ArticleMagma (New York, N.Y.)2024
Improved quantitative parameter estimation for prostate T
Article in Magma (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Utilizing 3D fast spin echo anatomical imaging to reduce the number of contrast preparations inMagnetic resonance in medicine · 2025Article
- Rician Likelihood Loss for Quantitative MRI With Self-Supervised Deep Learning.NMR in biomedicine · 2025Article
- Improved quantitative parameter estimation for prostate TMagma (New York, N.Y.) · 2024Article
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
Abstract
objectiveQuantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T MATERIALS AND
methodsLarge physics-based synthetic datasets simulating T
resultsIn the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T DISCUSSION: This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T
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.