A Study of Image-based C-arm Tracking Using Minimal Fiducials

Image-based tracking of the c-arm continues to be a critical and challenging problem for many clinical applications due to its widespread use in many computer-assisted procedures that rely upon its accuracy for further planning, registration, and reconstruction tasks. In this thesis, a variety of approaches are presented to improve current c-arm tracking methods and devices for intra-operative procedures. The first approach presents a novel two-dimensional fiducial comprising a set of coplanar conics and an improved single-image pose estimation algorithm that addresses segmentation errors using a mathematical equilibration approach. Simulation results show an improvement in the mean rotation and translation errors by factors of 4 and 1.75, respectively, as a result of using the proposed algorithm. Experiments using real data obtained by imaging a simple precisely machined model consisting of three coplanar ellipses retrieve pose estimates that are in good agreement with those obtained by a ground truth optical tracker. This two-dimensional fiducial can be easily placed under the patient allowing a wide field of view for the motion of the c-arm. The second approach employs learning-based techniques to two-view geometrical theories. A demonstrative algorithm is used to simultaneously tackle matching and segmentation issues of features segmented from pairs of acquired images. The corrected features can then be used to retrieve the epipolar geometry which can ultimately provide pose parameters using a one-dimensional fiducial. The problem of match refinement for epipolar geometry estimation is formulated in a reinforcement-learning framework. Experiments demonstrate the ability to both reject false matches and fix small localization errors in the segmentation of true noisy matches in a minimal number of steps. The third approach presents a feasibility study for an approach that entirely eliminates the use of tracking fiducials. It relies only on preoperative data to initialize a point-based model that is subsequently used to iteratively estimate the pose and the structure of the point-like intraoperative implant using three to six images simultaneously. This method is tested in the framework of prostate brachytherapy in which preoperative data including planned 3-D locations for a large number of point-like implants called seeds is usually available. Simultaneous pose estimation for the c-arm for each image and localization of the seeds is studied in a simulation environment. Results indicate mean reconstruction errors that are less than 1.2 mm for noisy plans of 84 seeds or fewer. These are attained when the 3D mean error introduced to the plan as a result of adding Gaussian noise is less than 3.2 mm

Méthodes de segmentation d'images médicales basées sur la fusion d'information clinique : application à l'ouverture de la valve aortique et à la réalisation des contours de la prostate

Le domaine de l’imagerie médicale a pris depuis de nombreuses années un essor sans pareil permettant le développement de nouvelles méthodes de diagnostic et de traitement. Celles-ci se sont évidemment accompagnées de nombreux outils facilitant le travail des médecins. La présente thèse propose deux approches pour l’aide à la segmentation de structures anatomiques sur des images médicales. Une première technique se penche sur la détermination semi-automatique de l’aire de l’ouverture de la valve aortique. La combinaison des contours actifs et d’information a priori provenant de l’électrocardiogramme constitue une contribution majeure de cette méthode. Des essais ont été réalisés sur six patients. Ils ont produit une courbe de l’évolution temporelle de l’aire de la valve comparable à celle obtenue avec une segmentation manuelle. La seconde méthode permet de tracer les contours de la prostate sur des images de CT en exploitant l’information sur la prostate obtenue d’images d’échographie. L’objectif de cette méthode est de proposer des contours initiaux aux radio-oncologues afin de réduire la variabilité dans la détermination du volume de la prostate. La contribution majeure de cette technique est la projection des contours extraits de l’échographie sur les images de CT. Ces contours sont ensuite déformés pour les adapter à la forme réelle de la prostate sur l’image CT. Une étude clinique a été menée afin de vérifier l’impact de l’utilisation de cet outil d’aide au traçage des contours sur la variabilité intra et inter-observateurs. Les résultats de cette étude ont été très concluants puisqu’ils ont permis de montrer qu’il est possible de diminuer la variabilité inter-observateur de 6% sur le volume complet. L’étude n’a par contre pas permis de tirer une conclusion définitive concernant la diminution de la variabilité intra observateur. Le temps nécessaire pour le traçage des contours constituait aussi un aspect qui a été mesuré par cette étude. Les résultats obtenus montrent une diminution de 46% du temps nécessaire pour la réalisation des contours lorsque l’on propose des contours initiaux adaptés à l’image.Since many years the use of medical imaging techniques has increased significantly. Medical imaging has driven the development of treatments and diagnosis to increase the efficiency and the precision of the physicians. This thesis proposes two methods to help the segmentation of anatomical structures in medical images. The first technique creates semi-automatic segmentation for the opening of the aortic valve. This method combines active contours (snakes) and a priori information from the electrocardiogram for guiding the segmentation. This association is the major contribution of this approach. This method has been tested on six patients. The curve of the area of the opening of the valve produced by the algorithm is very similar to the same curve obtained with manual segmentation. The second technique extracts a segmentation of the prostate on CT images using ultrasound data. The aim of this tool is to suggest initial contours to the physician in order to reduce the variability in his delineation of the prostate volume. The major contribution of this technique is to project planning ultrasound contours on the CT images. After the projection, the contours are directly adapted to the CT image with a deformation process. A clinical survey has been led to assess that this tool can help to reduce the intra and inter-observer variability in his delineation of the prostate volume. The result of this study shows that it is possible to reduce the inter-observer variability by 6% on the complete volume. It is also possible to reduce the intra observer variability by 12%. The time for delineation of the prostate was also a factor that was measured in the clinical study. It was found that it is possible to reduce the time to draw contours as much as 46% when initial contours are suggested to the physician

