J. M. Esnaola Capa. Co-Registration of Multimodal Datasets in Patient-Specific Computational Models to Correlate Fibrotic Area and Electrograms’ Signals. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2020
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia that affects up to 2% of the general population. Recent studies have shown that unhealthy tissue substrate (e.g. fibrotic tissue) can be responsible for initiating and maintaining this cardiopathology. Various techniques are available to assess atrial anatomy (CT scans), detect fibrosis (LGE MRI) and provide insights on patients’ electrophysiology (electro-anatomical maps). The aim of this work is to present a way to co-register all this information on a single map as well as develop a user-friendly interface to allow this process to be carried out easily. Thus, to perform the co-registration of these maps, a pre-alignment of both maps is needed. This could be done fully automatically using a PCA method or manually choosing landmarks which correspond to the same locations in both anatomies. From this point, the source map is iteratively deformed to match the target geometry within a tolerance and, therefore, a map is obtained containing all the information from the multimodal datasets related to a patient. Once this result map is obtained, a mesh processing is performed in order to build a tetrahedral model and attach the information needed to run simulations. Thus, through this process a volumetric model is achieved in which the fiber orientations are included. Consequently, together with the fibrotic tissue already present in the original MRI map and a smoothed LAT signal incorporated through the co-registration method, it is possible to carry out personalized simulations. Finally, in-silico electrograms (EGMs) are computed and analyzed. Regarding the different pre-alignment methods, it was verified that establishing five landmarks in the atrial anatomy as a reference provided the best results. Moreover, from the results of the simulations it was checked that the patient-specific model was accurately built and corresponded adequately to the real physiological situation of the patient. In addition, EGMs revealed the presence of fragmentation as well as a significant reduction in the amplitude in areas of fibrotic tissue, agreeing in this way with what is stated in the literature.