scholarly journals Abnormalities in T2-weighted cardiovascular magnetic resonance images of hypertrophic cardiomyopathy: Regional distribution and relation to late gadolinium enhancement and severity of hypertrophy

2008 ◽  
Vol 28 (1) ◽  
pp. 242-245 ◽  
Author(s):  
Hassan Abdel-Aty ◽  
Myra Cocker ◽  
Oliver Strohm ◽  
Neil Filipchuk ◽  
Matthias G. Friedrich
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Late Gadolinium Enhancement ◽  
Hypertrophic Cardiomyopathy ◽  
Magnetic Resonance ◽  
Regional Distribution ◽  
Magnetic Resonance Images ◽  
Gadolinium Enhancement

scholarly journals Towards Replacing Late Gadolinium Enhancement with Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy

Circulation ◽  
2021 ◽  
Author(s):  
Qiang Zhang ◽  
Matthew K. Burrage ◽  
Elena Lukaschuk ◽  
Mayooran Shanmuganathan ◽  
Iulia A. Popescu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Magnetic Resonance ◽  
Deep Learning ◽  
Late Gadolinium Enhancement ◽  
Hypertrophic Cardiomyopathy ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Tissue Characterization ◽  
Wilcoxon Test ◽  
Gadolinium Enhancement

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.

scholarly journals Right-ventricular mechanics assessed by cardiovascular magnetic resonance feature tracking in children with hypertrophic cardiomyopathy

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248725
Author(s):  
Joanna Petryka-Mazurkiewicz ◽  
Lidia Ziolkowska ◽  
Łukasz Mazurkiewicz ◽  
Monika Kowalczyk-Domagała ◽  
Agnieszka Boruc ◽  
...  
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Late Gadolinium Enhancement ◽  
Hypertrophic Cardiomyopathy ◽  
Magnetic Resonance ◽  
Right Ventricular ◽  
Feature Tracking ◽  
Global Longitudinal Strain ◽  
Radial Strain ◽  
Gadolinium Enhancement ◽  
Ventricular Mechanics

Background Although hypertrophic cardiomyopathy (HCM) is considered a disease of the left ventricle (LV), right ventricular (RV) abnormalities have also been reported on. Cardiovascular magnetic resonance feature tracking (CMR-FT) accurately and reproducibly quantifies RV myocardial deformation. Aim To investigate RV deformation disorders in childhood HCM using CMR-FT. Material and methods Consecutive subjects aged <18 years with echocardiographic evidence of HCM were enrolled. Cardiovascular magnetic resonance (CMR) was performed including RV volumetric and functional assessment, and late gadolinium enhancement (LGE) imaging. Results We included 54 children (37 males, 68.5%) with HCM, of which 28 patients (51.8%; mean extent of 2.18 ± 2.34% of LV mass) had late gadolinium enhancement. LV outflow tract obstruction (LVOTO) was detected in 19 subjects (35.2%). In patients with LVOTO, RV global longitudinal strain (RVGLS) (-16.1±5.0 vs. -20.7±5.3, p<0.01), RVGLS rate (-1.05±0.30 vs. -1.26±0.40, p = 0.03), RV radial strain (RVR) (15.8±7.7 vs. 22.1±7.0, p<0.01) and RVR rate (0.95±0.35 vs. 1.6±0.44, p<0.01) were lower than in patients without LVOTO. The RVR rate (p<0.01) was lower in patients with LGE in comparison to patients without LGE. Conclusions Children with HCM, especially with LVOTO, have significantly reduced indices of RV mechanics despite normal RV systolic function. It seems that the degree of LVOT obstruction is responsible for compromising the RV dynamics, rather than either mass or the amount of LV fibrosis.

Sign in / Sign up

Export Citation Format

Share Document