ArticleBMC medical imaging2025
Optimized deep learning-accelerated single-breath-hold abdominal HASTE with and without fat saturation improves and accelerates abdominal imaging at 3 Tesla.
Article in BMC medical imaging, 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.
- 5.0 T versus 1.5 T and 3.0 T upper abdominal MRI: a prospective two‑cohort study of image quality and diagnostic confidence.European radiology · 2026Article
- Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation.Journal of magnetic resonance imaging : JMRI · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning-accelerated single-shot turbo-spin-echo techniques (DL-HASTE) enable single-breath-hold T2-weighted abdominal imaging. However, studies evaluating the image quality of DL-HASTE with and without fat saturation (FS) remain limited. This study aimed to prospectively evaluate the technical feasibility and image quality of abdominal DL-HASTE with and without FS at 3 Tesla. MATERIALS AND
methodsDL-HASTE of the upper abdomen was acquired with variable sequence parameters regarding FS, flip angle (FA) and field of view (FOV) in 10 healthy volunteers and 50 patients. DL-HASTE sequences were compared to clinical sequences (HASTE, HASTE-FS and T2-TSE-FS BLADE). Two radiologists independently assessed the sequences regarding scores of overall image quality, delineation of abdominal organs, artifacts and fat saturation using a Likert scale (range: 1-5).
resultsBreath-hold time of DL-HASTE and DL-HASTE-FS was 21 ± 2 s with fixed FA and 20 ± 2 s with variable FA (p < 0.001), with no overall image quality difference (p > 0.05). DL-HASTE required a 10% larger FOV than DL-HASTE-FS to avoid aliasing artifacts from subcutaneous fat. Both DL-HASTE and DL-HASTE-FS had significantly higher overall image quality scores than standard HASTE acquisitions (DL-HASTE vs. HASTE: 4.8 ± 0.40 vs. 4.1 ± 0.50; DL-HASTE-FS vs. HASTE-FS: 4.6 ± 0.50 vs. 3.6 ± 0.60; p < 0.001). Compared to the T2-TSE-FS BLADE, DL-HASTE-FS provided higher overall image quality (4.6 ± 0.50 vs. 4.3 ± 0.63, p = 0.011). DL-HASTE achieved significant higher image quality (p = 0.006) and higher sharpness score of organs compared to DL-HASTE-FS (p < 0.001).
conclusionDeep learning-accelerated HASTE with and without fat saturation were both feasible at 3 Tesla and showed improved image quality compared to conventional sequences.
trial registrationNot applicable.
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.