Evidence map›Paper›PMID 29351284›Full record

ArticlePloS one2018

Magnetic resonance angiography with compressed sensing: An evaluation of moyamoya disease.

Takayuki Yamamoto, Tomohisa Okada, Yasutaka Fushimi, Akira Yamamoto, Koji Fujimoto, Sachi Okuchi, Hikaru Fukutomi, Jun C Takahashi, Takeshi Funaki, Susumu Miyamoto and 4 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
5.1field-weighted citation impact, top 5% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 49 citations in OpenAlex.

  1. Article
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  9. Vessel wall MR imaging in neuroradiology.La Radiologia medica · 2022
    Review
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  11. Article
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  20. Hybrid of Compressed Sensing and Parallel Imaging Applied to Three-dimensional Isotropic TMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 4 institutions in 4 countries.

Takayuki YamamotoDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID 0000-0003-0306-3691
Tomohisa OkadaDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yasutaka FushimiDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Akira YamamotoDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Koji FujimotoDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Sachi OkuchiDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Hikaru FukutomiDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Jun C TakahashiDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Takeshi FunakiDepartment of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Susumu MiyamotoDepartment of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Aurélien F StalderSiemens ltd. China, Shanghai, China.
Yutaka NatsuakiSiemens Medical Solutions USA, Inc., Huntington Beach, California, United States of America.
Peter SpeierSiemens Healthcare, Erlangen, Germany.
Kaori TogashiDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Kyoto University · JPSiemens (China) · CNSiemens Healthcare (United States) · USSiemens Healthineers (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compressed sensing (CS) reconstructions of under-sampled measurements generate missing data based on assumptions of image sparsity. Non-contrast time-of-flight MR angiography (TOF-MRA) is a good candidate for CS based acceleration, as MRA images feature bright trees of sparse vessels over a well-suppressed anatomical background signal. A short scan time derived from CS is beneficial for patients of moyamoya disease (MMD) because of the frequency of MR scans. The purpose of this study was to investigate the reliability of TOF-MRA with CS in the evaluation of MMD. Twenty-two patients were examined using TOF-MRA with CS (CS-TOF) and parallel imaging (PI-TOF). The acceleration factors were 3 (CS3) and 5 (CS5) for CS-TOF, and 3 (PI3) for PI-TOF. Two neuroradiologists evaluated the MMD grading according to stenosis/occlusion scores using the modified Houkin's system, and the visibility of moyamoya vessels (MMVs) using a 3-point scale. Concordance was calculated with Cohen's κ. The numbers of MMVs in the basal ganglia were compared using Bland-Altman analysis and Wilcoxon's signed-rank tests. MRA scan times were 4:07, 3:53, and 2:42 for PI3, CS3, and CS5, respectively. CS-reconstruction completed within 10 minutes. MMD grading and MMV visibility scales showed excellent correlation (κ > .966). Although the number of MMVs was significantly higher in CS3 than in PI3 (p < .0001) and CS5 (p < .0001), Bland-Altman analysis showed a good agreement between PI3, CS3, and CS5. Compressed sensing can accelerate TOF-MRA with improved visualization of small collaterals in equivalent time (CS3) or equivalent results in a shorter scan time (CS5).

Indexed as

AdultAgedFemaleHumansMagnetic Resonance AngiographyMaleMiddle AgedMoyamoya Disease

Identifiers

PMID29351284
PMCPMC5774704
OpenAlexW2792971003

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.