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The success of transcatheter aortic valve implantation (TAVI) is highly dependent on the prediction of the interaction between the prosthesis and the aortic root anatomy. The simulation of the surgical procedure may be useful to guide artificial valve selection and delivery, nevertheless the introduction of simulation models into the clinical workflow is often hindered by model complexity and computational burden. To address this point, we introduced a patient-specific mass-spring model (MSM) with viscous damping, as a good trade-off between simulation accuracy and time-efficiency. The anatomical model consisted of a hexahedral mesh, segmented from pre-procedural patient-specific cardiac computer tomographic (CT) images of the aortic root, including valve leaflets and attached calcifications. Nodal forces were represented by linear-elastic springs acting on edges and angles. A fast integration approach based on the modulation of nodal masses was also tested. The model was validated on seven patients, comparing simulation results with post-procedural CT images with respect to calcification and aortic wall position. The validation showed that the MSM was able to predict calcification displacement with an average accuracy of 1.72 mm and 1.54 mm for the normal and fast integration approaches, respectively. Wall displacement root mean squared error after valve expansion was about 1 mm for both approaches, showing an improved matching with respect to the pre-procedural configuration. In terms of computational burden, the fast integration approach allowed a consistent reduction of the computational times, which decreased from 36 h to 21.8 min per 100 K hexahedra. Our findings suggest that the proposed linear-elastic MSM model may provide good accuracy and reduced computational times for TAVI simulations, fostering its inclusion in clinical routines.

The integration of electroanatomic maps with highly resolved computed tomography cardiac images plays an important role in the successful planning of the ablation procedure of arrhythmias. In this paper, we present and validate a fully-automated strategy for the registration and fusion of sparse, atrial endocardial electroanatomic maps (CARTO maps) with detailed left atrial (LA) anatomical reconstructions segmented from a pre-procedural MDCT scan. Registration is accomplished by a parameterized geometric transformation of the CARTO points and by a stochastic search of the best parameter set which minimizes the misalignment between transformed CARTO points and the LA surface. The subsequent fusion of electrophysiological information on the registered CT atrium is obtained through radial basis function interpolation. The algorithm is validated by simulation and by real data from 14 patients referred to CT imaging prior to the ablation procedure. Results are presented, which show the validity of the algorithmic scheme as well as the accuracy and reproducibility of the integration process. The obtained results encourage the application of the integration method in post-intervention ablation assessment and basic AF research and suggest the development for real-time applications in catheter guiding during ablation intervention.