Evidence map›Paper›PMID 36733222›Full record

ArticleJournal of magnetic resonance imaging : JMRI2023

DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI.

Ozan Genc, Melanie A Morrison, Javier E Villanueva-Meyer, Brian Burns, Christopher P Hess, Suchandrima Banerjee, Janine M Lupo

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

Article in Journal of magnetic resonance imaging : JMRI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
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

7 authors at 1 institution in 2 countries.

Ozan GencDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Melanie A MorrisonDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID 0000-0002-5993-0515
Javier E Villanueva-MeyerDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Brian BurnsGE Healthcare, Menlo Park, California, USA.
Christopher P HessDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Suchandrima BanerjeeGE Healthcare, Menlo Park, California, USA.
Janine M LupoDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID 0000-0002-0051-6387
University of California, San Francisco · US

Funding

MICROBLEEDS AS A MARKER OF RADIATION-INDUCED BRAIN INJURY IN PEDIATRIC PATIENTSR01HD079568 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LUPO, JANINE MARIE · 2015 to 2019
$2.5M
NICHD NIH HHS R01 HD079568
6 · The paper itself

Abstract

backgroundAlthough susceptibility-weighted imaging (SWI) is the gold standard for visualizing cerebral microbleeds (CMBs) in the brain, the required phase data are not always available clinically. Having a postprocessing tool for generating SWI contrast from T2*-weighted magnitude images is therefore advantageous. PURPOSE: To create synthetic SWI images from clinical T2*-weighted magnitude images using deep learning and evaluate the resulting images in terms of similarity to conventional SWI images and ability to detect radiation-associated CMBs. STUDY TYPE: Retrospective. POPULATION: A total of 145 adults (87 males/58 females; 43.9 years old) with radiation-associated CMBs were used to train (16,093 patches/121 patients), validate (484 patches/4 patients), and test (2420 patches/20 patients) our networks. FIELD STRENGTH/SEQUENCE: 3D T2*-weighted, gradient-echo acquired at 3 T. ASSESSMENT: Structural similarity index (SSIM), peak signal-to-noise-ratio (PSNR), normalized mean-squared-error (nMSE), CMB counts, and line profiles were compared among magnitude, original SWI, and synthetic SWI images. Three blinded raters (J.E.V.M., M.A.M., B.B. with 8-, 6-, and 4-years of experience, respectively) independently rated and classified test-set images. STATISTICAL TESTS: Kruskall-Wallis and Wilcoxon signed-rank tests were used to compare SSIM, PSNR, nMSE, and CMB counts among magnitude, original SWI, and predicted synthetic SWI images. Intraclass correlation assessed interrater variability. P values <0.005 were considered statistically significant.

resultsSSIM values of the predicted vs. original SWI (0.972, 0.995, 0.9864) were statistically significantly higher than that of the magnitude vs. original SWI (0.970, 0.994, 0.9861) for whole brain, vascular structures, and brain tissue regions, respectively; 67% (19/28) CMBs detected on original SWI images were also detected on the predicted SWI, whereas only 10 (36%) were detected on magnitude images. Overall image quality was similar between the synthetic and original SWI images, with less artifacts on the former.

conclusionsThis study demonstrated that deep learning can increase the susceptibility contrast present in neurovasculature and CMBs on T2*-weighted magnitude images, without residual susceptibility-induced artifacts. This may be useful for more accurately estimating CMB burden from magnitude images alone. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Indexed as

Deep LearningAdultCerebral HemorrhageFemaleHumansMagnetic Resonance ImagingMaleRetrospective StudiesSensitivity and SpecificityBayesian optimizationcerebral microbleedsdeep learninggenerative adversarial networkssusceptibility-weighted imagingsynthetic image generation

Identifiers

PMID36733222
PMCPMC10443940
OpenAlexW4319061767

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.