ArticleMagnetic resonance in medicine2026
ASL 4D MRA Intracranial Vessel Segmentation With Deep Learning U-Nets.
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. Cited by 1 paper.
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
1 citing paper in PubMed.
- ASL 4D MRA Intracranial Vessel Segmentation With Deep Learning U-Nets.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
8 authors.
Funding
Abstract
purposeTo propose a spatio-temporal U-Net based network (4DST) that exploits both spatial and dynamic information while avoiding memory-intensive 4D convolutional layers for ASL-based non-contrast enhanced 4-dimensional MR angiography (4D MRA) vessel segmentation.
methodsPulsed ASL-based 4D MRA data were collected on 35 healthy volunteers and 5 arteriovenous malformation patients. Spatial only (2D, 3D) and spatio-temporal U-Net variations (including the proposed 4DST) were tested. Two recently developed methods, including feature-based isolation forest and BRAVE-Net, were used for comparison. Dice-Sørensen coefficient (DSC), center-line Dice (clDice), Hausdorff distance (HD), precision, accuracy, specificity, and sensitivity were calculated. Sensitivity was analyzed relative to SNR and arterial transit time (ATT) to explore detectability. From graph analysis, total vessel length, number of branches, and number of endpoints were reported.
results4DST achieved the best DSC, clDice, and HD (0.876 ± 0.03, 0.865 ± 0.02, 6.241 ± 0.95, respectively). 4DST outperformed all other models across the SNR range of 1 to 10 and arterial transit time range of 500 to 800 ms in sensitivity. Last, the 4DST segmentations yielded total lengths and the number of branch splits that more closely matched the ground truths compared to the other models.
conclusionThe proposed 4DST network architecture offers an overall improvement in 4D MRA vessel segmentation performance over the compared methods and provides the framework for an end-to-end trainable model for spatio-temporal datasets. Additionally, 4DST requires minimal pre/post-processing steps, rendering it an attractive solution for pulsed ASL-based 4D MRA vessel segmentation.
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.