Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning

doi:10.1161/CIRCIMAGING.124.016786

	Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning
	Qi, Haikun1,2 ; Qian, Pengfang1 ; Tang, Langlang 3; Chen, Binghua 4; An, Dongaolei 4; Wu, Lian-Ming 4
	2024-09-01
发表期刊	CIRCULATION-CARDIOVASCULAR IMAGING (IF:6.5[JCR-2023],8.2[5-Year])
ISSN	1941-9651
EISSN	1942-0080
卷号	17 期号:9
发表状态	已发表
DOI	10.1161/CIRCIMAGING.124.016786
摘要	BACKGROUND: Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard technique for diagnosing myocardial infarction (MI), which, however, poses risks due to gadolinium contrast usage. Techniques enabling MI assessment based on contrast-free CMR are desirable to overcome the limitations associated with contrast enhancement. METHODS: We introduce a novel deep generative learning method, termed cine-generated enhancement (CGE), which transforms standard contrast-free cine CMR into LGE-equivalent images for MI assessment. CGE features with multislice spatiotemporal feature extractor, enhancement contrast modulation, and sophisticated loss function. Data from 430 patients with acute MI from 3 centers were collected. After image quality control, 1525 pairs (289 patients) of center I were used for training, and 293 slices (52 patients) of the same center were reserved for internal testing. The 40 patients (401 slices) of the other 2 centers were used for external testing. The CGE robustness was further tested in 20 normal subjects in a public cine CMR data set. CGE images were compared with LGE for image quality assessment and MI quantification regarding scar size and transmurality. RESULTS: The CGE method produced images of superior quality to LGE in both internal and external data sets. There was a significant (P<0.001) correlation between CGE and LGE measurements of scar size (Pearson correlation, 0.79/0.80; intraclass correlation coefficient, 0.79/0.77) and transmurality (Pearson correlation, 0.76/0.64; intraclass correlation coefficient, 0.76/0.63) in internal/external data set. Considering all data sets, CGE demonstrated high sensitivity (91.27%) and specificity (95.83%) in detecting scars. Realistic enhancement images were obtained for the normal subjects in the public data set without false positive subjects. CONCLUSIONS: CGE achieved superior image quality to LGE and accurate scar delineation in patients with acute MI of both internal and external data sets. CGE can significantly simplify the CMR examination, reducing scan times and risks associated with gadolinium-based contrasts, which are crucial for acute patients.
关键词	deep learning magnetic resonance spectroscopy myocardial infarction
URL	查看原文
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China["82102027","82171884","82101981"] ; High Technology Research and Development Center of the Ministry of Science and Technology of China[SQ2022YFC2400133]
WOS研究方向	Cardiovascular System & Cardiology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Cardiac & Cardiovascular Systems ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001314566600019
出版者	LIPPINCOTT WILLIAMS & WILKINS
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/427466
专题	生物医学工程学院信息科学与技术学院_硕士生生物医学工程学院_PI研究组_齐海坤组
通讯作者	Qi, Haikun; An, Dongaolei; Wu, Lian-Ming
作者单位	1.ShanghaiTech Univ, Sch Biomed Engn, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China 2.Shanghai Clin Res & Trial Ctr, Shanghai, Peoples R China 3.Fujian Med Univ, Longyan Hosp 1, Dept Radiol, Fuzhou, Peoples R China 4.Shanghai Jiao Tong Univ, Renji Hosp, Sch Med, Dept Radiol, 160 Pujian Rd, Shanghai 200127, Peoples R China
第一作者单位	生物医学工程学院
通讯作者单位	生物医学工程学院
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Qi, Haikun,Qian, Pengfang,Tang, Langlang,et al. Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning[J]. CIRCULATION-CARDIOVASCULAR IMAGING,2024,17(9).
APA	Qi, Haikun,Qian, Pengfang,Tang, Langlang,Chen, Binghua,An, Dongaolei,&Wu, Lian-Ming.(2024).Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning.CIRCULATION-CARDIOVASCULAR IMAGING,17(9).
MLA	Qi, Haikun,et al."Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning".CIRCULATION-CARDIOVASCULAR IMAGING 17.9(2024).