Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive 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 multi-center Hypertrophic Cardiomyopathy Registry (HCMR), using HCM as an exemplar. The datasets were randomized into two independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement and myocardial lesion burden quantification. Image quality was compared using nonparametric Wilcoxon test. Intra- and inter-observer agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC. Results: 1348 HCM patients provided 4093 triplets of matched T1-maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development, and 345 for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets, p<0.001, Wilcoxon test). VNE revealed characteristic HCM lesions in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyper-intensity myocardial lesions (r=0.77-0.79, ICC=0.77-0.87; p<0.001) and intermediate-intensity lesions (r=0.70-0.76, ICC=0.82-0.85; p<0.001). The native CMR images (cine plus T1-map) required for VNE can be acquired within 15 minutes. Producing a VNE image takes less than one second. Conclusions: VNE is a new CMR technology that resembles conventional LGE, without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

Background: Myocardial scar is currently assessed non-invasively using cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) as an imaging gold-standard. However, a contrast-free approach would provide many advantages, including a faster and cheaper scan, without contrast-associated problems. Methods: 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 (AI). VNE was developed for patients with prior myocardial infarction on 4271 datasets (912 patients), where each dataset is comprised of slice position-matched cine, T1-maps and LGE images. After quality control, 3002 datasets (775 patients) were used for development, and 291 datasets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, employing two adversarial discriminators to improve the image quality. The left ventricle was contoured semi-automatically. 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 were compared using linear regression, Pearson correlation (R) and intraclass correlation coefficients (ICC). Proof-of-principle histopathological comparison of VNE in a porcine model of myocardial infarction was also performed. Results: VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 datasets (all p<0.001). VNE correlated strongly with LGE in quantifying scar size (R=0.89, ICC=0.94) and transmurality (R=0.84, ICC=0.90) in 66 patients (277 test datasets). Two CMR experts reviewed all test image slices and reported an overall accuracy of 84% of VNE in detecting scar 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. Conclusions: VNE demonstrated high agreement with LGE-CMR for myocardial scar assessment in patients with prior myocardial infarction in visuospatial distribution and lesion quantification, with superior image quality. VNE is a potentially transformative AI-based technology, with promise to reduce scan times and costs, increase clinical throughput, and improve the accessibility of CMR in the very-near future.