7 pp. / All Arizona wells must be registered with the ADWR. Domestic private well are not overseen or regulated by ADEQ. The well owner has the responsibility for maintaining and ownership status of the well and is also responsible for its operating performance and for checking its water quality. The purpose of this publication is to assist well owners to check the registration of their well by searching the ADWR imaged records files, and how to keep well installation and maintenance records current.

Rigid-body registration is an important research area with major applications in computer-assisted and image-guided surgery. In these surgeries, often the relationship between the preoperative and intraoperative images taken from a patient must be established. This relationship is computed through a registration process, which finds a set of transformation parameters that maps some point fiducials measured on a patient anatomy to a preoperative model. Due to point measurement error caused by medical measurement instruments, the estimated registration parameters are imperfect and this reduces the accuracy of the performed registrations. Medical measurement instruments often perturb the collected points from the patient anatomy by heterogeneous noise. If the noise characteristics are known, they can be incorporated in the registration algorithm in order to more reliably and accurately estimate the registration parameters and their variances. Current techniques employed in rigid-body registration are primarily based on the well-known Iterative Closest Points (ICP) algorithm. Such techniques are susceptible to the existence of noise in the data sets, and are also very sensitive to the initial alignment errors. Also, the literature offers no analytical solution on how to estimate the accuracy of the performed registrations in the presence of heterogenous noise. In an effort to alleviate these problems, we propose and validate various novel registration techniques based on the Unscented Kalman Filter (UKF) algorithm. This filter is generally employed for analyzing nonlinear systems corrupted by additive heterogenous Gaussian noise. First, we propose a new registration algorithm to fit two data sets in the presence of arbitrary Gaussian noise, when the corresponding points between the two data sets are assumed to be known. Next, we extend this algorithm to perform surface-based registration, where point correspondences are not available, but the data sets are roughly aligned. A solution to multi-body point and surface-based registration problem is then proposed based on the UKF algorithm. The outputs of the proposed UKF registration algorithms are then utilized to estimate the accuracy of the performed registration. For the first time, novel derivations are presented that can estimate the distribution of registration error at a target in the presence of an arbitrary Gaussian noise. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-09-28 07:25:38.229

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.

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