ORCID Profile
0000-0001-8036-6253
Current Organisations
Buckinghamshire Healthcare NHS Trust
,
University of Oxford
,
Royal Brompton and Harefield Hospitals
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Publisher: Elsevier BV
Date: 06-2021
Publisher: Frontiers Media SA
Date: 23-11-2021
Abstract: Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown good performance in general image registration tasks. We propose MOCOnet, a convolutional neural network solution, for generalisable motion artefact correction in T1 maps. Methods: The network architecture employs U-Net for producing distance vector fields and utilises warping layers to apply deformation to the feature maps in a coarse-to-fine manner. Using the UK Biobank imaging dataset scanned at 1.5T, MOCOnet was trained on 1,536 mid-ventricular T1 maps (acquired using the ShMOLLI method) with motion artefacts, generated by a customised deformation procedure, and tested on a different set of 200 s les with a erse range of motion. MOCOnet was compared to a well-validated baseline multi-modal image registration method. Motion reduction was visually assessed by 3 human experts, with motion scores ranging from 0% (strictly no motion) to 100% (very severe motion). Results: MOCOnet achieved fast image registration (& second per T1 map) and successfully suppressed a wide range of motion artefacts. MOCOnet significantly reduced motion scores from 37.1±21.5 to 13.3±10.5 ( p & 0.001), whereas the baseline method reduced it to 15.8±15.6 ( p & 0.001). MOCOnet was significantly better than the baseline method in suppressing motion artefacts and more consistently ( p = 0.007). Conclusion: MOCOnet demonstrated significantly better motion correction performance compared to a traditional image registration approach. Salvaging data affected by motion with robustness and in a time-efficient manner may enable better image quality and reliable images for immediate clinical interpretation.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 15-11-2022
DOI: 10.1161/CIRCULATIONAHA.122.060137
Abstract: 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 .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.
Publisher: Springer Science and Business Media LLC
Date: 30-06-2021
DOI: 10.1038/S41598-021-92923-4
Abstract: Stress and rest T1-mapping may assess for myocardial ischemia and extracellular volume (ECV). However, the stress T1 response is method-dependent, and underestimation may lead to misdiagnosis. Further, ECV quantification may be affected by time, as well as the number and dosage of gadolinium (Gd) contrast administered. We compared two commonly available T1-mapping approaches in their stress T1 response and ECV measurement stability. Healthy subjects (n = 10, 50% female, 35 ± 8 years) underwent regadenoson stress CMR (1.5 T) on two separate days. Prototype ShMOLLI 5(1)1(1)1 sequence was used to acquire consecutive mid-ventricular T1-maps at rest, stress and post-Gd contrast to track the T1 time evolution. For comparison, standard MOLLI sequences were used: MOLLI 5(3)3 Low (256 matrix) & High (192 matrix) Heart Rate (HR) to acquire rest and stress T1-maps, and MOLLI 4(1)3(1)2 Low & High HR for post-contrast T1-maps. Stress and rest myocardial blood flow (MBF) maps were acquired after IV Gd contrast (0.05 mmol/kg each). Stress T1 reactivity (delta T1) was defined as the relative percentage increase in native T1 between rest and stress. Myocardial T1 values for delta T1 (dT1) and ECV were calculated. Residuals from the identified time dependencies were used to assess intra-method variability. ShMOLLI achieved a greater stress T1 response compared to MOLLI Low and High HR (peak dT1 = 6.4 ± 1.7% vs. 4.8 ± 1.3% vs. 3.8 ± 1.0%, respectively both p 0.0001). ShMOLLI dT1 correlated strongly with stress MBF (r = 0.77, p 0.001), compared to MOLLI Low HR (r = 0.65, p 0.01) and MOLLI High HR (r = 0.43, p = 0.07). ShMOLLI ECV was more stable to gadolinium dose with less time drift (0.006–0.04% per minute) than MOLLI variants. Overall, ShMOLLI demonstrated less intra-in idual variability than MOLLI variants for stress T1 and ECV quantification. Power calculations indicate up to a fourfold (stress T1) and 7.5-fold (ECV) advantage in s le-size reduction using ShMOLLI. Our results indicate that ShMOLLI correlates strongly with increased MBF during regadenoson stress and achieves a significantly higher stress T1 response, greater effect size, and greater ECV measurement stability compared with the MOLLI variants tested.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 24-08-2021
DOI: 10.1161/CIRCULATIONAHA.121.054432
Abstract: 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. 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 Hypertrophic Cardiomyopathy Registry, using hypertrophic cardiomyopathy as an exemplar. The datasets were randomized into 2 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 a nonparametric Wilcoxon test. Intra- and interobserver agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC. A total of 1348 hypertrophic cardiomyopathy 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 were used for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets P .001 [Wilcoxon test]). VNE revealed lesions characteristic of hypertrophic cardiomyopathy in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyperintensity myocardial lesions ( r =0.77–0.79 ICC=0.77–0.87 P .001) and intermediate-intensity lesions ( r =0.70–0.76 ICC=0.82–0.85 P .001). The native CMR images (cine plus T1 map) required for VNE can be acquired within 15 minutes and producing a VNE image takes less than 1 second. VNE is a new CMR technology that resembles conventional LGE but without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 10-2021
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 05-07-2022
Abstract: The sympathetic cotransmitter, neuropeptide Y (NPY), is released into the coronary sinus during ST‐segment–elevation myocardial infarction and can constrict the coronary microvasculature. We sought to establish whether peripheral venous (PV) NPY levels, which are easy to obtain and measure, are associated with microvascular obstruction, myocardial recovery, and prognosis. NPY levels were measured immediately after primary percutaneous coronary intervention and compared with angiographic and cardiovascular magnetic resonance indexes of microvascular function. Patients were prospectively followed up for 6.4 (interquartile range, 4.1–8.0) years. PV (n=163) and coronary sinus (n=68) NPY levels were significantly correlated ( r =0.92 P .001) and associated with multiple coronary and imaging parameters of microvascular function and infarct size (such as coronary flow reserve, acute myocardial edema, left ventricular ejection fraction, and late gadolinium enhancement 6 months later). We therefore assessed the prognostic value of PV NPY during follow‐up, where 34 patients (20.7%) developed heart failure or died. Kaplan‐Meier survival analysis demonstrated that high PV NPY levels ( .4 pg/mL by binary recursive partitioning) were associated with increased incidence of heart failure and mortality (hazard ratio, 3.49 [95% CI, 1.65–7.4] P .001). This relationship was maintained after adjustment for age, cardiovascular risk factors, and previous myocardial infarction. Both PV and coronary sinus NPY levels correlate with microvascular function and infarct size after ST‐segment–elevation myocardial infarction. PV NPY levels are associated with the subsequent development of heart failure or mortality and may therefore be a useful prognostic marker. Further research is required to validate these findings.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Elsevier BV
Date: 2023
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Mayooran Shanmuganathan.