Accurate localization of the surgical target and adjacent normal anatomy is essential to safe and effective surgery. Preoperative computed tomography (CT) and / or magnetic resonance (MR) images offer exquisite visualization of anatomy and a valuable basis for surgical planning. Multimodality deformable image registration (DIR) can be used to bring preoperative images and planning information to a geometrically resolved anatomical context presented in intraoperative CT or cone-beam CT (CBCT). Such capability promises to improve reckoning of the surgical plan relative to the intraoperative state of patient and thereby improve surgical precision and safety. This talk focuses on advanced DIR developed for key image guidance applications in otolaryngology and spinal neurosurgery. For transoral robotic based-of-tongue surgery, a hybrid DIR method integrating a surface-based initialization and a shape-driven Demons algorithm with multi-scale optimization was developed to resolve the large deformation associated with the operative setup, with gross deformation > 30 mm. The method yielded registration accuracy of ~1.7 mm in cadaver studies. For orthopaedic spine surgery, a multiresolution free-form DIR method was developed with constraints designed to maintain the rigidity of bones within otherwise deformable transformations of surrounding soft tissue. Validation in cadaver studies demonstrated registration accuracy of ~1.4 mm and preservation of rigid-body morphology (near-ideal values of dilatation and shear) and topology (lack of tissue folding / tearing). For spinal neurosurgery, where preoperative MR is the preferred modality for delineation of tumors, the spinal cord, and nervous and vascular systems, a multimodality DIR method was developed to realize viscoelastic diffeomorphisms between MR and intraoperative CT using a modality-independent-neighborhood descriptor (MIND) and a Huber metric in a multiresolution Demons optimization. Clinical studies demonstrated sub-voxel registration accuracy (< 2 mm) and diffeomorphism of the estimated deformation (sub-voxel invertibility error = 0.001 mm and positive Jacobian determinants). These promising advances could facilitate more reliable visualization of preoperative planning data within up-to-date intraoperative CT or CBCT in support of safer, high-precision surgery.
Sureerat Reaungamornrat is a PhD candidate in Computer Science at Johns Hopkins University working under the supervision of Prof. Jeffrey H. Siewerdsen and Russell H. Taylor. Her research focuses on the development of new deformable 3D image registration methods for image-guided interventions. Her work earned the 2014 and 2016 SPIE Young Scientist awards and the 2016 Robert Wagner All-Conference Best Student Paper award. She received her Master of Science in Engineering from Johns Hopkins University for the work on novel surgical tracking configuration for mobile C-arm CBCT.