Evidence map›Paper›PMID 41932134›Full record

ArticleMedical image analysis2026

Functional imaging constrained diffusion for brain PET synthesis from structural MRI.

Minhui Yu, Mengqi Wu, Ling Yue, Andrea Bozoki, Mingxia Liu

Abstract read
In one paragraph

Article in Medical image analysis, 2026. 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. Distribution-Guided Multi-Tracer Brain PET Synthesis from Structural MRI with Class-Conditioned Weighted Diffusion.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026
    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

5 authors.

Minhui YuDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, 27599, USA. Electronic address: minhui.yu@unc.edu.
Mengqi WuDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, 27599, USA.
Ling YueDepartment of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China. Electronic address: bellinthemoon@hotmail.com.
Andrea BozokiDepartment of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. Electronic address: abozoki@unc.edu.
Mingxia LiuDepartment of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. Electronic address: mingxia_liu@med.unc.edu.

Funding

Optimized High-Resolution Fast Magnetic Resonance Fingerprinting with Cloud-Based ReconstructionR01NS134849 · NINDS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yong Chen, Pew-Thian Yap · 2024 to 2026
$1.9M
AI-Powered MRI Quality Control and Artifact Correction for Multi-Site StudiesR01EB035160 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Pew-Thian Yap · 2024 to 2026
$1.2M
NIBIB NIH HHS R01 EB035160NINDS NIH HHS R01 NS134849
6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used in multimodal analysis of neurodegenerative disorders. While MRI is broadly utilized in clinical settings, PET is less accessible. Many studies have attempted to use deep generative models to synthesize PET from MRI scans. However, they often suffer from unstable training and inadequately preserve brain functional information conveyed by PET. To this end, we propose a functional imaging constrained diffusion (FICD) framework for 3D brain PET image synthesis with paired structural MRI as input condition, through a new constrained diffusion model (CDM). The FICD introduces noise to PET and then progressively removes it with CDM, ensuring high output fidelity throughout a stable training phase. The CDM learns to predict denoised PET with a functional imaging constraint introduced to ensure voxel-wise alignment between each denoised PET and its ground truth. Quantitative and qualitative analyses conducted on 293 subjects with paired T1-weighted MRI and

Indexed as

BrainImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingMultimodal ImagingPositron-Emission TomographyAlgorithmsFluorodeoxyglucose F18Generative Artificial IntelligenceHumansImage EnhancementRadiopharmaceuticalsReproducibility of ResultsSensitivity and SpecificityFluorodeoxyglucose F18RadiopharmaceuticalsBrainDiffusion modelImage synthesisPETStructural MRI

Identifiers

PMID41932134
PMCPMC13263774

What Socratic 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 Socratic graph.