Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.
Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.
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DOI：
10.1161/circulationaha.122.060137
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发表时间：
2022-11-15
期刊：
影响因子：
37.8
通讯作者：
Ferreira VM
中科院分区：
文献类型：
--
作者：
Zhang Q;Burrage MK;Shanmuganathan M;Gonzales RA;Lukaschuk E;Thomas KE;Mills R;Leal Pelado J;Nikolaidou C;Popescu IA;Lee YP;Zhang X;Dharmakumar R;Myerson SG;Rider O;Oxford Acute Myocardial Infarction (OxAMI) Study;Channon KM;Neubauer S;Piechnik SK;Ferreira VM
使用心血管磁共振晚期钆增强（LGE）作为成像金标准，对心肌疤痕进行无创评估。无对比度的方法将提供许多优点，包括更快、更便宜的扫描，而没有与对比度相关的问题。虚拟原生增强（VNE）是一种新的技术，可以产生虚拟LGE样图像，而不需要对比度。VNE结合了电影成像和原生T1图，使用人工智能生成类似LGE的图像。从4271个数据集（912例患者）中为既往心肌梗死患者开发VNE;每个数据集包括切片位置匹配的电影、T1图和LGE图像。质量控制后，3002个数据集（775例患者）用于开发，291个数据集（68例患者）用于测试。使用生成对抗网络训练VNE生成器，使用2个对抗鉴别器来提高图像质量。半自动绘制左心室轮廓。采用半峰全宽法定量心肌瘢痕体积。使用中心线弦法测量瘢痕透壁性，并在靶心图上可视化。使用线性回归、Pearson相关（R）和组内相关系数比较VNE和LGE的病变定量。还进行了VNE在猪心肌梗死模型中的原理证明组织病理学比较。在5名独立操作员对291个数据集进行盲法分析时，VNE提供的图像质量明显优于LGE（所有P <0.001）。在66名患者（277个测试数据集）中，VNE与LGE在量化瘢痕大小（R，0.89;组内相关系数，0.94）和透壁性（R，0.84;组内相关系数，0.90）方面密切相关。两名心血管磁共振专家审查了所有测试图像切片，并报告与LGE相比，VNE检测疤痕的总体准确性为84%，特异性为100%，灵敏度为77%。在2例猪心肌梗死模型中，VNE也显示出与组织病理学良好的视觉空间一致性。VNE与LGE心血管磁共振在既往心肌梗死患者的心肌瘢痕评估中在视觉空间分布和病变量化方面表现出高度一致性，具有上级图像质量。VNE是一种潜在的基于人工智能的变革性技术，有望在不久的将来减少扫描时间和成本，提高临床吞吐量，并改善心血管磁共振的可访问性。
Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan without contrast-associated problems. Virtual native enhancement (VNE) is a novel technology that can produce virtual LGE-like images without the need for contrast. VNE combines cine imaging and native T1 maps to produce LGE-like images using artificial intelligence. VNE was developed for patients with previous myocardial infarction from 4271 data sets (912 patients); each data set comprises slice position-matched cine, T1 maps, and LGE images. After quality control, 3002 data sets (775 patients) were used for development and 291 data sets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, using 2 adversarial discriminators to improve the image quality. The left ventricle was contoured semiautomatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull’s-eye plots. Lesion quantification by VNE and LGE was compared using linear regression, Pearson correlation (R), and intraclass correlation coefficients. Proof-of-principle histopathologic comparison of VNE in a porcine model of myocardial infarction also was performed. VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 data sets (all P<0.001). VNE correlated strongly with LGE in quantifying scar size (R, 0.89; intraclass correlation coefficient, 0.94) and transmurality (R, 0.84; intraclass correlation coefficient, 0.90) in 66 patients (277 test data sets). Two cardiovascular magnetic resonance experts reviewed all test image slices and reported an overall accuracy of 84% for VNE in detecting scars when compared with LGE, with specificity of 100% and sensitivity of 77%. VNE also showed excellent visuospatial agreement with histopathology in 2 cases of a porcine model of myocardial infarction. VNE demonstrated high agreement with LGE cardiovascular magnetic resonance for myocardial scar assessment in patients with previous myocardial infarction in visuospatial distribution and lesion quantification with superior image quality. VNE is a potentially transformative artificial intelligence–based technology with promise in reducing scan times and costs, increasing clinical throughput, and improving the accessibility of cardiovascular magnetic resonance in the near future.