Evidence map›Paper›PMID 38653786›Full record

ArticleSkeletal radiology2024

Improved Resolution and Image Quality of Musculoskeletal Magnetic Resonance Imaging using Deep Learning-based Denoising Reconstruction: A Prospective Clinical Study.

Hung P Do, Carly A Lockard, Dawn Berkeley, Brian Tymkiw, Nathan Dulude, Scott Tashman, Garry Gold, Jordan Gross, Erin Kelly, Charles P Ho

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Article in Skeletal radiology, 2024. 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
–field-weighted citation impact
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.

  1. Trial
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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

10 authors.

Hung P DoCanon Medical Systems USA, Inc., 2441 Michelle Drive, Tustin, CA, 92780, USA. hdo@us.medical.canon.ORCID http://orcid.org/0000-0002-8141-8630
Carly A LockardSteadman Philippon Research Institute, 181 West Meadow Dr, Vail, CO, 81657, USA.
Dawn BerkeleyCanon Medical Systems USA, Inc., 2441 Michelle Drive, Tustin, CA, 92780, USA.
Brian TymkiwCanon Medical Systems USA, Inc., 2441 Michelle Drive, Tustin, CA, 92780, USA.
Nathan DuludeThe Steadman Clinic, 181 West Meadow Drive, Suite 400, Vail, CO, 81657, USA.
Scott TashmanSteadman Philippon Research Institute, 181 West Meadow Dr, Vail, CO, 81657, USA.
Garry GoldStanford University, 450 Jane Stanford Way, Stanford, CA, 94305-2004, USA.
Jordan GrossUniversity of Southern California, 3551 Trousdale Pkwy, Los Angeles, CA, 90089, USA.
Erin KellyCanon Medical Systems USA, Inc., 2441 Michelle Drive, Tustin, CA, 92780, USA.
Charles P HoSteadman Philippon Research Institute, 181 West Meadow Dr, Vail, CO, 81657, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo prospectively evaluate a deep learning-based denoising reconstruction (DLR) for improved resolution and image quality in musculoskeletal (MSK) magnetic resonance imaging (MRI).

methodsImages from 137 contrast-weighted sequences in 40 MSK patients were evaluated. Each sequence was performed twice, first with the routine parameters and reconstructed with a routine reconstruction filter (REF), then with higher resolution and reconstructed with DLR, and with three conventional reconstruction filters (NL2, GA43, GA53). The five reconstructions (REF, DLR, NL2, GA43, and GA53) were de-identified, randomized, and blindly reviewed by three MSK radiologists using eight scoring criteria and a forced ranking. Quantitative SNR, CNR, and structure's full width at half maximum (FWHM) for resolution assessment were measured and compared. To account for repeated measures, Generalized Estimating Equations (GEE) with Bonferroni adjustment was used to compare the reader's scores, SNR, CNR, and FWHM between DLR vs. NL2, GA43, GA53, and REF.

resultsCompared to the routine REF images, the resolution was improved by 47.61% with DLR from 0.39 ± 0.15 mm

conclusionThis prospective clinical evaluation demonstrated that DLR allows approximately two times finer resolution and improved image quality compared to the standard-of-care images.

Indexed as

Deep LearningMagnetic Resonance ImagingAdolescentAdultAgedContrast MediaFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedMusculoskeletal DiseasesProspective StudiesSignal-To-Noise RatioContrast MediaHuman observer studyImage quality assessmentMRI deep learning reconstructionProspective clinical evaluation

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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.