ArticleMagnetic resonance imaging2023
Intracranial arterial flow velocity mapping in quantitative time-of-flight MR angiography using deep machine learning.
Article in Magnetic resonance imaging, 2023. 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, 3 citations in OpenAlex.
- Article
- Estimation of Blood Velocity from TOF-MRA Arterial Centerlines: Theory, Simulation, and Inverse Solution.Bioengineering (Basel, Switzerland) · 2026Article
- Simultaneous Luminal and Hemodynamic Evaluation of the Cervical Arteries Using Nonenhanced 3D Quantitative Quiescent-Interval Slice-Selective Magnetic Resonance Angiography.Journal of magnetic resonance imaging : JMRI · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo evaluate the application of deep machine learning (DML) to 3D quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) to measure blood flow velocity within the intracranial arteries. MATERIALS AND
methodsIntracranial qTOF MRA was acquired in 15 subjects at 3 T. Blood flow velocity quantitation with qTOF MRA was done using a non-DML computer-vision procedure, and using convolutional DML neural networks. 3D phase contrast (PC) MRA was used as the comparator. Using PC velocity measures as the output target and qTOF two-echo source image data as inputs, DML neural networks were trained to predict component blood flow velocities. Total velocities and peak intracranial arterial blood flow velocities were computed from component velocities.
resultsCompared to non-DML image analysis, DML-based analysis of qTOF MRA image data improved agreement with PC for mean component velocity (intraclass correlation coefficient (ICC) = 0.966 versus 0.939), mean total velocity (ICC = 0.835 versus 0.723), and peak velocity (ICC = 0.816 versus 0.597), as well as narrowed the 95% Bland-Altman limits of agreement for mean component velocity ([-5.16, +4.31]cm/s versus [-6.86, +6.53]cm/s), mean total velocity ([-6.78,+3.59]cm/s versus [-9.39, +7.09]cm/s) and peak velocity ([-13.5,+10.2]cm/s versus [-21.3, +10.2]cm/s). Compared to non-DML analysis, DML image analysis reduced the root-mean-square deviation from PC velocity measures by 28%-36%, and shortened calculation times by 35-fold.
conclusionThe application of DML image analysis to intracranial qTOF MRA for velocity quantitation markedly shortened calculation times, substantially improved the agreement of component, total, and peak arterial blood flow velocities, and provided excellent agreement of hemodynamic measures with respect to 3D PC.
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.