Evidence map›Paper›PMID 36270417›Full record

ArticleMagnetic resonance imaging2023

Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network.

Chang Gao, Vahid Ghodrati, Shu-Fu Shih, Holden H Wu, Yongkai Liu, Marcel Dominik Nickel, Thomas Vahle, Brian Dale, Victor Sai, Ely Felker and 5 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 28% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

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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

15 authors at 5 institutions in 4 countries.

Chang GaoDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States.
Vahid GhodratiDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States.
Shu-Fu ShihDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States.
Holden H WuDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States.
Yongkai LiuDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States.
Marcel Dominik NickelMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
Thomas VahleMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
Brian DaleMR R&D Collaborations, Siemens Medical Solutions USA, Inc., Cary, NC, United States.
Victor SaiDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States.
Ely FelkerDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States.
Chuthaporn SurawechDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Radiology, Division of Diagnostic Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
Qi MiaoDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China.
J Paul FinnDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States.
Xiaodong ZhongMR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States.
Peng HuDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States. Electronic address: peng.hu@gmail.com.
University of California, Los Angeles · USSiemens Healthcare (Germany) · DESiemens Healthcare (United States) · USChulalongkorn University · THFirst Hospital of China Medical University · CN

Funding

Expanding on a new paradigm for MRI in pediatric congenital heart diseaseR01HL127153 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHRISTODOULOU, ANTHONY G, FINN, JOHN PAUL · 2015 to 2024
$6.1M
NHLBI NIH HHS R01 HL127153
6 · The paper itself

Abstract

purposeStack-of-radial MRI allows free-breathing abdominal scans, however, it requires relatively long acquisition time. Undersampling reduces scan time but can cause streaking artifacts and degrade image quality. This study developed deep learning networks with adversarial loss and evaluated the performance of reducing streaking artifacts and preserving perceptual image sharpness.

methodsA 3D generative adversarial network (GAN) was developed for reducing streaking artifacts in stack-of-radial abdominal scans. Training and validation datasets were self-gated to 5 respiratory states to reduce motion artifacts and to effectively augment the data. The network used a combination of three loss functions to constrain the anatomy and preserve image quality: adversarial loss, mean-squared-error loss and structural similarity index loss. The performance of the network was investigated for 3-5 times undersampled data from 2 institutions. The performance of the GAN for 5 times accelerated images was compared with a 3D U-Net and evaluated using quantitative NMSE, SSIM and region of interest (ROI) measurements as well as qualitative scores of radiologists.

resultsThe 3D GAN showed similar NMSE (0.0657 vs. 0.0559, p = 0.5217) and significantly higher SSIM (0.841 vs. 0.798, p < 0.0001) compared to U-Net. ROI analysis showed GAN removed streaks in both the background air and the tissue and was not significantly different from the reference mean and variations. Radiologists' scores showed GAN had a significant improvement of 1.6 point (p = 0.004) on a 4-point scale in streaking score while no significant difference in sharpness score compared to the input.

conclusion3D GAN removes streaking artifacts and preserves perceptual image details.

Indexed as

ArtifactsMagnetic Resonance ImagingImage Processing, Computer-AssistedMotionRespirationAbdominal MRIArtifact suppressionDeep adversarial networkDeep learningRadial acquisition

Identifiers

PMID36270417
PMCPMC10163826
OpenAlexW4306709684

What OpenQuestion holds

Textmetadata
LicenceTDM
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.