Virtual native enhancement instead of late gadolinium enhancement

Virtual native enhancement instead of late gadolinium enhancement

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is considered the gold standard for non-invasive myocardial tissue characterization [1]. Artificial intelligence was used to develop a CMR virtual native enhancement (VNE) imaging which does not need an intravenous contrast as in LGE. VNE uses a deep learning model with multiple streams of convolutional neural networks to enhance existing signals of native T1 maps and cine imaging of cardiac structure and function to produce LGE equivalent images. T1 maps are pixel wise maps of tissue T1 relaxation times.

The technology was developed in CMR datasets from the Hypertrophic Cardiomyopathy Registry. Datasets were randomized into two independent groups for deep learning and testing as is usual with artificial intelligence programs. Test data of VNE and LGE were scored by experienced persons to assess image quality, myocardial lesion quantification and visuospatial agreement. 4093 triplets of matched T1 maps, cine and LGE datasets were obtained from 1348 HCM patients. 2695 datasets were used for the development of VNE method and 345 for independent testing. Significantly better image quality than LGE was obtained by VNE. VNE correlated with LGE in detecting and quantifying both hyperintensity and intermediate intensity myocardial lesions in 326 datasets (121 patients). A remarkable feature was that native CMR images can be acquired within 15 minutes and VNE image produced in less than a second [1].

If this technology is perfected, it will avoid the potential risks of contrast allergy and issues like nephrogenic systemic fibrosis in those with renal impairment while using LGE. The good amount of post processing time needed for getting good quality LGE images may also be saved. Interestingly, there were no false positives in this study. CMR-LGE for documentation of  fibrosis is important while deciding on the risk of sudden cardiac death and need for implantation of a cardioverter-defibrillator.

In a previous study, native T1-mapping was performed using a shortened modified Look-Locker imaging (ShMOLLI) technique in 25 HCM patients and 20 controls [2]. This was followed by LGE imaging after 10 minutes. There was very good agreement between fibrosis sizes calculated from the two methods. The authors concluded that native T1-mapping can be used for non-contrast assessment of myocardial fibrosis in HCM. VNE can be seen as an enhancement over the previous native T1 mapping by ShMOLLI technique.


  1. Zhang Q, Burrage MK, Lukaschuk E, Shanmuganathan M, Popescu IA, Nikolaidou C, Mills R, Werys K, Hann E, Barutcu A, Polat SD, Salerno M, Jerosch-Herold M, Kwong RY, Watkins HC, Kramer CM, Neubauer S, Ferreira VM, Piechnik SK; HCMR investigators. Towards Replacing Late Gadolinium Enhancement with Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy. Circulation. 2021 Jul 7. doi: 10.1161/CIRCULATIONAHA.121.054432. Epub ahead of print. PMID: 34229451.
  2. Małek ŁA, Werys K, Kłopotowski M, Śpiewak M, Miłosz-Wieczorek B, Mazurkiewicz Ł, Petryka-Mazurkiewicz J, Marczak M, Witkowski A. Native T1-mapping for non-contrast assessment of myocardial fibrosis in patients with hypertrophic cardiomyopathy–comparison with late enhancement quantification. Magn Reson Imaging. 2015 Jul;33(6):718-24.