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About

Alejandro Sisniega is a leading researcher in medical imaging and image-guided interventions, with a focus on advancing cone-beam CT (CBCT) and real-time 3D reconstruction for neurosurgery and interventional radiology. His work bridges the gap between computational imaging and clinical translation, addressing critical challenges in accuracy, speed, and deformation compensation. Sisniega’s major contributions include developing a real-time 3D video reconstruction method using simultaneous localization and mapping (SLAM) for neuroendoscopic guidance, which compensates for brain deformation during transventricular neurosurgery—a breakthrough that enhances precision in deep-brain targeting. He also pioneered accelerated model-based iterative reconstruction (MBIR) for CBCT using morphological pyramids, achieving superior noise-resolution tradeoffs with reduced computational burden, and devised targeted deformable motion compensation algorithms for vascular interventional imaging. His work on calibration and registration of freehand video-guided surgical drills for orthopaedic trauma minimizes radiation exposure and improves guidewire placement accuracy. With over 25 citations across his top papers, Sisniega’s research has been recognized for its translational impact, including the development of a multi-source semi-stationary CT system for brain imaging that integrates adaptive scatter estimation and diffusion models. His achievements underscore a commitment to making image-guided procedures safer, faster, and more reliable.