Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems - Drakopoulos, Fotis; Tsolakis, Christos; Angelopoulos, Angelos; Liu, Yixun; Yao, Chengjun; Kavazidi, Kyriaki Rafailia; Foroglou, Nikolaos; Fedorov, Andrey; Frisken, Sarah; Kikinis, Ron; Golby, Alexandra; Chrisochoides, Nikos (Frontiers in Digital Health, 2021)
Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms significantly during surgery, particularly in the presence of tumor resection, Non-Rigid Registration (NRR) of the preoperative image data to the patient is required. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. The use of NRR in immersive environments is critical, and a new way of using mixed reality with ultrasound, MRI, and CT is considered. Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An adaptive physics-based registration method (Adaptive-PBNRR) registers preoperative and intraoperative MRI for each patient, and the results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and a physics-based non-rigid registration (PBNRR) implemented in ITKv4.7.0, upon which Adaptive-PBNRR was based. Three measures aid in assessing the accuracy of the registration methods: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Results: The Adaptive-PBNNR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and 4 times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that Adaptive-PBNRR could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. Conclusions: The Adaptive-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room.