to optimal, that is, their loss converges to approximately 1, and their performance is superior to networks trained with the standard mean-squared error (MSE). The proposed loss function reduces the bias of the estimates compared to the MSE loss, and improves the match of the noise variance to the CRB. This performance gain translates to in vivo maps that align better with the literature.
conclusionNormalizing the squared error with the CRB during the training of neural networks improves their performance in estimating biophysical parameters.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.