Evidence map›Paper›PMID 39007984›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2024

Assessment of image quality and diagnostic accuracy for cervical spondylosis using T2w-STIR sequence with a deep learning-based reconstruction approach.

Qiuying Tao, Kaiyu Wang, Baohong Wen, Yimeng Kang, Jinghan Dang, Jieping Sun, Xiaoyu Niu, Mengzhe Zhang, Zijun Liu, Weijian Wang and 2 more

Abstract read
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In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 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
–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

3 citing papers in PubMed.

  1. Article
  2. Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Review
  3. Article
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

12 authors.

Qiuying TaoDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0000-0002-2207-8576
Kaiyu WangMR Research China, GE Healthcare, Beijing, China.ORCID 0000-0002-9300-7265
Baohong WenDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0000-0003-4566-7565
Yimeng KangDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0000-0002-7598-9066
Jinghan DangDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0009-0001-6460-6533
Jieping SunDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0009-0000-1119-3694
Xiaoyu NiuDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0000-0002-5056-3878
Mengzhe ZhangDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0000-0001-7796-2580
Zijun LiuDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0009-0002-8794-3476
Weijian WangDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.ORCID 0000-0001-9691-6395
Yong ZhangDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China. zzuzhangyong2013@163.com.ORCID 0000-0003-4892-6614
Jingliang ChengDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China. fccchengjl@zzu.edu.cn.ORCID 0000-0002-6996-329X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate potential of enhancing image quality, maintaining interobserver consensus, and elevating disease diagnostic efficacy through the implementation of deep learning-based reconstruction (DLR) processing in 3.0 T cervical spine fast magnetic resonance imaging (MRI) images, compared with conventional images.

methodsThe 3.0 T cervical spine MRI images of 71 volunteers were categorized into two groups: sagittal T2-weighted short T1 inversion recovery without DLR (Sag T2w-STIR) and with DLR (Sag T2w-STIR-DLR). The assessment covered artifacts, perceptual signal-to-noise ratio, clearness of tissue interfaces, fat suppression, overall image quality, and the delineation of spinal cord, vertebrae, discs, dopamine, and joints. Spanning canal stenosis, neural foraminal stenosis, herniated discs, annular fissures, hypertrophy of the ligamentum flavum or vertebral facet joints, and intervertebral disc degeneration were evaluated by three impartial readers.

resultsSag T2w-STIR-DLR images exhibited markedly superior performance across quality indicators (median = 4 or 5) compared to Sag T2w-STIR sequences (median = 3 or 4) (p < 0.001). No statistically significant differences were observed between the two sequences in terms of diagnosis and grading (p > 0.05). The interobserver agreement for Sag T2w-STIR-DLR images (0.604-0.931) was higher than the other (0.545-0.853), Sag T2w-STIR-DLR (0.747-1.000) demonstrated increased concordance between reader 1 and reader 3 in comparison to Sag T2w-STIR (0.508-1.000). Acquisition time diminished from 364 to 197 s through the DLR scheme.

conclusionsOur investigation establishes that 3.0 T fast MRI images subjected to DLR processing present heightened image quality, bolstered diagnostic performance, and reduced scanning durations for cervical spine MRI compared with conventional sequences.

Indexed as

Cervical VertebraeDeep LearningMagnetic Resonance ImagingSpondylosisAdultAgedFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedCervical spineDeep learning-based reconstructionDiagnostic performanceImage qualityMagnetic resonance imaging

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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.