Evidence map›Paper›PMID 41310910›Full record

ArticleMedical physics2025

Paired PET-MRI Deep Learning Model for Translating [

Cheng-Han Tsai, Shao-Yi Huang, Yu-Nong Lin, Han-Wei Wang, Ing-Tsung Hsiao, Kevin T Chen

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Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

6 authors.

Cheng-Han TsaiDepartment of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
Shao-Yi HuangDepartment of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan.
Yu-Nong LinDepartment of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
Han-Wei WangDepartment of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
Ing-Tsung HsiaoDepartment of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan.
Kevin T ChenDepartment of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.

Funding

Ministry of Education, Taiwan R.O.C. NTU-113V1015-4Ministry of Education, Taiwan R.O.C. NTU-114L7872Ministry of Education, Taiwan R.O.C. NTU-114L900703Ministry of Education, Taiwan R.O.C. NTU-114V1015-5National Health Research Institutes NHRI-EX114- 11205ECNational Science and Technology Council 114-2628-E-002-019-MY3
6 · The paper itself

Abstract

backgroundAmyloid PET imaging has been extensively employed in the noninvasive assessment of amyloid-beta accumulation in Alzheimer's disease. Various amyloid radiotracers are commonly used in clinical settings; however, the limited interchangeability among these radiotracers hinders the feasibility of long-term clinical trials and multicenter comparisons. The Centiloid method was proposed for standardization, though providing a single score per image; voxel-wise translation remains a formidable task. PURPOSE: This paper proposes a U-Net model based on a deformable convolution network (DCNv3-based U-Net) for [

methodsThe DCNv3-based U-Net combined the benefits of deformable convolution that captures long-range dependencies with efficient computation and the encoder-decoder architecture with skip connections for local-global feature learning and image synthesis.

resultsThe prediction images presented increased homogeneity to other previous models, closely resembling the texture of [

conclusionsThe DCNv3-based U-Net demonstrated high performance in metrics measurement and statistical analyses for the PET image-to-image translation task. This work also justified the importance of MR images in providing structural information.

Indexed as

AmyloidAmyloid beta-PeptidesAniline CompoundsBenzothiazolesDeep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingMultimodal ImagingPositron-Emission TomographyStilbenesThiazolesAlzheimer DiseaseCarbon RadioisotopesHumans2-(4'-(methylamino)phenyl)-6-hydroxybenzothiazole4-(N-methylamino)-4'-(2-(2-(2-fluoroethoxy)ethoxy)ethoxy)stilbeneAmyloidAmyloid beta-PeptidesAniline CompoundsBenzothiazolesCarbon RadioisotopesStilbenesThiazolesamyloid PETdeformable convolution networkmedical image translation

Identifiers

PMID41310910
PMCPMC12660554

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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.