Evidence map›Paper›PMID 40682663›Full record

ArticleNeuroradiology2025

Cross-modality image-to-image translation from MR to synthetic

Youngbeom Seo, Heesung Yang, Eunjung Kong, Vivek Sanker, Atman Desai, Jungwon Lee, So Hee Park, You Seon Song, Ikchan Jeon

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Article in Neuroradiology, 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

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

1 citing paper in PubMed.

  1. 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

9 authors.

Youngbeom Seo *Department of Neurosurgery, Korea University Ansan Hospital, Ansan, Republic of Korea (South Korea).
Heesung Yang *School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea (South Korea).
Eunjung KongDepartment of Nuclear Medicine, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, Republic of Korea (South Korea).
Vivek SankerDepartment of Neurosurgery, Stanford University, Stanford, CA, USA.
Atman DesaiDepartment of Neurosurgery, Stanford University, Stanford, CA, USA.
Jungwon LeePohang University of Science and Technology, Pohang, Republic of Korea (South Korea).
So Hee ParkDepartment of Neurosurgery, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, Republic of Korea (South Korea).
You Seon SongDepartment of Radiology, Pusan National University School of Medicine, BioMedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea (South Korea).
Ikchan JeonDepartment of Neurosurgery, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, Republic of Korea (South Korea). jicns@hanmail.net.

Funding

National Research Foundation of Korea NRF-2022R1F1A1073230
6 · The paper itself

Abstract

objectiveThis study aims to identify the possibility of cross-modality image-to-image translation from magnetic resonance (MR) to synthetic positron emission tomography (PET)/MR fusion images using conditional generative adversarial networks (CGAN).

methodsRetrospective study was conducted involving 32 simultaneous 6-[

resultsTotal 2167 pairs of T1C and PET/T1C fusion images were obtained, which were randomly assigned to training and test datasets in 9:1 ratio (1950 and 217 pairs), and training data were further divided into training and validation datasets in 4:1 ratio (1560 and 390 pairs). The correlation coefficients were 0.706 (CI:0.533-0.822) for TBR

conclusionCGAN based on simultaneous

Indexed as

Brain NeoplasmsMagnetic Resonance ImagingMultimodal ImagingNeural Networks, ComputerPositron-Emission TomographyAdultAgedAlgorithmsDihydroxyphenylalanineFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedRadiopharmaceuticalsRetrospective StudiesDihydroxyphenylalaninefluorodopa F 18RadiopharmaceuticalsBrain cancerCross-modalityGenerative adversarial networksImage-to-image translationPET/MR

Identifiers

PMID40682663

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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.