ORCID Profile
0000-0001-5927-0230
Current Organisations
Oxford University Hospitals NHS Foundation Trust
,
University of Oxford
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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: Wiley
Date: 09-2009
DOI: 10.1002/MRM.22051
Abstract: Accurate measurement of peak velocity is critical to the assessment of patients with stenotic valvular disease. Conventional phase contrast (PC) methods for imaging high-velocity jets in aortic stenosis are susceptible to intravoxel dephasing signal loss, which can result in unreliable measurements. The most effective method for reducing intravoxel dephasing is to shorten the echo time (TE) however, the amount that TE can be shortened in conventional sequences is limited. A new sequence incorporating velocity-dependent slice excitation and ultrashort TE (UTE) centric radial readout trajectories is proposed that reduces TE from 2.85 to 0.65 ms. In a high-velocity stenotic jet phantom, a conventional sequence had >5% flow error at a flow rate of only 400 mL/s (velocity >358 cm/s), whereas the PC-UTE showed excellent agreement (<5% error) at much higher flow rates (1080 mL/s, 965 cm/s). In vivo feasibility studies demonstrated that by measuring velocity over a shorter time the PC-UTE approach is more robust to intravoxel dephasing signal loss. It also has less inherent higher-order motion encoding. This sequence therefore demonstrates potential as a more robust method for measuring peak velocity and flow in high-velocity turbulent stenotic jets.
Publisher: Oxford University Press (OUP)
Date: 28-04-2023
Abstract: Hypertrophic cardiomyopathy (HCM) is characterized by hypercontractility and diastolic dysfunction, which alter blood flow haemodynamics and are linked with increased risk of adverse clinical events. Four-dimensional flow cardiac magnetic resonance (4D-flow CMR) enables comprehensive characterization of ventricular blood flow patterns. We characterized flow component changes in non-obstructive HCM and assessed their relationship with phenotypic severity and sudden cardiac death (SCD) risk. Fifty-one participants (37 non-obstructive HCM and 14 matched controls) underwent 4D-flow CMR. Left-ventricular (LV) end-diastolic volume was separated into four components: direct flow (blood transiting the ventricle within one cycle), retained inflow (blood entering the ventricle and retained for one cycle), delayed ejection flow (retained ventricular blood ejected during systole), and residual volume (ventricular blood retained for & two cycles). Flow component distribution and component end-diastolic kinetic energy/mL were estimated. HCM patients demonstrated greater direct flow proportions compared with controls (47.9 ± 9% vs. 39.4 ± 6%, P = 0.002), with reduction in other components. Direct flow proportions correlated with LV mass index (r = 0.40, P = 0.004), end-diastolic volume index (r = −0.40, P = 0.017), and SCD risk (r = 0.34, P = 0.039). In contrast to controls, in HCM, stroke volume decreased with increasing direct flow proportions, indicating diminished volumetric reserve. There was no difference in component end-diastolic kinetic energy/mL. Non-obstructive HCM possesses a distinctive flow component distribution pattern characterised by greater direct flow proportions, and direct flow-stroke volume uncoupling indicative of diminished cardiac reserve. The correlation of direct flow proportion with phenotypic severity and SCD risk highlight its potential as a novel and sensitive haemodynamic measure of cardiovascular risk in HCM.
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 Saul Myerson.