Evidence map›Paper›PMID 41912831›Full record

ArticleNPJ digital medicine2026

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease.

Jing Wang, Aocheng Zhong, Qian Xu, Haolin Huang, Yuhua Zhu, Jiaying Lu, Min Wang, Jiehui Jiang, Chengyang Li, Ming Ni and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

18 authors.

Jing Wang *Department of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China.
Aocheng Zhong *School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
Qian Xu *Department of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China.
Haolin Huang *School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
Yuhua ZhuDepartment of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China.
Jiaying LuDepartment of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China.
Min WangSchool of Life Sciences, Shanghai University, Shanghai, China.
Jiehui JiangSchool of Life Sciences, Shanghai University, Shanghai, China.
Chengyang LiDepartment of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China.
Ming NiDepartment of Nuclear Medicine, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Kaicong SunSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
Yihui GuanDepartment of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China.
Jie LuDepartment of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
Mei TianDepartment of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China.
Dinggang ShenSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
Huiwei ZhangDepartment of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China. zhanghuiwei@fudan.edu.cn.
Qian WangSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China. qianwang@shanghaitech.edu.cn.
Chuantao ZuoDepartment of Nuclear Medicine/PET center, Huashan Hospital, Fudan University, Shanghai, China. zuochuantao@fudan.edu.cn.

Funding

Shanghai Science and Technology Program Project 25TS1405000STI2030-Major Projects 2022ZD0211600the Basic Research Talent Development Program of Huashan Hospital, Fudan University 2025JC077the National Natural Science Foundation of China 82394432the National Natural Science Foundation of China 82394434,82272039, and 82021002the Shanghai Medical Innovation & Development Foundation SMIDF-150-2025A18
6 · The paper itself

Abstract

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude threshold transfer and cross-site comparability. We developed and validated the first unified, anatomically guided deep-learning framework to harmonize PET-MRI quantification to PET-CT standards across multiple tracers and scanner manufacturers. The model learns CT-anchored attenuation representations using a vision transformer autoencoder, aligns MRI features to the CT space via contrastive objectives, and performs attention-guided residual correction. In paired same-day scans (N = 70;

Identifiers

PMID41912831
PMCPMC13199423

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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.