Evidence map›Paper›PMID 40762171›Full record

ArticleMagnetic resonance in medicine2025

Utilizing 3D fast spin echo anatomical imaging to reduce the number of contrast preparations in

Junru Zhong, Chaoxing Huang, Ziqiang Yu, Fan Xiao, Thierry Blu, Siyue Li, Tim-Yun Michael Ong, Ki-Wai Kevin Ho, Queenie Chan, James F Griffith and 1 more

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2025. 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. Review
  2. Article
  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

11 authors.

Junru ZhongCU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0002-3897-9280
Chaoxing HuangCU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0003-4941-5840
Ziqiang YuCU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0009-0000-2466-4942
Fan XiaoDepartment of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID https://orcid.org/0009-0002-3766-6917
Thierry BluDepartment of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
Siyue LiCU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0001-8791-5841
Tim-Yun Michael OngDepartment of Orthopaedics & Traumatology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0002-4460-9286
Ki-Wai Kevin HoDepartment of Orthopaedics & Traumatology, The Chinese University of Hong Kong Medical Centre, Hong Kong SAR, China.ORCID https://orcid.org/0000-0001-8647-8475
Queenie ChanPhilips Healthcare, Hong Kong, China.ORCID https://orcid.org/0000-0003-1270-995X
James F GriffithCU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0001-5206-9382
Weitian ChenCU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0001-7242-9285

Funding

Innovation and Technology Commission, HKSAR Government MRP/001/18XShanghai Baiyulan Pujiang Talents Program 24PJA100The Yushan Fellow Program, Taiwan
6 · The paper itself

Abstract

purposeTo propose and evaluate an accelerated

methodsThis retrospective study utilized MRI data from 40 participants (30 OA patients and 10 healthy volunteers). A volume of PD-weighted anatomical FSE images and a volume of

resultsThe best-performed deep learning models achieved RPEs below 5% across all evaluated scenarios. This performance was consistent even in reduced acquisition settings that included only one PD-weighted image and one

conclusionThe proposed approach enables efficient

Indexed as

Cartilage, ArticularImaging, Three-DimensionalKnee JointMagnetic Resonance ImagingOsteoarthritis, KneeAdultAgedAlgorithmsContrast MediaDeep LearningFemaleHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMaleMiddle AgedContrast Mediadeep learningosteoarthritis

Identifiers

PMID40762171
PMCPMC12501725

What OpenQuestion holds

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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.