Evidence map›Paper›PMID 41535020›Full record

ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2026

Short-TR Acquisition Time-of-flight MR Angiography with Deep Learning Reconstruction: Technical Feasibility and Initial Clinical Evaluation in Moyamoya Disease.

Naoyuki Takei, Keita Fujii, Xucheng Zhu, Shohei Inui, Naoya Sakamoto, Yuichi Suzuki, Tetsuya Wakayama, Shiori Amemiya, Osamu Abe

Abstract read
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Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of 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.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Naoyuki TakeiDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0003-1171-8256
Keita FujiiRadiology Center, The University of Tokyo Hospital, Tokyo, Japan.ORCID http://orcid.org/0009-0004-4095-4973
Xucheng ZhuGE HealthCare, Menlo Park, CA, USA.ORCID http://orcid.org/0000-0002-5537-5542
Shohei InuiDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0003-3021-4141
Naoya SakamotoDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0001-8779-0989
Yuichi SuzukiRadiology Center, The University of Tokyo Hospital, Tokyo, Japan.ORCID http://orcid.org/0000-0003-0867-6710
Tetsuya WakayamaGE HealthCare, Hino, Tokyo, Japan.ORCID http://orcid.org/0000-0002-4640-3142
Shiori AmemiyaDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0002-2132-1642
Osamu AbeDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0002-1180-2629

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop and evaluate short-TR acquisition time-of-flight (STRA-TOF) MR angiography (MRA), which combines an optimized STRA with deep learning-based reconstruction to achieve scan-time reduction while maintaining image quality in the visualization of intracranial arteries.

methodsTen healthy volunteers and 3 patients with moyamoya disease were examined using 3D TOF MRA with the clinical moyamoya protocol and 2 STRA-TOF protocols employing 4-slab (STRA4) and 9-slab (STRA9) configurations. STRA-TOF employed a TR of approximately 10 ms with variable-density Poisson-disc sampling and unrolled deep learning reconstruction. Bloch equation simulations validated the theoretical basis for STRA. Quantitative assessment included SNR and contrast-to-noise ratio measurements. Two radiologists independently evaluated image quality using a 3-point scale across 9 vascular territories and overall image quality, with blinded assessment. Statistical analysis was performed using the Friedman test with post hoc Wilcoxon signed-rank tests.

resultsSTRA-TOF achieved approximately a 50% reduction in scan time compared with conventional protocols. Both STRA sequences demonstrated significantly higher SNR and contrast-to-noise ratio than conventional TOF (P < 0.001). Overall image quality scores were higher for STRA4 and STRA9 compared with conventional TOF in both readers. Across the 9 vascular territories, both readers consistently rated STRA sequences equal to or superior to conventional TOF, particularly for distal branches. In the small patient cohort with moyamoya disease (n = 3), STRA-TOF demonstrated feasibility for visualizing complex arterial pathology, including stenotic vessels, collateral circulation, and postsurgical vascular changes.

conclusionSTRA-TOF achieved approximately a 50% reduction in scan time while maintaining or improving image quality compared with conventional 3D TOF MRA. This technique addresses the fundamental scan-time limitations of the conventional method, with potential for significant clinical benefits in terms of patient comfort, workflow efficiency, and improved access to intracranial artery evaluation.

Indexed as

deep learning reconstructionintracranial arteriesmoyamoya diseasescan-time reductiontime-of-flight magnetic resonance angiography

Identifiers

PMID41535020
PMCPMC13219747

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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.