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Background: Properly understanding the origin and progression of the thoracic aortic aneurysm (TAA) can help prevent its growth and rupture. For a better understanding of this pathogenesis, the aortic blood flow has to be studied and interpreted in great detail. We can obtain detailed aortic blood flow information using magnetic resonance imaging (MRI) based computational fluid dynamics (CFD) with a prescribed motion of the aortic wall. Methods: We performed two different types of simulations—static (rigid wall) and dynamic (moving wall) for healthy control and a patient with a TAA. For the latter, we have developed a novel morphing approach based on the radial basis function (RBF) interpolation of the segmented 4D-flow MRI geometries at different time instants. Additionally, we have applied reconstructed 4D-flow MRI velocity profiles at the inlet with an automatic registration protocol. Results: The simulated RBF-based movement of the aorta matched well with the original 4D-flow MRI geometries. The wall movement was most dominant in the ascending aorta, accompanied by the highest variation of the blood flow patterns. The resulting data indicated significant differences between the dynamic and static simulations, with a relative difference for the patient of 7.47±14.18% in time-averaged wall shear stress and 15.97±43.32% in the oscillatory shear index (for the whole domain). Conclusions: In conclusion, the RBF-based morphing approach proved to be numerically accurate and computationally efficient in capturing complex kinematics of the aorta, as validated by 4D-flow MRI. We recommend this approach for future use in MRI-based CFD simulations in broad population studies. Performing these would bring a better understanding of the onset and growth of TAA. [ABSTRACT FROM AUTHOR]

Abstract Background Properly understanding the origin and progression of the thoracic aortic aneurysm (TAA) can help prevent its growth and rupture. For a better understanding of this pathogenesis, the aortic blood flow has to be studied and interpreted in great detail. We can obtain detailed aortic blood flow information using magnetic resonance imaging (MRI) based computational fluid dynamics (CFD) with a prescribed motion of the aortic wall. Methods We performed two different types of simulations—static (rigid wall) and dynamic (moving wall) for healthy control and a patient with a TAA. For the latter, we have developed a novel morphing approach based on the radial basis function (RBF) interpolation of the segmented 4D-flow MRI geometries at different time instants. Additionally, we have applied reconstructed 4D-flow MRI velocity profiles at the inlet with an automatic registration protocol. Results The simulated RBF-based movement of the aorta matched well with the original 4D-flow MRI geometries. The wall movement was most dominant in the ascending aorta, accompanied by the highest variation of the blood flow patterns. The resulting data indicated significant differences between the dynamic and static simulations, with a relative difference for the patient of 7.47±14.18% in time-averaged wall shear stress and 15.97±43.32% in the oscillatory shear index (for the whole domain). Conclusions In conclusion, the RBF-based morphing approach proved to be numerically accurate and computationally efficient in capturing complex kinematics of the aorta, as validated by 4D-flow MRI. We recommend this approach for future use in MRI-based CFD simulations in broad population studies. Performing these would bring a better understanding of the onset and growth of TAA.

Cardiovascular diseases (CVD) are today a major cause of death globally that is diagnosed by measurement and quantification of left ventricle (LV) wall motion (WM) abnormality of the heart. The aim of this study was to assess the utility of left ventricular (LV) entropy, a novel measure of disease derived from two-dimensional (2D) echocardiography images that assesses the probability distribution of pixel intensities in the LV. The purpose of this research is to develop the method of LV entropy to predict heart diseases. In this algorithm, a frame is usually chosen as the reference frame to extract the region of interest (ROI) around LV and then it is mapped to all images in a cardiac cycle. Then Shannon Entropy transform was applied to calculate the distribution of pixel intensities across the LV so we obtained entropy curves and compared them. The main idea is to find a motion estimation accuracy. The results obtained by our method are quantitatively evaluated to those obtained by an experienced echocardiographer visually on 22 normal cases and 19 myocardial infarction (MI) cases in apical four-chamber (A4C) view. The entropy of diastole in MI cases was 0.50 (0.29-0.58) while in normal cases was 0.75 (0.64-1.13). The entropy of systole in MI cases was 0.64 (0.26-1.04) while in normal cases was 0.81 (0.63-1.26). The percent change of entropy for diastole and systole between normal and MI cases are 33.3% and 20.2%. The results indicate that the LV entropy curves of MI cases have less changes than normal cases.

Cardiovascular diseases (CVD) are today a major cause of death globally that is diagnosed by measurement and quantification of left ventricle (LV) wall motion (WM) abnormality of heart. The aim of this study was to assess the utility of left ventricular (LV) entropy, a novel measure of disease derived from two-dimensional (2D) echocardiography images that assesses the probability distribution of pixel intensities in the LV. The proposed of this research is to develop the method of LV entropy to predict heart diseases. In this algorithm, a frame is usually chosen as the reference frame to extract region of interest (ROI) around LV and then it is mapped to all images in a cardiac cycle. Then Shannon Entropy transform was applied to calculate the distribution of pixel intensities across the LV so we obtained entropy curves and compared them. The main idea is to find a motion estimation accuracy. The results obtained by our method are quantitatively evaluated to those obtained by an experienced echocardiographer visually on 22 normal cases and 19 myocardial infarction (MI) cases in apical four-chamber (A4C) view. The entropy of diastole in MI cases was 0.50 (0.29- 0.58) while in normal cases was 0.75 (0.64-1.13). The entropy of systole in MI cases was 0.64 (0.26-1.04) while in normal cases was 0.81 (0.63-1.26). The percent change of entropy for diastole and systole between normal and MI cases are 33.3% and 20.2%. The results indicate that the LV entropy curves of MI cases have less changes than normal cases. [ABSTRACT FROM AUTHOR]