ArticleMagnetic resonance in medicine2026
3D and 4D Free-Breathing Abdominal T1-Weighted MRI in Clinical Practice Using Deep Learning Auto-Navigation and Reconstruction.
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
purposeTo develop and evaluate an automated clinical prototype for a 1-min free-breathing T1-weighted 3D MRI and a 2.25-min 4D MRI utilizing radial k-space acquisition and deep learning (DL) auto-navigation and reconstruction.
methodsThe clinical prototype was deployed on 3 T GE Healthcare scanners, using the GE DISCO-Star (radial golden-angle stack-of-stars) pulse sequence, DL auto-navigation (RANGR), DL reconstruction (Movienet), and vendor-specific image processing to efficiently generate DICOM files. The system automatically collects raw data, transmits the data to an external high-performance computer, performs image reconstruction, and generates DICOM files. A customized version of the Movienet network was trained, using compressed sensing references, to achieve 2.25-fold and 2-fold acquisition acceleration for 3D and 4D MRI, respectively. The prototype was evaluated on 50 patients (ages: 8-87) with TE = 1.46-1.6 ms, TR = 3.2-3.4 ms, flip angle = 12°, in-plane resolution = 1.17-1.64 mm, and slice thickness = 4 mm. Image quality was assessed qualitatively by three expert radiologists, who compared Movienet to conventional vendor methods, followed by a statistical analysis using Wilcoxon signed-rank tests.
resultsThe Movienet prototype demonstrated remarkable efficiency, requiring only 90 s of GPU and 4 min on a CPU computation for 3D reconstruction, while exhibiting better performance, characterized by reduced streaking artifacts and about one-point improvement in image quality on a five-point scale compared to vendor technology. For 4D reconstructions, reconstruction time increased by 20 s (GPU, +1 min CPU) while maintaining comparable quality metrics. For all metrics, the differences were statistically significant (all p-values < 0.0001).
conclusionThe Movienet prototype significantly enhances motion robustness and acquisition speed in abdominal MRI, offering a transformative approach for clinical applications.
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.