Spinal injections for back-pain management are frequently carried out in hospitals and radiological clinics. Currently, these procedures are performed under fluoroscopy or CT guidance in specialized interventional radiology facilities, and thus incur a major financial burden on the healthcare system. Additionally, the current practice exposes patients and surgeons to X-ray radiation. The use of US for image guided navigation of the spine would greatly reduce the exposure of both the patient and the physician to ionizing radiation and allow the procedure to be performed outside of a specialized facility. However, US as the sole guidance modality has its own challenges. In particular, due to the significant level of occlusion in spinal US images, it can be difficult to accurately identify the appropriate injection site. Here, a groupwise US to CT registration algorithm for guiding percutaneous spinal interventions is presented. In our registration methodology, each vertebra in CT is treated as a sub-volume and transformed individually. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. The sub-volumes are then reconstructed into a single volume. In each iteration of registration, an US image is simulated from the reconstructed CT volume and an intensity-based similarity metric with the real US image is calculated. Validation studies are performed on datasets from a lamb cadaver, five patient-based phantoms designed to preserve realistic curvatures of the spine and a sixth patient-based phantom where the curvature of the spine is changed between preoperative and intraoperative imaging. For datasets where the spine curve between two imaging modalities was artificially perturbed, the proposed methodology was able to register initial misalignments of up to 20 mm with a success rate of 95%. For the phantom with a physical change in the curvature of the spine introduced between the US and CT datasets, the registration success rate was 98.5%. Finally, the registration success rate for the lamb cadaver with soft tissue information was 87%. The results demonstrate that our algorithm robustly registers US and CT datasets of the spine, regardless of a change in the patients pose between preoperative and intraoperative image acquisitions. / Thesis (Master, Computing) -- Queen's University, 2009-11-27 13:48:33.288

The subject of this dissertation is the calculation and visualization of intraoperative measurement uncertainty in computer-assisted surgical procedures. Error is the difference between the observed or measured value and the true value (called ground-truth) of a quantity. Uncertainty is the unknown difference between the measured and true values, and exists in the absence of knowledge of ground truth. If one has an algorithm for computing the ground truth then one can get an accurate estimate of error. However, in computer-assisted surgery, the ground truth is often unknown. The introduction of error to surgical procedures is inevitable: it cannot be avoided by simply taking very careful measurements, providing more accurate algorithms, or by improving instrument calibration. One can only reduce errors as much as reasonably possible, calculate a reliable estimate of the uncertainty, and provide a meaningful way to convey this uncertainty information to clinicians. In this dissertation, I demonstrate that the visualization of registration uncertainty improves surgical navigation and that real-time computation of intraoperative measurement uncertainty is possible. In an extensive user study of surgeons and surgical residents, I compare methods of visualizing intraoperative uncertainty and determine that there are several methods of effectively conveying uncertainty in surgical navigation. / Thesis (Ph.D, Computing) -- Queen's University, 2010-01-25 16:33:26.755

Computer assisted surgery uses a collection of different techniques including but not limited to: CT-guided, fluoroscopy-guided, and ultrasound-guided imaging which allows medical staff to view bony anatomy of a patient in relation to surgical tools on a computer screen. By providing this visual data to surgeons less invasive surgeries can be performed on a patient's fractured scaphoid. The data required for a surgeon to perform a minimally invasive surgery while looking only at a computer screen, and not directly at a patient's anatomy, will be provided by CT and ultrasound data. We will discuss how ultrasound and CT data can be used together to allow a minimally invasive surgery of the scaphoid to be performed. In this thesis we will explore two techniques of registering segmented ultrasound images to CT data; an Iterative Closest Point (ICP) approach, and an Unscented Kalman Filter-based Registration (UKF). We use two different ultrasound segmentation methods; a semi-automatic segmentation, and a Bayesian segmentation technique. The segmented ultrasound data is then registered to a CT volume. The success or failure of the registrations is measured by the error calculated in mapping the corresponding land- marks to one another and calculating the target registration error. The results show that the Unscented Kalman Filter-based registration using the Bayesian segmentation of ultrasound images has the least registration error, and has the most robustness to error in initial alignment of the two data sets. / Thesis (Master, Computing) -- Queen's University, 2010-05-28 11:17:31.934

