Evidence map›Paper›PMID 39912939›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Optimizing MR-based attenuation correction in hybrid PET/MR using deep learning: validation with a flatbed insert and consistent patient positioning.

Hanzhong Wang, Yue Wang, Qiaoyi Xue, Yu Zhang, Xiaoya Qiao, Zengping Lin, Jiaxu Zheng, Zheng Zhang, Yang Yang, Min Zhang and 5 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

15 authors.

Hanzhong WangDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yue WangDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qiaoyi XueCentral Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
Yu ZhangDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaoya QiaoDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zengping LinCentral Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
Jiaxu ZhengCentral Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
Zheng ZhangCentral Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
Yang YangBeijing United Imaging Research Institute of Intelligent Imaging, Beijing, China.
Min ZhangDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qiu HuangDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yanqi HuangCentral Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
Tuoyu CaoCentral Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
Jin WangDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. wj21061@rjh.com.cn.
Biao LiDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. lb10363@rjh.com.cn.ORCID 0000-0002-0493-2062

Funding

National leading clinical discipline project and Shanghai Municipal Key Clinical Specialty shslczdzk03403
6 · The paper itself

Abstract

purposeTo address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve precise MR-CT matching for accurate MRAC evaluation.

methodsA validation dataset of 21 patients underwent whole-body [

resultsDistinct differences were observed among MRAC methods in spine and femur regions. Joint histogram analysis showed MRAC-4 (continuous μ-map) closely aligned with CTAC. Lesion-distance analysis revealed MRAC-4 minimized bone-induced SUV interference (r = 0.01, p = 0.8643). However, tissues prone to bone segmentation interference, such as the spine and liver, exhibited greater SUV variability and lower reproducibility in MRAC-4 compared to MRAC-2 (2D bone segmentation, discrete μ-map) and MRAC-3 (3D bone segmentation, discrete μ-map).

conclusionUsing a flatbed insert, this study validated MRAC with high precision. Continuous μ-value MRAC method (MRAC-4) demonstrated superior accuracy and minimized bone-related SUV errors but faced challenges in reproducibility, particularly in bone-rich regions.

Indexed as

Deep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingMultimodal ImagingPatient PositioningPositron Emission Tomography Computed TomographyAdultAgedFemaleFluorodeoxyglucose F18HumansMaleMiddle AgedReproducibility of ResultsFluorodeoxyglucose F18Attenuation correctionDeep learningImage synthesisPET/MR

Identifiers

PMID39912939

What OpenQuestion holds

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Registered trials

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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.