Dataset Information

Heart deformation analysis: measuring regional myocardial velocity with MR imaging.

ABSTRACT: The aim of the present study was to test the hypothesis that heart deformation analysis (HDA) may serve as an alternative for the quantification of regional myocardial velocity. Nineteen healthy volunteers (14 male and 5 female) without documented cardiovascular diseases were recruited following the approval of the institutional review board (IRB). For each participant, cine images (at base, mid and apex levels of the left ventricle [LV]) and tissue phase mapping (TPM, at same short-axis slices of the LV) were acquired within a single magnetic resonance (MR) scan. Regional myocardial velocities in radial and circumferential directions acquired with HDA (Vrr and Vcc) and TPM (Vr and V?) were measured during the cardiac cycle. HDA required shorter processing time compared to TPM (2.3?±?1.1 min/case vs. 9.5?±?3.7 min/case, p?

SUBMITTER: Lin K

PROVIDER: S-EPMC4958501 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Heart deformation analysis: measuring regional myocardial velocity with MR imaging.

Lin Kai K Collins Jeremy D JD Chowdhary Varun V Markl Michael M Carr James C JC

The international journal of cardiovascular imaging 20160413 7

The aim of the present study was to test the hypothesis that heart deformation analysis (HDA) may serve as an alternative for the quantification of regional myocardial velocity. Nineteen healthy volunteers (14 male and 5 female) without documented cardiovascular diseases were recruited following the approval of the institutional review board (IRB). For each participant, cine images (at base, mid and apex levels of the left ventricle [LV]) and tissue phase mapping (TPM, at same short-axis slices ...[more]

PMID: 27076222

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Project description:BackgroundCardiac magnetic resonance (MR) images are often collected with different imaging parameters, which may impact the calculated values of myocardial radiomic features.PurposeTo investigate the sensitivity of myocardial radiomic features to changes in imaging parameters in cardiac MR images.Study typeProspective.PopulationA total of 11 healthy participants/five patients.Field strength/ sequenceA 3 T/cine balanced steady-state free-precession, T1 -weighted spoiled gradient-echo, T2 -weighted turbo spin-echo, and quantitative T1 and T2 mapping. For each sequence, the flip angle, in-plane resolution, slice thickness, and parallel imaging technique were varied to study the sensitivity of radiomic features to alterations in imaging parameters.AssessmentMyocardial contours were manually delineated by experienced readers, and a total of 1023 radiomic features were extracted using PyRadiomics with 11 image filters and six feature families.Statistical testsSensitivity was defined as the standardized mean difference (D effect size), and the robust features were defined at sensitivity < 0.2. Sensitivity analysis was performed on predefined sets of reproducible features. The analysis was performed using the entire cohort of 16 subejcts.Results64% of radiomic features were robust (sensitivity < 0.2) to changes in any imaging parameter. In qualitative sequences, radiomic features were most sensitive to changes in in-plane spatial resolution (spatial resolution: 0.6 vs. flip angle: 0.19, parallel imaging: 0.18, slice thickness: 0.07; P < 0.01 for all); in quantitative sequences, radiomic features were least sensitive to changes in spatial resolution (spatial resolution: 0.07 vs. slice thickness: 0.16, flip angle: 0.24; P < 0.01 for all). In an individual feature level, no singular feature family/image filter was identified as robust (sensitivity < 0.2) across sequences; however, highly sensitive features were predominantly associated with high-frequency wavelet filters across all sequences (32/50 features).Data conclusionIn cardiac MR, a considerable number of radiomic features are sensitive to changes in sequence parameters.Evidence level1 TECHNICAL EFFICACY: Stage 1.

Project description:AimTo use velocity vector imaging (VVI) technology to evaluate the correlation between the apical four-chamber view and short-axis myocardial movement in rats.MethodsWe used 25 10-week-old male Sprague-Dawley rats to measure the myocardium peak systolic velocity (Vs; cm/s), peak diastolic velocity (Vd; cm/s), peak systolic strain (SR; %), peak systolic strain rate (SRs; 1/s), and peak diastolic strain rate (SRd; 1/s) from the apical four-chamber view of the left ventricle (LV) and the parasternal mitral valve (PMV)-level short-axis view, and to analyze the correlation between myocardial motion in corresponding views of the two sections.ResultsComparing the myocardial motion between the lateral wall's basal segment in the apical four-chamber view of the LV and the lateral wall of the PMV-level short-axis view revealed that the Vd was positively correlated (r = 0.59, p<0.01), as was SRs (r = 0.68, p<0.05). Comparing the myocardial motion between the lateral wall's middle segment in the apical four-chamber view of the LV and the lateral wall of the PMV-level short-axis view demonstrated that Vd, SRs, and SRd were positively correlated (r = 0.63, 0.82, 0.79, respectively, all p<0.01). Our comparison of myocardial motion between the posterior septum's basal segment in the apical four-chamber view of the LV and the posterior septum of PMV-level short-axis view showed that Vd and SRs were positively correlated (r = 0.57, 0.68, respectively, both p<0.01). Comparing the myocardial motion between the posterior septum's middle segment in the apical four-chamber view of the LV and the posterior septum of the PMV-level short-axis view revealed that Vs, Vd, SR, and SRd were positively correlated (r = 0.89, 0.63, 0.64, 0.6, respectively, all p<0.01), and the SRs also had a significant positive correlation (r = 0.53, p<0.05).ConclusionVVI technology could be a valuable tool for evaluating the myocardial walls motion of the apical four-chamber view of the rat LV.

Project description:Recent dose reduction techniques have made retrospective computed tomography (CT) scans more applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible. However, hyperparameters of generic image intensity-based registration techniques, which are used for tracking motion, have not been systematically optimised for this modality. There is limited work on their validation for measuring regional strains from retrospective gated CCT images and open-source software for motion analysis is not widely available. We calculated strain using our open-source platform by applying an image registration warping field to a triangulated mesh of the left ventricular endocardium. We optimised hyperparameters of two registration methods to track the wall motion. Both methods required a single semi-automated segmentation of the left ventricle cavity at end-diastolic phase. The motion was characterised by the circumferential and longitudinal strains, as well as local area change throughout the cardiac cycle from a dataset of 24 patients. The derived motion was validated against manually annotated anatomical landmarks and the calculation of strains were verified using idealised problems. Optimising hyperparameters of registration methods allowed tracking of anatomical measurements with a mean error of 6.63% across frames, landmarks, and patients, comparable to an intra-observer error of 7.98%. Both registration methods differentiated between normal and dyssynchronous contraction patterns based on circumferential strain ([Formula: see text], [Formula: see text]). To test whether a typical 10 temporal frames sampling of retrospective gated CCT datasets affects measuring cardiac mechanics, we compared motion tracking results from 10 and 20 frames datasets and found a maximum error of [Formula: see text]. Our findings show that intensity-based registration techniques with optimal hyperparameters are able to accurately measure regional strains from CCT in a very short amount of time. Furthermore, sufficient sensitivity can be achieved to identify heart failure patients and left ventricle mechanics can be quantified with 10 reconstructed temporal frames. Our open-source platform will support increased use of CCT for quantifying cardiac mechanics.

Dataset Information

Heart deformation analysis: measuring regional myocardial velocity with MR imaging.

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Heart deformation analysis: measuring regional myocardial velocity with MR imaging.

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