Evidence map›Paper›PMID 35031812›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2022

A two-stage cardiac PET and late gadolinium enhancement MRI co-registration method for improved assessment of non-ischemic cardiomyopathies using integrated PET/MR.

Zheng Zhang, Xing Chen, Qing Wan, Haiyan Wang, Na Qi, Zhiwen You, Jianmin Yuan, Lingzhi Hu, Hongwei Sun, Zhe Wang and 2 more

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Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 36% of its field
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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. European journal of nuclear medicine and molecular imaging · 2026
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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

12 authors at 3 institutions in 1 country.

Zheng Zhang *The Institute of Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
Xing Chen *Department of Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Qing Wan *Department of Cardiovascular Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Haiyan WangDepartment of Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Na QiDepartment of Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Zhiwen YouDepartment of Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Jianmin YuanUnited Imaging Healthcare Co. Ltd., Shanghai, 201807, China.
Lingzhi HuUnited Imaging Healthcare Co. Ltd., Shanghai, 201807, China.
Hongwei SunUnited Imaging Healthcare Co. Ltd., Shanghai, 201807, China.
Zhe WangUnited Imaging Healthcare Co. Ltd., Shanghai, 201807, China.
Chenxi HuThe Institute of Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China. chenxi.hu@sjtu.edu.cn.
Jun ZhaoDepartment of Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China. petcenter@126.com.ORCID http://orcid.org/0000-0002-9887-5512
Shanghai East Hospital · CNUnited Imaging Healthcare (China) · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeRespiratory motion causes mismatches between PET images of the myocardium and the corresponding cardiac MR images in cardiac integrated PET/MR. The mismatch may affect the attenuation correction and the diagnosis of non-ischemic cardiomyopathies. In this study, we present a two-stage cardiac PET and MR late gadolinium enhancement (LGE) co-registration method, which seeks to improve diagnostic accuracy of non-ischemic cardiomyopathies via better image co-registration using an integrated whole-body PET/MR system.

methodsThe proposed PET and LGE two-stage co-registration method was evaluated through comparison with one-stage direct co-registration and no-registration. One hundred and ninety-one slices of LGE and forty lesions were studied. Two trained nuclear medicine physicians independently assessed the displacement between LGE and PET to qualitatively evaluate the co-registration quality. The changes of the mean SUV in the normal myocardium and the LGE-enhanced lesions before and after image co-registration were measured to quantitatively evaluate the accuracy and value of image co-registration.

resultsThe two-stage method had an improved image registration score (4.93 ± 0.89) compared with the no-registration method (3.49 ± 0.84, p value < 0.001) and the single-stage method (4.23 ± 0.81, p value < 0.001). Furthermore, the two-stage method led to increased SUV value in the myocardium (3.87 ± 2.56) compared with the no-registration method (3.14 ± 1.92, p value < 0.001) and the single-stage method (3.32 ± 2.16, p value < 0.001). The mean SUV in the LGE lesion significantly increased from 2.51 ± 2.09 to 2.85 ± 2.35 (p value < 0.001) after the two-stage co-registration.

conclusionThe proposed two-stage registration method significantly improved the co-registration between PET and LGE in integrated PET/MR imaging. The technique may improve diagnostic accuracy of non-ischemic cardiomyopathies via better image co-registration. REGISTERED NO: DF-2020-085,2020.04.30.

Indexed as

CardiomyopathiesGadoliniumContrast MediaHumansMagnetic Resonance ImagingPositron-Emission TomographyContrast MediaGadolinium18F-FDG PETCardiac PET/MRImage co-registrationLate gadolinium enhancement imageNon-ischemic cardiomyopathy

Identifiers

PMID35031812
OpenAlexW4226136095

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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.