Spinal needle injections for back pain management are frequently carried out in hospitals and radiological clinics. Currently, these procedures are performed under fluoroscopy or CT guidance in specialized interventional radiology facilities. As an alternative, the use of inexpensive ultrasound image guidance promises to improve the efficacy and safety of these procedures. We propose to eliminate or reduce the need for ionizing radiation, by creating and registering a statistical shape model of the lumbar vertebrae to 3D ultrasound volumes of patient, using a groupwise registration algorithm. From a total of 35 patient CT volumes, a statistical shape model of the L2, L3 and L4 vertebrae is built, including the mean shape, and principal modes of variation. The statistical shape model is registered to the 3D ultrasound by interchangeably optimizing the model parameters and their relative poses. We also use a biomechanical model to constrain the relative motion of the models throughout the registration process. Validation is performed on three tissue mimicking-phantoms designed to preserve realistic curvature of the spine. We compare pairwise and groupwise registration of the statistical shape model of the spine and demonstrate that clinically acceptable mean target error registration of 2.4 mm can be achieved with the proposed method. Registration results also show that the groupwise registration outperforms the pairwise in terms of success rate. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2010-09-27 20:08:01.828

2D-3D registration is one of the underpinning technologies that enables image-guided intervention in computer-assisted orthopaedic surgery (CAOS). Preoperative 3D images and surgical plans need to be mapped to the patient in the operating room before they can be used to augment the surgical intervention, and this task is generally fulfilled by using 2D-3D registration which spatially aligns a preoperative 3D image to a set of intraoperative fluoroscopic images. The key problem in 2D-3D registration is to define an accurate similarity metric between the 2D and 3D data, and choose an appropriate optimization algorithm. Various similarity metrics and optimization algorithms have been proposed for 2D-3D registration; however, current techniques have several critical limitations. First, a good initial guess - usually within a few millimetres from the true solution - is required, and such capture range is often not wide enough for clinical use. Second, for currently used optimization algorithms, it is difficult to achieve a good balance between the computation efficiency and registration accuracy. Third, most current techniques register a 3D image of a single bone to a set of fluoroscopic images, but in many CAOS procedures, such as a multi-fragment fracture treatment, multiple bone pieces are involved. In this thesis, research has been conducted to investigate the above problems: 1) two new registration techniques are proposed that use recently developed optimization techniques, i.e. Unscented Kalman Filter (UKF) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to improve the capture range for the 2D-3D registration problem; 2) a multiple-object 2D-3D registration technique is proposed that simultaneously aligns multiple 3D images of fracture fragments to a set of fluoroscopic images of fracture ensemble; 3) a new method is developed for fast and efficient construction of anatomical atlases; and 4) a new atlas-based multiple-object 2D-3D registration technique is proposed to aid fracture reduction in the absence of preoperative 3D images. Experimental results showed that: 1) by using the new optimization algorithms, the robustness against noise and outliers was improved, and the registrations could be performed more efficiently; 2) the simultaneous registration of multiple bone fragments could achieve a clinically acceptable global alignment among all objects with reasonable computation cost; and 3) the new atlas construction method could construct and update intensity atlases accurately and efficiently; and 4) the use of atlas in multiple-object 2D-3D registration is feasible. / Thesis (Ph.D, Computing) -- Queen's University, 2011-09-28 10:58:04.406

Prostate brachytherapy, a treatment for prostate cancer, was a procedure that typically involved placing radioactive sources in a cancerous prostate using percutaneous needles. The placement of these sources determined the dose that the prostate and healthy tissues surrounding it received. However, because a needle could bend, tissue could deform, and a patient could move, each source may have been displaced from its planned position. This source misplacement could later cause some cancer to be spared or healthy organs to be further damaged. To better understand patterns of source misplacement, and eventually reduce the phenomenon, this work matched and registered implanted sources with their planned positions. Each implant was registered to its plan using a sequence of four successive registrations. A rough initial registration was first found, using features known in the planned dataset and estimated from the implanted dataset. Second, subsets of sources were reconstructed in the implanted dataset. The implanted sources were next matched to the planned sources using the subsets as constraints. Finally, the optimal rigid transformation between the implants and the plan was found. The algorithm was tested on both simulated and clinical datasets. Simulations placed limits on how properties of the subsets affected registration accuracy. When tested on 9 clinical datasets, the algorithm found 100% of correct plan-implant source matches within seconds on commonly available computers. When the implanted strands were reconstructed as sine waves, 97% of t strands had an amplitude of less than 2mm. The clinical accuracy result generally agreed with simulation: subsets with amplitudes less than 2mm were expected to produce an accuracy >90%. The high accuracy of the algorithm may enable its use in finding patterns of source misplacement. The fast run-time of the algorithm may additionally make it useful for use in a clinical setting. / Thesis (Master, Computing) -- Queen's University, 2011-12-23 13:28:07.348

