Evidence map›Paper›PMID 38153622›Full record

ArticleRadiological physics and technology2024

Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom.

Tatsuya Hayashi, Shinya Kojima, Toshimune Ito, Norio Hayashi, Hiroshi Kondo, Asako Yamamoto, Hiroshi Oba

Abstract read
PubMed Publisher
In one paragraph

Article in Radiological physics and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. 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

7 authors.

Tatsuya HayashiGraduate School of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan. t-hayashi@med.teikyo-u.ac.jp.ORCID http://orcid.org/0000-0001-5269-4081
Shinya KojimaGraduate School of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan.ORCID http://orcid.org/0000-0001-6199-9784
Toshimune ItoGraduate School of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan.ORCID http://orcid.org/0000-0001-5613-2801
Norio HayashiDepartment of Radiological Technology, Gunma Prefectural College of Health Sciences, 323-1 Kamiokimachi, Maebashi, Gunma, 371-0052, Japan.
Hiroshi KondoDepartment of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan.
Asako YamamotoDepartment of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan.ORCID http://orcid.org/0000-0003-4500-0269
Hiroshi ObaDepartment of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan.

Funding

Japan Society for the Promotion of Science 23K07142
6 · The paper itself

Abstract

This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ADC = 1.1 × 10

Indexed as

Deep LearningWaterDiffusion Magnetic Resonance ImagingIceMagnetic Resonance ImagingReproducibility of ResultsIceWaterApparent diffusion coefficientDeep learning reconstructionDiffusion-weighted imagingIce water phantom

Identifiers

PMID38153622

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.