Co-Registration of Multimodal Datasets in Patient-Specific Computational Models to Correlate Fibrotic Area and Electrograms' Signals
Most of the leading research groups are aiming towards developing personalized computational modeling starting from clinical data. This model helps to better understand the patients' cardiac diseases and suggest a tailor-made treatment reducing the risk of recurrence.
Atrial fibrillation (AF) is the most common arrhythmia and computer models have proved to be helpful tools to unveil AF mechanisms and to personalize therapies. These models need to be fed with clinical data and, in most of the cases, this means integrating spatial information about atrial physiology and anatomy in a single patient from multimodal datasets. Different techniques give various important information, for example, we will get the atrial anatomy from the segmentation of CT scans, approximate the fibrotic tissue location from LGE MRI images and we get an insight of the patient's atrial electrophysiology from intracardiac mapping.
The aim of this project is to implement a method to co-register multimodal datasets coming from the same patient and integrate all the information in a computational model. In our case, we are interested in co-registering the atrial anatomy, from CT and LGE MRI images, with the CARTO maps. This patient specific model will therefore include atrial anatomy, fibrotic tissue distribution and electrophysiological properties.
In-silico electrograms will be calculated to validate the model with clinical data. Additionally, a signal analysis will be performed to correlate electrograms from CARTO and substrate remodeling due to fibrosis.