Trimodality image registration of ultrasound, cardiac computed tomography, and magnetic resonance imaging for transcatheter aortic valve implantation and replacement image guidance

Background This study presents a registration system that integrates preoperative cardiac Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) volume data with 2D Ultrasound (US) images of the aortic valve. The registration process aims to combine three different imaging modalities (US-CT-MRI) to improve the accuracy of diagnosing aortic valve disorders and provide surgical guidance during the implantation and replacement of the transcatheter aortic valve. Methods The registration framework involves two key components: temporal synchronization and spatial registration. Temporal synchronization allows the identification of frames in the CT and MRI volume that correspond to the same cardiac phase as the US time-series data. For spatial registration, an intensity-based normalized mutual information method combined with a pattern search optimization algorithm is used to produce interpolated cardiac CT and MRI images that align with the US image. Results The accuracy of the trimodality registration method is evaluated using the Dice similarity coefficient. The obtained coefficients are 0.92 ± 0.05 and 0.92 ± 0.04 for comparisons between US-CT and US-MRI, respectively, in short-axis "Mercedes Benz" sign views. The Hausdorff distance, which measures the dissimilarity between two sets of points, was found to be 1.49 ± 0.20 and 1.49 ± 0.19 for both US-CT and US-MRI pairings, respectively. Notably, these values are comparable to the precision achieved when an expert manually registers each image. Conclusions The proposed registration technique demonstrates excellent accuracy in enhancing image-guided systems for aortic valve surgical guidance. It shows promise in the context of Transcatheter Aortic Valve Implantation (TAVI) and Transcatheter Aortic Valve Replacement (TAVR) procedures. The successful integration of US-CT-MRI imaging modalities enables better diagnosis and surgical planning for aortic valve disorders, potentially leading to improved patient outcomes in these procedures.