Computational modeling is a field that is continuously growing and personalized models need patients' data. Even if several efforts have been done in this direction, most of them required a big amount of human interaction, which is both time consuming and could be a source of error. CT or LGE-MRI image segmentation provides the atrial anatomy as endocardial and epicardial surfaces with an approximate distribution of fibrotic tissue. However, realistic in- silico experiments have to be run on a 3D model including both fibre orientation and heterogeneous tissue properties. The aim of this project is to create an automated pipeline for atrial model generation. The input will be an endocardial and epicardial surface coming from CT or LGE-MRI image segmentation and the output a 3D computational atrial model including fiber orientation. Every block of the pipeline will be evaluated to detect bottlenecks and possible weaknesses. Additionally, the entire pipeline will be tested by outputting 3D atrial models to be used directly in an in-silico experiment to evaluate vulnerability to arrhythmia.
Automatic model generation is still a challenge due to the concatenation of many individual steps needed to personalize an atrial model. The processes to create a volumetric mesh, generate fiber orientation and region tagging are individually implemented. The student needs to create an interface between them to work in a pipeline that outputs a model of the atria ready to be used by openCARP. The student will develop an algorithm which will take as an input the endocardial and epicardial atrial segmentation coming from clinical data to generate a 3D computational model including different regions and fiber orientation. Taking the fibrosis location from the LGE-MRI image, the student will assess the effect of increasing fibrosis density by changing the tissue properties in this area. Using this approach, she/he should be able to correlate the density of fibrosis with the vulnerability to initiate and maintain atrial fibrillation.
- Experience in MATLAB, C++ and Python
- Fluent in spoken and written English