Evidence map›Paper›PMID 38388867›Full record

ArticleJournal of imaging informatics in medicine2024

Comparing Strain Assessment in Compressed Sensing and Conventional Cine MRI.

Kaixuan Yao, Wei Deng, Rong He, Hui Gao, Linlin Wang, Ren Zhao, Xiuzheng Yue, Yongqiang Yu, Liang Zhong, Xiaohu Li

Abstract readComparative Study
In one paragraph

Article in Journal of imaging informatics in medicine, 2024. 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
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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

10 authors.

Kaixuan Yao *Research Center of Clinical Medical Imaging; Anhui Province Clinical Image Quality Control Center, Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui Province, China.
Wei Deng *Research Center of Clinical Medical Imaging; Anhui Province Clinical Image Quality Control Center, Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui Province, China.
Rong HeResearch Center of Clinical Medical Imaging; Anhui Province Clinical Image Quality Control Center, Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui Province, China.
Hui GaoResearch Center of Clinical Medical Imaging; Anhui Province Clinical Image Quality Control Center, Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui Province, China.
Linlin WangImaging Center, Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230031, People's Republic of China.
Ren ZhaoDepartment of Cardiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Hefei, 230022, China.
Xiuzheng YuePhilips Healthcare, Beijing, 100600, China.
Yongqiang YuResearch Center of Clinical Medical Imaging; Anhui Province Clinical Image Quality Control Center, Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui Province, China.
Liang ZhongNational Heart Centre Singapore, Duke NUS Medical School, National University of Singapore, Queenstown, Singapore. zhong.liang@nhcs.com.sg.
Xiaohu LiResearch Center of Clinical Medical Imaging; Anhui Province Clinical Image Quality Control Center, Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui Province, China. lixiaohu@ahmu.edu.cn.ORCID 0000-0002-9084-1254

Funding

Anhui Provincial Natural Science Foundation 202304295107020027Anhui Provincial Natural Science Foundation 202304295107020028Anhui Provincial Natural Science Foundation 202304295107020029Anhui Provincial Natural Science Foundation 2308085Y48National Natural Science Foundation of China 81970446National Natural Science Foundation of China 82071897National Natural Science Foundation of China 82371949National Natural Science Foundation of China 82371959Science Fund for Distinguished Young Scholars of Anhui Province 2022AH020071
6 · The paper itself

Abstract

The aim of this study is to assess the feasibility of compressed sensing (CS) acceleration methods compared to conventional segmented cine (Seg) cardiac magnetic resonance (CMR) for evaluating left ventricular (LV) function and strain by feature tracking (FT). In this prospective study, 45 healthy volunteers underwent CMR imaging used Seg, threefold (CS3), fourfold (CS4), and eightfold (CS8) CS acceleration. Cine images were scored for quality (1-5 scale). LV volumetric and functional parameters and global longitudinal (GLS), circumferential (GCS), and radial strains (GRS) were quantified. LV volumetric and functional parameters exhibited no differences between Seg and all CS cines (all P > 0.05). The strains were similar for Seg, CS3, and CS4 (all P > 0.05). Similarly, no significant differences were observed in GRS and GCS between Seg and CS8 (all P > 0.05), but the global longitudinal strain was significantly lower for CS8 versus Seg (P < 0.001). Image quality declined with CS acceleration, especially in long-axis views with CS8. CS cine MRI at acceleration factor 4 maintained good image quality and accurate measurements of LV function and strain, although there was a slight reduction in the quality of long-axis images and GLS with CS8. CS acceleration up to a factor of 4 enabled fast CMR evaluations, making it suitable for clinical use.

Indexed as

Magnetic Resonance Imaging, CineAdultFeasibility StudiesFemaleHeart VentriclesHumansImage Interpretation, Computer-AssistedMaleMiddle AgedProspective StudiesVentricular Function, LeftYoung AdultAcceleration factorsCardiac magnetic resonanceCompressed sensingFeature trackingStrain

Identifiers

PMID38388867
PMCPMC11300746

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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.