Deformable Image Registration for Surgical Guidance using Intraoperative Cone-Beam CT

deformable image registration;Demons algorithm;symmetric diffeomorphism;multimodality image registration;MIND descriptor;CT;CBCT;MRI;image-guided surgery;Biomedical Engineering

Reaungamornrat, SureeratPrince, Jerry L. ;

Johns Hopkins University

关键词: deformable image registration;    Demons algorithm;    symmetric diffeomorphism;    multimodality image registration;    MIND descriptor;    CT;    CBCT;    MRI;    image-guided surgery;    Biomedical Engineering;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/40825/REAUNGAMORNRAT-DISSERTATION-2017.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

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 localization and visualization of the surgical plan relative to the intraoperative state of patient and thereby improve surgical precision and safety.The research reported in this dissertation seeks to develop advanced forms of DIR for key image guidance applications in otolaryngology and spinal neurosurgery. For transoral robotic base-of-tongue surgery, a hybrid DIR method integrating a surface-based initialization and a shape-driven Demons registration 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 dilation 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).Overall, the methods demonstrated registration accuracy comparable to the image voxel size, robustness against typical forms of image quality degradation (image noise, anisotropic voxel size, and intensity nonuniformity), insensitivity to a broad range of parameter settings, and the ability to resolve locally coherent deformation without introduction of unrealistic changes of anatomical structures. 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.