A fully automatic service for the geometrical personalization of cardiac ventricular meshes fulfilled accuracy and quality requirements in 87% of 255 cases.
A fully automatic cloud-based service successfully generates personalized ventricular cardiac meshes from segmented images, fulfilling quality requirements in 87% of cases.
Computational cardiac physiology has great potential to improve the management of cardiovascular diseases. One of the main bottlenecks in this field is the customization of the computational model to the anatomical and physiological status of the patient. We present a fully automatic service for the geometrical personalization of cardiac ventricular meshes with high-order interpolation from segmented images. The method is versatile (able to work with different species and disease conditions) and robust (fully automatic results fulfilling accuracy and quality requirements in 87% of 255 cases). Results also illustrate the capability to minimize the impact of segmentation errors, to overcome the sparse resolution of dynamic studies and to remove the sometimes unnecessary anatomical detail of papillary and trabecular structures. The smooth meshes produced can be used to simulate cardiac function, and in particular mechanics, or can be used as diagnostic descriptors of anatomical shape by cardiologists. This fully automatic service is deployed in a cloud infrastructure, and has been made available and accessible to the scientific community.
Lamata et al. (Wed,) conducted a other in cardiovascular diseases (n=255). Automatic service for geometrical personalization of cardiac ventricular meshes was evaluated on Fulfilling accuracy and quality requirements. A fully automatic service for the geometrical personalization of cardiac ventricular meshes fulfilled accuracy and quality requirements in 87% of 255 cases.
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