Light Detection And Ranging (LiDAR) mapping has been emerging over the past few years as a mainstream tool for the dense acquisition of three dimensional point data. Besides the conventional mapping missions, LiDAR systems have proven to be very useful for a wide spectrum of applications such as forestry, structural deformation analysis, urban mapping, and reverse engineering. The wide application scope of LiDAR lead to the development of many laser scanning technologies that are mountable on multiple platforms (i.e., airborne, mobile terrestrial, and tripod mounted), this caused variations in the characteristics and quality of the generated point clouds. As a result of the increased popularity and diversity of laser scanners, one should address the heterogeneous LiDAR data post processing (i.e., registration and segmentation) problems adequately. Current LiDAR integration techniques do not take into account the varying nature of laser scans originating from various platforms. In this dissertation, the author proposes a methodology designed particularly for the registration and segmentation of heterogeneous LiDAR data. A data characterization and filtering step is proposed to populate the points’ attributes and remove non-planar LiDAR points. Then, a modified version of the Iterative Closest Point (ICP), denoted by the Iterative Closest Projected Point (ICPP) is designed for the registration of heterogeneous scans to remove any misalignments between overlapping strips. Next, a region-growing-based heterogeneous segmentation algorithm is developed to ensure the proper extraction of planar segments from the point clouds. Validation experiments show that the proposed heterogeneous registration can successfully align airborne and terrestrial datasets despite the great differences in their point density and their noise level. In addition, similar testes have been conducted to examine the heterogeneous segmentation and it is shown that one is able to identify common planar features in airborne and terrestrial data without resampling or manipulating the data in any way. The work presented in this dissertation provides a framework for the registration and segmentation of airborne and terrestrial laser scans which has a positive impact on the completeness of the scanned feature. Therefore, the derived products from these point clouds have higher accuracy as seen in the full manuscript.

No description available.

The first part of this work presents an accurate analysis of the most relevant 3D registration techniques, including initial pose estimation, pairwise registration and multiview registration strategies. A new classification has been proposed, based on both the applications and the approach of the methods that have been discussed.The main contribution of this thesis is the proposal of a new 3D multiview registration strategy. The proposed approach detects revisited regions obtaining cycles of views that are used to reduce the inaccuracies that may exist in the final model due to error propagation. The method takes advantage of both global and local information of the registration process, using graph theory techniques in order correlate multiple views and minimize the propagated error by registering the views in an optimal way. The proposed method has been tested using both synthetic and real data, in order to show and study its behavior and demonstrate its reliability. / La primera part d'aquest treball presenta una anàlisi acurada de les tècniques de registre 3D es rellevants, incloent tècniques d'estimació de la posició inicial, registre pairwise i registre entre múltiples vistes. S'ha proposat una nova classificació de les tècniques, depenent de les seves aplicacions i de l'estratègia utilitzada.La contribució mes important d'aquesta tesi és la proposta d'un nou mètode de registre 3D utilitzant múltiples vistes. El mètode proposat detecta regions ja visitades prèviament, obtenint cicles de vistes que s'utilitzen per tal de reduir els desalineaments en el model final deguts principalment a la propagació de l'error durant el procés de registre. Aquest mètode utilitza tant informació global com local, correlacionant les vistes mitjançant tècniques de grafs que permeten minimitzar l'error propagat i registrar les vistes de forma òptima. El mètode proposat ha estat provat utilitzant dades sintètiques i reals, per tal de mostrar i analitzar el seu comportament i demostrar la seva eficàcia.

This dissertation addresses the problem of scanning the geometry of real objects and building accurate computer models of those objects. We present a complete system which employs a structured light scanner to acquire 3D views of objects from multiple viewpoints. These multiple views, expressed in a sensor-oriented coordinate system, are then registered into a model-centred coordinate system, before being integrated into a single mesh describing the object's geometry. Line of sight constraints forbid any single view from capturing the entire surface of an object, so multiple scans must be performed. We have developed registration techniques which may register all of the views simultaneously, resulting in a globally optimal solution. Statistical error modeling of the sensor, and the use of these models in the registration process, forms a key part of the research. It is motiviated by the observation that all measurements are subject to some degree of random measurement error. The true values of these errors cannot be determined, however their statistical properties may be modeled. Our registration system utilises these error models to improve registration accuracy, and to allow the accuracy of the registration to be estimated. The resulting system is a flexible platform for 3D data capture and modeling. It may be used in conjunction with the structured light scanner, or 3D data acquired from any other source. We demonstrate this capability with models constructed from sources such as laser range finders and scanning touch probe systems. The contributions of this thesis are as follows: a novel stereo matching algorithm which permits the estimation of stereo disparity as well as the uncertainty in the disparity, development of a practical 3D vision sensor based on structured light techniques, two novel algorithms for performing simultaneous multiple view point set registration, while supporting individual point error models and estimating the uncertainty in the registration solution, a novel algorithm for efficiently solving the multiple view registration problem, and the implementation of a number of existing surface correspondence and reconstruction techniques, permitting the development of an integrated 3D vision system for capturing and modeling 3D objects.