A Centerline-Based Model Morphing Algorithm for Patient-Specific Finite Element Modeling of the Left Ventricle

Abstract

Abstract-Goal: Hexahedral automatic model generation is a recurrent problem in computer vision and computational biomechanics. It may even become a challenging problem when one wants to develop a patient-specific finite-element (FE) model of the left ventricle (LV), particularly when only low resolution images are available. In the present study, a fast and efficient algorithm is presented and tested to address such a situation. Methods: A template FE hexahedral model was created for a LV geometry using a General Electric (GE) ultrasound (US) system. A system of centerline was considered for this LV mesh. Then, the nodes located over the endocardial and epicardial surfaces are respectively projected from this centerline onto the actual endocardial and epicardial surfaces reconstructed from a patient's US data. Finally, the position of the internal nodes is derived by finding the deformations with minimal elastic energy. This approach was applied to eight patients suffering from congestive heart disease. A FE analysis was performed to derive the stress induced in the LV tissue by diastolic blood pressure on each of them. Results: Our model morphing algorithm was applied successfully and the obtained meshes showed only marginal mismatches when compared to the corresponding US geometries. The diastolic FE analyses were successfully performed in seven patients to derive the distribution of principal stresses. Conclusion: The original model morphing algorithm is fast and robust with low computational cost. Significance: This low cost model morphing algorithm may be highly beneficial for future patient-specific reduced-order modelling of the LV with potential application to other crucial organs.

Abnormal Tissue Zone Detection and Average Active Stress Estimation in Patients with LV Dysfunction

“…Besides, the accuracy of the geometry associated with the generic LV model is greater than the patient-specific geometries, hence, the need to adapt the generic model to each patient. This task is accomplished by a previously developed mesh morphing method, which takes a generic LV mesh and adapts it to the patients' image data [17]. The rest of the methodology consists of a minimization loop on the active tension a f .…”

“…We morphed the reference mesh (refer to Section 2.1) onto the geometry of each patient at ED time step for which US geometries were acquired. For this, endocardial and epicardial nodes of the deformable mesh were projected onto the patient's triangulated surfaces from ad e f i n e dL Vcenterline [17] with rigid and non-rigid transformation methods employing an in-house developed Matlab® code. In order to deform the reference bulk model with intermediate nodes, we used FE elastic rigid body deformation employing the displacement vectors obtained from boundaries projection trajectories [17].…”

“…For this, endocardial and epicardial nodes of the deformable mesh were projected onto the patient's triangulated surfaces from ad e f i n e dL Vcenterline [17] with rigid and non-rigid transformation methods employing an in-house developed Matlab® code. In order to deform the reference bulk model with intermediate nodes, we used FE elastic rigid body deformation employing the displacement vectors obtained from boundaries projection trajectories [17]. For each patient, after application of the mesh morphing method, we employed the pressure values at ES from pressure curves obtained during the medical intervention ( Table 1).…”

“…We applied the mesh morphing method explained in Section 2.3 to construct patient-specific FE meshes for 8 LBBBB patients, shown in the figure by Behdadfar et al [17]. Table 2 shows Figure 5.…”

An Optimized Model for the Local Compression Deformation of Soft Tissue

“…Nowadays, the finite element model [8][9][10], the meshless model [11][12] and the mass-spring model [13][14] are commonly used in virtual surgery training. The finite element model has high precision and adaptability, it can simulate large-scale soft tissue deformation, but its modeling is relatively complex, and it requires a lot of complicated numerical calculations, not suitable for real-time performance [15][16]. Wei et al [17] proposed a method for rendering the human head stiffness based on the finite element model.…”