A spatiotemporal analysis of the left coronary artery biomechanics using fluid-structure interaction models.

Biomechanics plays a critical role in coronary artery disease development. FSI simulation is commonly used to understand the hemodynamics and mechanical environment associated with atherosclerosis pathology. To provide a comprehensive characterization of patient-specific coronary biomechanics, an analysis of FSI simulation in the spatial and temporal domains was performed. In the current study, a three-dimensional FSI model of the LAD coronary artery was built based on a patient-specific geometry using COMSOL Multiphysics. The effect of myocardial bridging was simulated. Wall shear stress and its derivatives including time-averaged wall shear stress, wall shear stress gradient, and OSI were calculated across the cardiac cycle in multiple locations. Arterial wall strain (radial, circumferential, and longitudinal) and von Mises stress were calculated. To assess perfusion, vFFR was calculated. The results demonstrated the FSI model could identify regional and transient differences in biomechanical parameters within the coronary artery. The addition of myocardial bridging caused a notable change in von Mises stress and an increase in arterial strain during systole. The analysis performed in this manner takes greater advantage of the information provided in the space and time domains and can potentially assist clinical evaluation.

Left Ventricle Wall Motion Analysis with Real-Time MRI Feature Tracking in Heart Failure Patients: A Pilot Study.

Volumetric measurements with cardiac magnetic resonance imaging (MRI) are effective for evaluating heart failure (HF) with systolic dysfunction that typically induces a lower ejection fraction (EF) than normal (<50%) while they are not sensitive to diastolic dysfunction in HF patients with preserved EF (≥50%). This work is to investigate whether HF evaluation with cardiac MRI can be improved with real-time MRI feature tracking. In a cardiac MRI study, we recruited 16 healthy volunteers, 8 HF patients with EF < 50% and 10 HF patients with preserved EF. Using real-time feature tracking, a cardiac MRI index, torsion correlation, was calculated which evaluated the correlation of torsional and radial wall motion in the left ventricle (LV) over a series of sequential cardiac cycles. The HF patients with preserved EF and the healthy volunteers presented significant difference in torsion correlation (one-way ANOVA, p < 0.001). In the scatter plots of EF against torsion correlation, the HF patients with EF < 50%, the HF patients with preserved EF and the healthy volunteers were well differentiated, indicating that real-time MRI feature tracking provided LV function assessment complementary to volumetric measurements. This study demonstrated the potential of cardiac MRI for evaluating both systolic and diastolic dysfunction in HF patients.

A deep learning approach to real-time volumetric measurements without image reconstruction for cardiovascular magnetic resonance.

Objective.Cardiovascular magnetic resonance (CMR) can measure ventricular volumes for the quantitative assessment of cardiac function in clinical cardiology. Conventionally, CMR volumetric measurements require image reconstruction and segmentation. There are limited clinical applications of real-time CMR for volumetric measurements because real-time images cannot provide sufficient quality for accurate segmentation. The presented work aims to develop a new deep learning approach to measuring ventricular volumes without image reconstruction and demonstrate that this 'imageless' approach would improve volumetric measurements with real-time CMR.Approach. We have developed a deep learning model for measuring ventricular volumes directly from real-time CMR raw data without image reconstruction. This novel 'imageless' deep learning model, not being as sensitive to image quality, provided reliable volumetric measurements for real-time CMR. To demonstrate 'imageless' volumetric measurements, we conducted a real-time CMR study with healthy volunteers. Several performance metrics, including mean absolute error (MAE), the Pearson correlation coefficient, and Bland-Altman analysis, were used to evaluate the proposed 'imageless' deep learning model in reference to U-net and fully convolutional neural network (FCNN) models based on conventional image reconstruction and segmentation.Main results. With the same raw data, the 'imageless' deep learning model gave a lower MAE ('imageless' ≤9.6 ml; 'image-based' ≥12.1 ml), a higher correlation coefficient ('imageless' ≥0.75; 'image-based' ≤0.51) and smaller measurement difference ranges in Bland-Altman analysis ('imageless' ≤23.1 ml; 'image-based' ≥33.8 ml). To achieve comparable performance, the 'imageless' deep learning model needed 2/3 of the raw data used in image reconstruction for U-net and FCNN models, indicating there was a gain in imaging acceleration for real-time CMR.Significance. We have demonstrated a novel deep learning framework that can provide reliable volumetric measurements from real-time CMR raw data without image reconstruction. This 'imageless' approach to real-time volumetric measurements will improve the quantitative assessment of cardiac function in clinical cardiology.