Li, ZhenZhenLiContini, LetiziaLetiziaContiniIppoliti, Alessandro MariaAlessandro MariaIppolitiBastianelli, ElenaElenaBastianelliDe Marco, FedericoFedericoDe MarcoDankelman, JennyJennyDankelmanDe Momi, ElenaElenaDe Momi2024-07-192024-07-192024-07-01IEEE Sensors Journal 24 (13): 21750-21761 (2024-07-01)https://hdl.handle.net/11420/48441Endovascular interventions are minimally invasive procedures that utilize the vascular system to access anatomical regions deep within the body. Image-guided assistance provides valuable real-time information about the dynamic state of the vascular environment. However, the reliance on intraoperative 2-D fluoroscopy images limits depth perception, prompting the demand for intraoperative 3-D imaging. Existing image registration methods face difficulties in accurately incorporating tissue deformations compared to the preoperative 3-D model, particularly in a weakly supervised manner. Additionally, reconstructing deformations from 2-D to 3-D space and presenting this intraoperative model visually to clinicians poses further complexities. To address these challenges, this study introduces a novel deformable model-to-image registration framework using deep learning. Furthermore, this research proposes a visualization method through augmented reality to guide endovascular interventions. This study utilized image data collected from nine patients who underwent transcatheter aortic valve implantation (TAVI) procedures. The registration results in 2-D indicate that the proposed deformable model-to-image registration framework achieves a modified dice similarity coefficient (MDSC) value of 0.89±0.02 and a penalization of deformations in spare space (PDSS) value of 0.04±0.01, offering an improvement of 3.5%-98.6% over the state-of-the-art image registration approach. Additionally, the accuracy of registration in 3-D was evaluated using a dataset obtained from an intervention simulator, resulting in a mean absolute error (MAE) of 1.51±1.02 mm within the region of interest. Overall, the study validates the feasibility and accuracy of the proposed weakly supervised deformable model-to-image registration framework, demonstrating its potential to provide intraoperative 3-D imaging as intervention assistance in dynamic vascular environments.en1558-1748IEEE sensors journal2024132175021761IEEEhttps://creativecommons.org/licenses/by/4.0/Augmented realitydeep learningdeformationimage-guided interventionsimage registrationComputer Science, Information and General Works::004: Computer SciencesTechnology::610: Medicine, HealthDeformable model-to-image registration toward augmented reality-guided endovascular interventionsJournal Article10.15480/882.1315610.1109/JSEN.2024.340253910.15480/882.13156Journal Article