Evidence map›Paper›PMID 38386108›Full record

ArticleSkeletal radiology2024

Improved 3D DESS MR neurography of the lumbosacral plexus with deep learning and geometric image combination reconstruction.

Yenpo Lin, Ek T Tan, Gracyn Campbell, Philip G Colucci, Sumedha Singh, Ranqing Lan, Yan Wen, Darryl B Sneag

Abstract read
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In one paragraph

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. [The value of MR neuroimaging in image evaluation of facial neuritis].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2025
    Article
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4 · The record

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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

8 authors.

Yenpo LinDepartment of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA.ORCID http://orcid.org/0000-0001-9617-503X
Ek T TanDepartment of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA.
Gracyn CampbellDepartment of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA.
Philip G ColucciDepartment of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA.
Sumedha SinghDepartment of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA.
Ranqing LanBiostatistics Core, Hospital for Special Surgery, New York, NY, USA.
Yan WenGE Healthcare, Waukesha, WI, USA.
Darryl B SneagDepartment of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA. sneagd@hss.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the impact of deep learning (DL) reconstruction in enhancing image quality and nerve conspicuity in LSP MRN using DESS sequences. Additionally, a geometric image combination (GIC) method to improve DESS signals' combination was proposed. MATERIALS AND

methodsAdult patients undergoing 3.0 Tesla LSP MRN with DESS were prospectively enrolled. The 3D DESS echoes were separately reconstructed with and without DL and DL-GIC combined reconstructions. In a subset of patients, 3D T2-weighted short tau inversion recovery (STIR-T

resultsForty patients (22 females; mean age = 48.6 ± 18.5 years) were enrolled. Quantitatively, 'DESS GIC DL' demonstrated superior relative SNR (p < 0.001), while 'DESS S

conclusionApplication of a DL reconstruction with geometric image combination in DESS MRN improves nerve conspicuity of the LSP, especially for its smaller branch nerves.

Indexed as

Deep LearningImaging, Three-DimensionalLumbosacral PlexusMagnetic Resonance ImagingAdultFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedMaleMiddle AgedProspective StudiesDeep LearningLumbosacral PlexusMagnetic Resonance ImagingPeripheral Nerves

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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.