ORCID Profile
0000-0002-0268-5221
Current Organisation
University of Oxford
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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: 14-10-2013
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 03-2016
DOI: 10.1161/CIRCIMAGING.115.004430
Abstract: Patients with treated HIV infection have clear survival benefits although with increased cardiac morbidity and mortality. Mechanisms of heart disease may be partly related to untreated chronic inflammation. Cardiovascular magnetic resonance imaging allows a comprehensive assessment of myocardial structure, function, and tissue characterization. We investigated, using cardiovascular magnetic resonance, subclinical inflammation and myocardial disease in asymptomatic HIV-infected in iduals. Myocardial structure and function were assessed using cardiovascular magnetic resonance at 1.5-T in treated HIV-infected in iduals without known cardiovascular disease (n=103 mean age, 45±10 years) compared with healthy controls (n=92 mean age, 44±10 years). Assessments included left ventricular volumes, ejection fraction, strain, regional systolic, diastolic function, native T1 mapping, edema, and gadolinium enhancement. Compared with controls, subjects with HIV infection had 6% lower left ventricular ejection fraction ( P .001), 7% higher myocardial mass ( P =0.02), 29% lower peak diastolic strain rate ( P .001), 4% higher short-tau inversion recovery values ( P =0.02), and higher native T1 values (969 versus 956 ms in controls P =0.01). Pericardial effusions and myocardial fibrosis were 3 and 4× more common, respectively, in subjects with HIV infection (both P .001). Treated HIV infection is associated with changes in myocardial structure and function in addition to higher rates of subclinical myocardial edema and fibrosis and frequent pericardial effusions. Chronic systemic inflammation in HIV, which involves the myocardium and pericardium, may explain the high rate of myocardial fibrosis and increased cardiac dysfunction in people living with HIV.
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: Elsevier BV
Date: 11-2021
Publisher: Springer Science and Business Media LLC
Date: 08-03-2021
DOI: 10.1007/S40520-021-01808-Z
Abstract: Coronavirus disease 2019 (COVID-19) disproportionately affects older people. Observational studies suggest indolent cardiovascular involvement after recovery from acute COVID-19. However, these findings may reflect pre-existing cardiac phenotypes. We tested the association of baseline cardiovascular magnetic resonance (CMR) phenotypes with incident COVID-19. We studied UK Biobank participants with CMR imaging and COVID-19 testing. We considered left and right ventricular (LV, RV) volumes, ejection fractions, and stroke volumes, LV mass, LV strain, native T1, aortic distensibility, and arterial stiffness index. COVID-19 test results were obtained from Public Health England. Co-morbidities were ascertained from self-report and hospital episode statistics (HES). Critical care admission and death were from HES and death register records. We investigated the association of each cardiovascular measure with COVID-19 test result in multivariable logistic regression models adjusting for age, sex, ethnicity, deprivation, body mass index, smoking, diabetes, hypertension, high cholesterol, and prior myocardial infarction. We studied 310 participants ( n = 70 positive). Median age was 63.8 [57.5, 72.1] years 51.0% ( n = 158) were male. 78.7% ( n = 244) were tested in hospital, 3.5% ( n = 11) required critical care admission, and 6.1% ( n = 19) died. In fully adjusted models, smaller LV/RV end-diastolic volumes, smaller LV stroke volume, and poorer global longitudinal strain were associated with significantly higher odds of COVID-19 positivity. We demonstrate association of pre-existing adverse CMR phenotypes with greater odds of COVID-19 positivity independent of classical cardiovascular risk factors. Observational reports of cardiovascular involvement after COVID-19 may, at least partly, reflect pre-existing cardiac status rather than COVID-19 induced alterations.
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: Oxford University Press (OUP)
Date: 28-11-2022
Abstract: Obstructive hypertrophic cardiomyopathy (oHCM) is characterized by dynamic obstruction of the left ventricular (LV) outflow tract (LVOT). Although this may be mediated by interplay between the hypertrophied septal wall, systolic anterior motion of the mitral valve, and papillary muscle abnormalities, the mechanistic role of LV shape is still not fully understood. This study sought to identify the LV end-diastolic morphology underpinning oHCM. Cardiovascular magnetic resonance images from 2398 HCM in iduals were obtained as part of the NHLBI HCM Registry. Three-dimensional LV models were constructed and used, together with a principal component analysis, to build a statistical shape model capturing shape variations. A set of linear discriminant axes were built to define and quantify (Z-scores) the characteristic LV morphology associated with LVOT obstruction (LVOTO) under different physiological conditions and the relationship between LV phenotype and genotype. The LV remodelling pattern in oHCM consisted not only of basal septal hypertrophy but a combination with LV lengthening, apical dilatation, and LVOT inward remodelling. Salient differences were observed between obstructive cases at rest and stress. Genotype negative cases showed a tendency towards more obstructive phenotypes both at rest and stress. LV anatomy underpinning oHCM consists of basal septal hypertrophy, apical dilatation, LV lengthening, and LVOT inward remodelling. Differences between oHCM cases at rest and stress, as well as the relationship between LV phenotype and genotype, suggest different mechanisms for LVOTO. Proposed Z-scores render an opportunity of redefining management strategies based on the relationship between LV anatomy and LVOTO.
Publisher: Elsevier BV
Date: 2023
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 Stefan K. Piechnik.