A COMPUTATIONAL FRAMEWORK FOR PATIENT–SPECIFIC MODELING OF THE CARDIOVASCULAR SYSTEM

During the last decade there has been an increase in the number of scientific papers dedicated to applying computational techniques to model the main physical phenomena in the cardiovascular system. This trend was a direct consequence of the growth of the computational power, what came alongside with the development of more complex models, techniques and algorithms, all those capable of pre‐processing data, performing simulations and post‐processing results in an even more effective and efficient fashion. Fairly strong evidence about the importance of these developments has been presented throughout several landmark works [4,8]. As aforesaid, effectiveness and efficiency in handling large data-sets obtained in addition to the highly complex numerical simulations and the possibility of correlating mechanical aspects with cardiovascular diseases have given rise to new paradigms in the computational modeling. Hence, promising results have brought to light the need to perform patient‐specific modeling. Such kind of patient-oriented modeling engages several challenging stages like medical image processing, mesh generation, FEM data pre-processing, numerical simulation, FEM data post-processing and visualization. Nonetheless, there still is a lack of available computational tools devoted to handle the whole process in a single environment. This kind of unifier computational system is crucial not only to provide a common language capable of holding all the important steps in the modeling process, but also as a tool to speed up research time, helping to stay ahead of the field.