作者
Qiang Zhang, Matthew K Burrage, Elena Lukaschuk, Mayooran Shanmuganathan, Iulia A Popescu, Chrysovalantou Nikolaidou, Rebecca Mills, Konrad Werys, Evan Hann, Ahmet Barutcu, Suleyman D Polat, Hypertrophic Cardiomyopathy Registry (HCMR) Investigators, Michael Salerno, Michael Jerosch-Herold, Raymond Y Kwong, Hugh C Watkins, Christopher M Kramer, Stefan Neubauer, Vanessa M Ferreira, Stefan K Piechnik
发表日期
2021/8/24
期刊
Circulation
卷号
144
期号
8
页码范围
589-599
出版商
Lippincott Williams & Wilkins
简介
Background
Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to develop a contrast agent–free technology to replace LGE for faster and cheaper CMR scans.
Methods
A CMR virtual native enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1 maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multicenter …
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