Evidence map›Paper›PMID 42861037›Full record

ArticleQuantitative imaging in medicine and surgery2026

Quantification of plaque and detection of high-risk plaque characteristics based on coronary computed tomography angiography: comparison between superresolution deep learning reconstruction and hybrid iterative reconstruction.

Limiao Zou, Keting Xu, Xiaohuan Liu, Cheng Xu, Haipeng Liu, Ying Li, Ying Li, Tao Zhou, Lin Yang, Chunli Zhao and 19 more

Abstract read
In one paragraph

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

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

29 authors.

Limiao ZouDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Keting XuDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiaohuan LiuDepartment of Radiology, The First Affiliated Hospital of Henan Medical University, Weihui, China.
Cheng XuDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Haipeng LiuDepartment of Radiology, The First Affiliated Hospital of Henan Medical University, Weihui, China.
Ying LiDepartment of Radiology, People's Hospital of Mengzi City, Mengzi, China.
Ying LiDepartment of Radiology, People's Hospital Affiliated to Shandong First Medical University, Jinan, China.
Tao ZhouDepartment of Radiology, People's Hospital Affiliated to Shandong First Medical University, Jinan, China.
Lin YangDepartment of Radiology, Hanzhong Central Hospital, Hanzhong, China.
Chunli ZhaoDepartment of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Na LiDepartment of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Yi LvDepartment of Radiology, The First People's Hospital of Yulin, Yulin, China.
Wenli HongDepartment of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
Bo WangDepartment of Radiology, Yunnan Provincial Hospital of Traditional Chinese Medicine, Kunming, China.
Lijun ChenDepartment of Radiology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China.
Min XuScientific Collaboration, Canon Medical Systems China, Beijing, China.
Yufei LiResearch and Development Center, Canon Medical Systems China, Beijing, China.
Yunxin ZhongResearch and Development Center, Canon Medical Systems China, Beijing, China.
Zhiping ZhongDepartment of Radiology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China.
Jikun ZhengDepartment of Radiology, Yunnan Provincial Hospital of Traditional Chinese Medicine, Kunming, China.
Zhihua WuDepartment of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
Hongzhong MoDepartment of Radiology, The First People's Hospital of Yulin, Yulin, China.
Tong ZhangDepartment of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Demao DengDepartment of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Yaliang LiuDepartment of Radiology, Hanzhong Central Hospital, Hanzhong, China.
Shouqiang JiaDepartment of Radiology, People's Hospital Affiliated to Shandong First Medical University, Jinan, China.
Shaofei HeDepartment of Radiology, People's Hospital of Mengzi City, Mengzi, China.
Changhua LiangDepartment of Radiology, The First Affiliated Hospital of Henan Medical University, Weihui, China.
Yining WangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In addition to stenosis evaluation, coronary computed tomography angiography (CCTA) also allows for noninvasive plaque quantification and characterization, which informs risk stratification and management strategies. This study aimed to compare a novel superresolution deep learning reconstruction (SR-DLR) technique with a conventional hybrid iterative reconstruction (HIR) method in quantifying CCTA-derived plaque and detecting high-risk plaque (HRP) characteristics. Methods: A total of 255 consecutive patients (mean age 63.7±9.6 years; 184 men and 71 women) who underwent clinically indicated CCTA for assessment of suspected or known coronary artery disease were prospectively enrolled. The HIR algorithm with the FC43 kernel and the SR-DLR algorithm with the cardiac kernel reconstructed the same raw CCTA data. Total plaque, calcified plaque, noncalcified plaque (NCP), and low-attenuation plaque (LAP) volumes were quantified. Moreover, the ability to detect HRP characteristics (LAP, spotty calcification, positive remodeling, and napkin-ring sign) was evaluated. Paired comparisons were made between the two reconstruction pipelines. Results: Compared with the HIR pipeline, the SR-DLR pipeline obtained higher median volumes of total plaque {103.4 [interquartile range (IQR) 52.9-201.7] Conclusions: Compared with the HIR pipeline, the SR-DLR pipeline indicated higher total plaque, NCP, and LAP volumes, but no difference in calcified plaque volume was observed. Moreover, SR-DLR detected more LAPs than did HIR, while other HRP characteristics did not differ significantly. Further large-scale prospective studies including a reference standard are warranted to investigate the clinical impact of SR-DLR.

Indexed as

Coronary computed tomography angiography (CCTA)coronary plaque quantificationhigh-risk plaque characteristicshybrid iterative reconstruction (HIR)superresolution deep learning reconstruction (SR-DLR)

Identifiers

PMID42861037
PMCPMC13654107

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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.