Quinapril treatment curtails decline of global longitudinal strain and mechanical function in hypertensive rats.
Journal of hypertension
BACKGROUND: Left ventricular (LV) global longitudinal strain (GLS) has been proposed as an early imaging biomarker of cardiac mechanical dysfunction.OBJECTIVE: To assess the impact of angiotensin-converting enzyme (ACE) inhibitor treatment of hypertensive heart disease on LV GLS and mechanical function.METHODS: The spontaneously hypertensive rat (SHR) model of hypertensive heart disease (n = 38) was studied. A subset of SHRs received quinapril (TSHR, n = 16) from 3 months (mo). Wistar Kyoto rats (WKY, n = 13) were used as controls. Tagged cardiac MRI was performed using a 4.7 T Varian preclinical scanner.RESULTS: The SHRs had significantly lower LV ejection fraction (EF) than the WKYs at 3 mo (53.0 ± 1.7% vs. 69.6 ± 2.1%, P < 0.05), 14 mo (57.0 ± 2.5% vs. 74.4 ± 2.9%, P < 0.05) and 24 mo (50.1 ± 2.4% vs. 67.0 ± 2.0%, P < 0.01). At 24 mo, ACE inhibitor treatment was associated with significantly greater LV EF in TSHRs compared to untreated SHRs (64.2 ± 3.4% vs. 50.1 ± 2.4%, P < 0.01). Peak GLS magnitude was significantly lower in SHRs hearts compared with WKYs at 14 months (7.5% ± 0.4% vs. 9.9 ± 0.8%, P < 0.05). At 24 months, Peak GLS magnitude was significantly lower in SHRs compared with both WKYs (6.5 ± 0.4% vs. 9.7 ± 1.0%, P < 0.01) and TSHRs (6.5 ± 0.4% vs. 9.6 ± 0.6%, P < 0.05).CONCLUSIONS: ACE inhibitor treatment curtails the decline in global longitudinal strain in hypertensive rats, with the treatment group exhibiting significantly greater LV EF and GLS magnitude at 24 mo compared with untreated SHRs.
View details for DOI 10.1097/HJH.0000000000003512
View details for PubMedID 37466436
- StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE RADIOLOGY-CARDIOTHORACIC IMAGING 2023; 5 (3) Hide More
StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE.
Radiology. Cardiothoracic imaging
2023; 5 (3): e220196
To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI.In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models.The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc.StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.
View details for DOI 10.1148/ryct.220196
View details for PubMedID 37404792
View details for PubMedCentralID PMC10316292
- In Vivo Cardiac Diffusion Imaging Without Motion-Compensation Leads to Unreasonably High Diffusivity. Journal of magnetic resonance imaging : JMRI 2023 Hide More
Validating MRI-Derived Myocardial Stiffness Estimates Using In Vitro Synthetic Heart Models.
Annals of biomedical engineering
Impaired cardiac filling in response to increased passive myocardial stiffness contributes to the pathophysiology of heart failure. By leveraging cardiac MRI data and ventricular pressure measurements, we can estimate in vivo passive myocardial stiffness using personalized inverse finite element models. While it is well-known that this approach is subject to uncertainties, only few studies quantify the accuracy of these stiffness estimates. This lack of validation is, at least in part, due to the absence of ground truth in vivo passive myocardial stiffness values. Here, using 3D printing, we created soft, homogenous, isotropic, hyperelastic heart phantoms of varying geometry and stiffness and simulate diastolic filling by incorporating the phantoms into an MRI-compatible left ventricular inflation system. We estimate phantom stiffness from MRI and pressure data using inverse finite element analyses based on a Neo-Hookean model. We demonstrate that our identified softest and stiffest values of 215.7 and 512.3kPa agree well with the ground truth of 226.2 and 526.4kPa. Overall, our estimated stiffnesses revealed a good agreement with the ground truth ([Formula: see text] error) across all models. Our results suggest that MRI-driven computational constitutive modeling can accurately estimate synthetic heart material stiffnesses in the range of 200-500kPa.
View details for DOI 10.1007/s10439-023-03164-7
View details for PubMedID 36914919