ArticleMagnetic resonance in medicine2025
Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.
Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Combined ADC and TMagnetic resonance in medicine · 2026Article
- Methods for Uncertainty Quantification in Dictionary Matching to Advance Reliability of Quantitative MRI.Magnetic resonance in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
purposeTo accelerate respiratory triggered free-breathing T2 mapping of the abdomen while maintaining high-quality anatomical images, accurate T2 maps, and fast reconstruction times.
methodsWe developed a flexible deep learning framework that can be trained in a fully supervised manner to improve T2-weighted images or in a self-supervised manner to reconstruct T2 maps.
resultsFor retrospectively undersampled data, anatomical images and T2 maps reconstructed by the proposed deep learning method demonstrated reduced voxel-wise error compared to existing traditional and compressed sensing techniques. Reconstruction times were approximately 1 s per slice, significantly faster than existing compressed sensing techniques. Prospectively undersampled data were also acquired to assess the model.
conclusionThe proposed deep-learning framework reconstructed high-quality anatomical images and accurate T2 maps from datasets undersampled to only 160 total radial views (5 views per echo time), enabling full liver coverage in under three minutes on average with per-slice reconstruction times of approximately one second.
Indexed as
Identifiers
What Socratic 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 Socratic graph.