Evidence map›Paper›PMID 38366622›Full record

ArticleThe British journal of radiology2024

Clinical efficacy of motion-insensitive imaging technique with deep learning reconstruction to improve image quality in cervical spine MR imaging.

You Seon Song, In Sook Lee, Moonjung Hwang, Kyoungeun Jang, Xinzeng Wang, Maggie Fung

Open access · bronzeAbstract read
In one paragraph

Article in The British journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
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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

6 authors at 3 institutions in 2 countries.

You Seon SongPusan National University School of Medicine, Busan, Korea.
In Sook LeePusan National University School of Medicine, Busan, Korea.ORCID 0000-0001-7295-600X
Moonjung HwangGE Healthcare, 15F Seoul Square 416, Seoul, Seoul 04367, Korea.
Kyoungeun JangAIRS Medical, 13-14F, Keungil Tower, Seoul, Seoul 06142, Korea.
Xinzeng WangGE Healthcare, MR Clinical Solutions & Research Collaborations, Houston, Texas 77081, United States.
Maggie FungGE Healthcare, MR Clinical Solutions & Research Collaborations, New York, NY 10032, United States.
Clinical Solutions · USPusan National University Hospital · KRSeoul Medical Center · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo demonstrate that a T2 periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique using deep learning reconstruction (DLR) will provide better image quality and decrease image noise.

methodsFrom December 2020 to March 2021, 35 patients examined cervical spine MRI were included in this study. Four sets of images including fast spin echo (FSE), original PROPELLER, PROPELLER DLR50%, and DLR75% were quantitatively and qualitatively reviewed. We calculated the signal-to-noise ratio (SNR) of the spinal cord and sternocleidomastoid (SCM) muscle and the contrast-to-noise ratio (CNR) of the spinal cord by applying region-of-interest at the spinal cord, SCM muscle, and background air. We evaluated image noise with regard to the spinal cord, SCM, and back muscles at each level from C2-3 to C6-7 in the 4 sets.

resultsAt all disc levels, the mean SNR values for the spinal cord and SCM muscles were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE and original PROPELLER images (P < .0083). The mean CNR values of the spinal cord were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE at the C3-4 and 4-5 levels and PROPELLER DLR75% compared to FSE at the C6-7 level (P < .0083). Qualitative analysis of image noise on the spinal cord, SCM, and back muscles showed that PROPELLER DLR50% and PROPELLER DLR75% images showed a significant denoising effect compared to the FSE and original PROPELLER images.

conclusionThe combination of PROPELLER and DLR improved image quality with a high SNR and CNR and reduced noise. ADVANCES IN KNOWLEDGE: Motion-insensitive imaging technique (PROPELLER) increased the image quality compared to conventional FSE images. PROPELLER technique with a DLR reduced image noise and improved image quality.

Indexed as

Deep LearningArtifactsCervical VertebraeHumansImage EnhancementMagnetic Resonance ImagingTreatment Outcomedeep learning reconstructionmagnetic resonance imagingnoise reductionPROPELLER

Identifiers

PMID38366622
PMCPMC11027290
OpenAlexW4391897972

What OpenQuestion holds

Textmetadata
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.