Evidence map›Paper›PMID 36927877›Full record

ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2024

Thin-slice Two-dimensional T2-weighted Imaging with Deep Learning-based Reconstruction: Improved Lesion Detection in the Brain of Patients with Multiple Sclerosis.

Masatoshi Iwamura, Satoru Ide, Kenya Sato, Akihisa Kakuta, Soichiro Tatsuo, Atsushi Nozaki, Tetsuya Wakayama, Tatsuya Ueno, Rie Haga, Misako Kakizaki and 6 more

Open access · diamondAbstract read
In one paragraph

Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
6.4field-weighted citation impact, top 3% 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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

  1. Practical Brain MRI Guidelines for Anti-Aβ Antibody Treatment in Early Symptomatic Alzheimer's Disease.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
    Guideline
  2. Guideline
  3. Review
  4. Visualization of the Trochlear Nerve Using Deep Learning-enhanced 3D T2-weighted MR Imaging at 3T.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Article
  5. 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
  6. Enhanced Visualization of Intracranial Cortical Arteries Using Deep Learning Reconstruction in Vessel Wall MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Article
  7. Article
  8. Improved Assessment of Juxtacortical Lesions in Multiple Sclerosis Using Highly-accelerated High-resolution Double Inversion Recovery MR Imaging with Deep Learning-based Reconstruction.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
    Article
  9. Article
  10. Review
  11. Thin-slice TInsights into imaging · 2025
    Article
  12. Review
  13. Review
  14. Article
  15. Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024
    Review
  16. Article
  17. Article
  18. Review
  19. 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

16 authors at 4 institutions in 2 countries.

Masatoshi IwamuraDepartment of Radiology, Hirosaki University Graduate School of Medicine.
Satoru IdeDepartment of Radiology, University of Occupational and Environmental Health, School of Medicine.
Kenya SatoDepartment of Radiology, Aomori Prefectural Central Hospital.
Akihisa KakutaDepartment of Radiology, Aomori Prefectural Central Hospital.
Soichiro TatsuoDepartment of Radiology, Hirosaki University Graduate School of Medicine.
Atsushi NozakiMR Application and Workflow, GE Healthcare.
Tetsuya WakayamaMR Application and Workflow, GE Healthcare.
Tatsuya UenoDepartment of Neurology, Aomori Prefectural Central Hospital.
Rie HagaDepartment of Neurology, Aomori Prefectural Central Hospital.
Misako KakizakiDepartment of Radiology, Aomori Prefectural Central Hospital.
Yoko YokoyamaDepartment of Radiology, Aomori Prefectural Central Hospital.
Ryoichi YamauchiDepartment of Radiology, Aomori Prefectural Central Hospital.
Fumiyasu TsushimaDepartment of Radiology, Hirosaki University Graduate School of Medicine.
Koichi ShibutaniDepartment of Radiology, Aomori Prefectural Central Hospital.
Masahiko TomiyamaDepartment of Neurology, Hirosaki University Graduate School of Medicine.
Shingo KakedaDepartment of Radiology, Hirosaki University Graduate School of Medicine.
Aomori Prefectural Central Hospital · JPHirosaki University · JPGeneral Electric (Spain) · ESUniversity of Occupational and Environmental Health Japan · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBrain MRI with high spatial resolution allows for a more detailed delineation of multiple sclerosis (MS) lesions. The recently developed deep learning-based reconstruction (DLR) technique enables image denoising with sharp edges and reduced artifacts, which improves the image quality of thin-slice 2D MRI. We, therefore, assessed the diagnostic value of 1 mm-slice-thickness 2D T2-weighted imaging (T2WI) with DLR (1 mm T2WI with DLR) compared with conventional MRI for identifying MS lesions.

methodsConventional MRI (5 mm T2WI, 2D and 3D fluid-attenuated inversion recovery) and 1 mm T2WI with DLR (imaging time: 7 minutes) were performed in 42 MS patients. For lesion detection, two neuroradiologists counted the MS lesions in two reading sessions (conventional MRI interpretation with 5 mm T2WI and MRI interpretations with 1 mm T2WI with DLR). The numbers of lesions per region category (cerebral hemisphere, basal ganglia, brain stem, cerebellar hemisphere) were then compared between the two reading sessions.

resultsFor the detection of MS lesions by 2 neuroradiologists, the total number of detected MS lesions was significantly higher for MRI interpretation with 1 mm T2WI with DLR than for conventional MRI interpretation with 5 mm T2WI (765 lesions vs. 870 lesions at radiologist A, < 0.05). In particular, of the 33 lesions in the brain stem, radiologist A detected 21 (63.6%) additional lesions by 1 mm T2WI with DLR.

conclusionUsing the DLR technique, whole-brain 1 mm T2WI can be performed in about 7 minutes, which is feasible for routine clinical practice. MRI with 1 mm T2WI with DLR enabled increased MS lesion detection, particularly in the brain stem.

Indexed as

Deep LearningMultiple SclerosisBrainHumansMagnetic Resonance ImagingNeuroimagingbraindeep learning-based reconstructionmagnetic resonance imagingmultiple sclerosis

Identifiers

PMID36927877
PMCPMC11024714
OpenAlexW4327640181

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.