An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images

We have developed an algorithm for the rigid-body registration of a CT volume to a set of C-arm images. The algorithm uses a gradient-based iterative minimization of a least-squares measure of dissimilarity between the C-arm images and projections of the CT volume. To compute projections, we use a novel method for fast integration of the volume along rays. To improve robustness and speed, we take advantage of a coarse-to-fine processing of the volume/image pyramids. To compute the projections of the volume, the gradient of the dissimilarity measure, and the multiresolution data pyramids, we use a continuous image/volume model based on cubic B-splines, which ensures a high interpolation accuracy and a gradient of the dissimilarity measure that is well defined everywhere. We show the performance of our algorithm on a human spine phantom, where the true alignment is determined using a set of fiducial markers

Robustness and Accuracy of Feature-Based Single Image 2-D–3-D Registration Without Correspondences for Image-Guided Intervention

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Reconstruction of Patient-Specific Bone Models from X-Ray Radiography

The availability of a patient‐specific bone model has become an increasingly invaluable addition to orthopedic case evaluation and planning [1]. Utilized within a wide range of specialized visualization and analysis tools, such models provide unprecedented wealth of bone shape information previously unattainable using traditional radiographic imaging [2]. In this work, a novel bone reconstruction method from two or more x‐ray images is described. This method is superior to previous attempts in terms of accuracy and repeatability. The new technique accurately models the radiological scene in a way that eliminates the need for expensive multi‐planar radiographic imaging systems. It is also flexible enough to allow for both short and long film imaging using standard radiological protocols, which makes the technology easily utilized in standard clinical setups

Spline projection-based volume-to-image registration

This thesis focuses on the rigid-body registration of a three-dimensional model of an object to a set of its two-dimensional projections. The main contribution is the development of two registration algorithms that use a continuous model of the volume based on splines, either in the space domain or in the frequency domain. This allows for a well-defined gradient of the dissimilarity measure, which is a necessary condition for efficient and accurate registration. The first part of the thesis contains a review of the literature on volume-to- image registration. Then, we discuss data interpolation in the space domain and in the frequency domain. The basic concepts of our registration strategy are given in the second part of the thesis. We present a novel one-step approach for fast ray casting to simulate space-based volume projections. We also discuss the use of the central-slice theorem to simulate frequency-based volume projections. Then, we consider the question of the registration robustness. To improve the robustness of the space-based approach, we apply a multiresolution optimization strategy where spline-based data pyramids are processed in coarse-to-fine fashion, which improves speed as well. To improve the robustness of the frequency-based registration, we apply a coarse-to-fine strategy that involves weights in the frequency domain. In the third part, we apply our space-based algorithm to computer-assisted orthopedic surgery while adapting it to the perspective projection model. We show that the registration accuracy achieved using the orthopedic data is consistent with the current standards. Then, we apply our frequency-based registration to three-dimensional electron-microscopy application. We show that our algorithm can be used to obtain a refined solution with respect to currently available algorithms. The novelty of our approach is in dealing with a continuous space of geometric parameters, contrary to the standard methods which deal with quantized parameters. We conclude that our continuous parameter space leads to better registration accuracy. Last, we compare the performance of the frequency-based algorithm with that of the space-based algorithm in the context of electron microscopy. With these data, we observe that frequency-based registration algorithm outperforms the space-based one, which we attribute to the suitability of interpolation in the frequency domain when dealing with strictly space-limited data

ADVANCED INTRAOPERATIVE IMAGE REGISTRATION FOR PLANNING AND GUIDANCE OF ROBOT-ASSISTED SURGERY

Robot-assisted surgery offers improved accuracy, precision, safety, and workflow for a variety of surgical procedures spanning different surgical contexts (e.g., neurosurgery, pulmonary interventions, orthopaedics). These systems can assist with implant placement, drilling, bone resection, and biopsy while reducing human errors (e.g., hand tremors and limited dexterity) and easing the workflow of such tasks. Furthermore, such systems can reduce radiation dose to the clinician in fluoroscopically-guided procedures since many robots can perform their task in the imaging field-of-view (FOV) without the surgeon. Robot-assisted surgery requires (1) a preoperative plan defined relative to the patient that instructs the robot to perform a task, (2) intraoperative registration of the patient to transform the planning data into the intraoperative space, and (3) intraoperative registration of the robot to the patient to guide the robot to execute the plan. However, despite the operational improvements achieved using robot-assisted surgery, there are geometric inaccuracies and significant challenges to workflow associated with (1-3) that impact widespread adoption. This thesis aims to address these challenges by using image registration to plan and guide robot- assisted surgical (RAS) systems to encourage greater adoption of robotic-assistance across surgical contexts (in this work, spinal neurosurgery, pulmonary interventions, and orthopaedic trauma). The proposed methods will also be compatible with diverse imaging and robotic platforms (including low-cost systems) to improve the accessibility of RAS systems for a wide range of hospital and use settings. This dissertation advances important components of image-guided, robot-assisted surgery, including: (1) automatic target planning using statistical models and surgeon-specific atlases for application in spinal neurosurgery; (2) intraoperative registration and guidance of a robot to the planning data using 3D-2D image registration (i.e., an “image-guided robot”) for assisting pelvic orthopaedic trauma; (3) advanced methods for intraoperative registration of planning data in deformable anatomy for guiding pulmonary interventions; and (4) extension of image-guided robotics in a piecewise rigid, multi-body context in which the robot directly manipulates anatomy for assisting ankle orthopaedic trauma

Probabilistic Feature-Based Registration for Interventional Medicine

The need to compute accurate spatial alignment between multiple representations of patient anatomy is a problem that is fundamental to many applications in computer-integrated interventional medicine. One class of methods for computing such alignments is feature-based registration, which aligns geometric information of the shapes being registered, such as salient landmarks or models of shape surfaces. A popular algorithm for surface-based registration is the Iterative Closest Point (ICP) algorithm, which treats one shape as a cloud of points that is registered to a second shape by iterating between point-correspondence and point-registration phases until convergence. In this dissertation, a class of "most likely point" variants on the ICP algorithm is developed that offers several advantages over ICP, such as high registration accuracy and the ability to confidently assess the quality of a registration outcome. The proposed algorithms are based on a probabilistic interpretation of the registration problem, wherein the point-correspondence and point-registration phases optimize the probability of shape alignment based on feature uncertainty models rather than minimizing the Euclidean distance between the shapes as in ICP. This probabilistic framework is used to model anisotropic errors in the shape measurements and to provide a natural context for incorporating oriented-point data into the registration problem, such as shape surface normals. The proposed algorithms are evaluated through a range of simulation-, phantom-, and clinical-based studies, which demonstrate significant improvement in registration outcomes relative to ICP and state-of-the-art methods