Computed tomographic colonography(CTC), also called virtual colonoscopy, uses CT scanning and computer post-processing to create two dimensional images and three dimensional virtual views inside of the colon. Computer-aided polyp detection(CAPD) automatically detects colonic polyps and presents them to the user in either a first or second reader paradigm, with a goal reducing examination time while increasing the detection sensitivity. During colonoscopy, the endoscopists use the colonoscope inside of a patient's colon to target potential polyps and validate CAPD found ones. However, there is no direct information linking between CT images and the real-time optical colonoscopy(OC) video provided during the operation, thus endoscopists need to rely largely on their past experience to locate and remove polyps. The goal of this research project is to study the feasibility of developing an image guided colonoscopy(IGC) system that combines CTC images, real-time colonoscope position measurements, and video stream to validate and guide the removal of polyps found in CAPD. System would ease polyp level validation of CTC and improve the accuracy and efficiency of guiding the endoscopist to the target polyps. In this research project, a centerline based matching algorithm has been designed to estimate, in real time, the relative location of the colonoscope in the virtual colonoscopy environment. Furthermore, the feasibility of applying online simultaneous localization and mapping(SLAM) into CT image guided colonoscopy has been evaluated to further improve the performance of localizing and removing the pre-defined target polyps. A colon phantom is used to provide a testing setup to assess the performance of the proposed algorithms. / Master of Science

Image-guided surgeries employ advanced imaging and computing technologies to assist the surgeon when direct visualization is inadequate or unavailable. As modern surgeries continue to move toward minimally invasive procedures, tracked ultrasound (US), an emerging technology that uniquely combines US imaging and position tracking, has been increasingly used for intraoperative guidance in surgical interventions. The intrinsic accuracy of a tracked US system is primarily determined by a unique procedure called ``probe calibration", where a spatial registration between the coordinate systems of the transducer (provided by a tracking device affixed to the probe) and the US image plane must be established prior to imaging. Inaccurate system calibration causes misalignments between the US image and the surgical end-effectors, which may directly contribute to treatment failure. The probe calibration quality is further reduced by the "elevation beamwidth" or "slice thickness", a unique feature of the ultrasound beam pattern that gives rise to localization errors and imaging uncertainties. In this thesis, we aim to provide an automated, pure-computation-based, intraoperative calibration solution that also incorporates the slice thickness to improve the calibration accuracy, precision and reliability. The following contributions have been made during the course of this research. First, we have designed and developed an automated, freehand US calibration system with instant feedback on its calibration accuracy. The system was able to consistently achieve submillimeter accuracy with real-time performance. Furthermore, we have developed a novel beamwidth-weighted calibration framework (USB-FW) that incorporates US slice thickness to improve the estimation of calibration parameters. The new framework provides an effective means of quality control for calibration results. Extensive phantom validation demonstrated that USB-FW introduces statistically significant reduction (p = 0.001) in the calibration errors and produces calibration outcomes that are less variable than a conventional, non-beamwidth-weighted calibration. Finally, we were the first to introduce an automated, intraoperative Transrectal Ultrasound (TRUS) calibration technology for needle guidance in prostate brachytherapy. Our tests with multiple commercial TRUS scanners and brachytherapy stepper systems demonstrated that the proposed method is practical in use and can achieve high calibration accuracy, precision and robustness. / Thesis (Ph.D, Computing) -- Queen's University, 2012-10-22 16:18:55.439