Registration of magnetic resonance and ultrasound images for guiding prostate cancer interventions

Prostate cancer is a major international health problem with a large and rising incidence in many parts of the world. Transrectal ultrasound (TRUS) imaging is used routinely to guide surgical procedures, such as needle biopsy and a number of minimally-invasive therapies, but its limited ability to visualise prostate cancer is widely recognised. Magnetic resonance (MR) imaging techniques, on the other hand, have recently been developed that can provide clinically useful diagnostic information. Registration (or alignment) of MR and TRUS images during TRUS-guided surgical interventions potentially provides a cost-effective approach to augment TRUS images with clinically useful, MR-derived information (for example, tumour location, shape and size). This thesis describes a deformable image registration framework that enables automatic and/or semi-automatic alignment of MR and 3D TRUS images of the prostate gland. The method combines two technical developments in the field: First, a method for constructing patient-specific statistical shape models of prostate motion/deformation, based on learning from finite element simulations of gland motion using geometric data from a preoperative MR image, is proposed. Second, a novel “model-to-image” registration framework is developed to register this statistical shape model automatically to an intraoperative TRUS image. This registration approach is implemented using a novel model-to-image vector alignment (MIVA) algorithm, which maximises the likelihood of a particular instance of a statistical shape model given a voxel-intensity-based feature vector that represents an estimate of the surface normal vectors at the boundary of the organ in question. Using real patient data, the MR-TRUS registration accuracy of the new algorithm is validated using intra-prostatic anatomical landmarks. A rigorous and extensive validation analysis is also provided for assessing the image registration experiments. The final target registration error after performing 100 MR–TRUS registrations for each patient have a median of 2.40 mm, meaning that over 93% registrations may successfully hit the target representing a clinically significant lesion. The implemented registration algorithms took less than 30 seconds and 2 minutes for manually defined point- and normal vector features, respectively. The thesis concludes with a summary of potential applications and future research directions

Efficient design of precision medical robotics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 106-114).Medical robotics is increasingly demonstrating the potential to improve patient care through more precise interventions. However, taking inspiration from industrial robotics has often resulted in large, sometimes cumbersome designs, which represent high capital and per procedure expenditures, as well as increased procedure times. This thesis proposes and demonstrates an alternative model and method for developing economical, appropriately scaled medical robots that improve care and efficiency, while moderating costs. Key to this approach is a structured design process that actively reduces complexity. A selected medical procedure is decomposed into discrete tasks which are then separated into those that are conducted satisfactorily and those where the clinician encounters limitations, often where robots' strengths would be complimentary. Then by following deterministic principles and with continual user participation, prototyping and testing, a system can be designed that integrates into and assists with current procedures, rather than requiring a completely new protocol. This model is expected to lay the groundwork for increasing the use of hands-on technology in interventional medicine.by Nevan Clancy Hanumara.Ph.D

GVSU Undergraduate and Graduate Catalog, 2016-2017

Grand Valley State University 2016-2017 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1091/thumbnail.jp