Structural heart disease (SHD) is a recently recognized subset of heart disease, and minimally invasive, transcatheter treatments for SHD rely heavily on guidance from multiple imaging modalities. Mentally integrating the information from these images can be challenging during procedures and can take up time and increase radiation exposure. This study used the free Unity graphics engine and tailored LabVIEW and Python algorithms, along with deep learning, to merge echocardiography, CT-derived 3D heart models, and fiber optic shape sensing data with fluoroscopic imaging. Tests were performed on a patient specific ballistic gel heart model. This is the first attempt at fusing the above four imaging modalities together and can pave the way for more advanced guidance techniques in the future.
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