Atherosclerosis is often diagnosed using an ultrasound (US) examination in the carotid and femoral arteries and the abdominal aorta. A decision to operate requires two measures of disease severity: the degree of stenosis measured using B-mode US; and the blood flow patterns in the artery measured using spectral Doppler US. However other biomechanical factors such as wall shear stress (WSS) and areas of flow recirculation are also important in disease development and rupture. These are estimated using an image-guided modelling approach, where a three-dimensional computational mesh of the artery is simulated. To generate a patient-specific arterial 3D computational mesh, a 3D ultrasound (3DUS) system was developed. This system uses a standard clinical US scanner with an optical position sensor to measure the position of the transducer; a video capture card to record video images from the scanner; and a PC running Stradwin software to reconstruct 3DUS data. The system was characterised using an industry-standard set of calibration phantoms, giving a reconstruction accuracy of ± 0.17 mm with a 12MHz linear array transducer. Artery movements from pulsatile flow were reduced using a retrospective gating technique. The effect of pressure applied to the transducer moving and deforming the artery was reduced using an image-based rigid registration technique. The artery lumen found on each 3DUS image was segmented using a semi-automatic segmentation technique known as ShIRT (the Sheffield Image Registration Toolkit). Arterial scans from healthy volunteers and patients with diagnosed arterial disease were segmented using the technique. The accuracy of the semi-automatic technique was assessed by comparing it to manual segmentation of each artery using a set of segmentation metrics. The mean accuracy of the semi-automatic technique ranged from 85% to 99% and depended on the quality of the images and the complexity of the shape of the lumen. Patient-specific 3D computational artery meshes were created using ShIRT. An idealised mesh was created using key features of the segmented 3DUS scan. This was registered and deformed to the rest of the segmented dataset, producing a mesh that represents the shape of the artery. Meshes created using ShIRT were compared to meshes created using the Rhino solid modelling package. ShIRT produced smoother meshes; Rhino reproduced the shape of arterial disease more accurately. The use of 3DUS with image-guided modelling has the potential to be an effective tool in the diagnosis of atherosclerosis. Simulations using these data reflect in vivo studies of wall shear stress and recirculation in diseased arteries and are comparable with results in the literature created using MRI and other 3DUS systems.

<p>A tenet of modern radiotherapy (RT) is to identify the treatment target accurately, following which the high-dose treatment volume may be expanded into the surrounding tissues in order to create the clinical and planning target volumes. Respiratory motion can induce errors in target volume delineation and dose delivery in radiation therapy for thoracic and abdominal cancers. Historically, radiotherapy treatment planning in the thoracic and abdominal regions has used 2D or 3D images acquired under uncoached free-breathing conditions, irrespective of whether the target tumor is moving or not. Once the gross target volume has been delineated, standard margins are commonly added in order to account for motion. However, the generic margins do not usually take the target motion trajectory into consideration. That may lead to under- or over-estimate motion with subsequent risk of missing the target during treatment or irradiating excessive normal tissue. That introduces systematic errors into treatment planning and delivery. In clinical practice, four-dimensional (4D) imaging has been popular in For RT motion management. It provides temporal information about tumor and organ at risk motion, and it permits patient-specific treatment planning. The most common contemporary imaging technique for identifying tumor motion is 4D computed tomography (4D-CT). However, CT has poor soft tissue contrast and it induce ionizing radiation hazard. In the last decade, 4D magnetic resonance imaging (4D-MRI) has become an emerging tool to image respiratory motion, especially in the abdomen, because of the superior soft-tissue contrast. Recently, several 4D-MRI techniques have been proposed, including prospective and retrospective approaches. Nevertheless, 4D-MRI techniques are faced with several challenges: 1) suboptimal and inconsistent tumor contrast with large inter-patient variation; 2) relatively low temporal-spatial resolution; 3) it lacks a reliable respiratory surrogate. In this research work, novel 4D-MRI techniques applying MRI weightings that was not used in existing 4D-MRI techniques, including T2/T1-weighted, T2-weighted and Diffusion-weighted MRI were investigated. A result-driven phase retrospective sorting method was proposed, and it was applied to image space as well as k-space of MR imaging. Novel image-based respiratory surrogates were developed, improved and evaluated.</p> / Dissertation

A novel series of near-infrared fluorescent contrast agents was developed and characterized. Their physicochemical and optical properties were measured. By altering functional groups of cyanine fluorophores, the selective targeting of endocrine glands, exocrine glands, cartilage and bone using NIR fluorescence to visualize the targeted tissue has been reported. These agents have high specificity for tissue targeting inherent to the chemical structure of the fluorophore. After a single low-dose intravenous injection these agents have high specificity for tissue targeting inherent to the chemical structure of the fluorophore. The results lay the foundation for future improvements in optical imaging in endocrine surgery, tissue engineering, joint surgery, and cartilage-specific drug development.

Objectives. The goal in radiotherapy is to deliver adequate radiation to the tumor volume while limiting damage to the surrounding healthy tissue. However, this goal is challenged by respiratory-induced motion. The objective of this work was to identify whether motion in electronic portal images can be tracked with an optical flow algorithm and whether a neural network can predict tumor motion. Methods. A multi-resolution optical flow algorithm that incorporates weighting based on the differences between image frames was used to automatically sample the vectors corresponding to the motion. The global motion was obtained by computing the average weighted mean from the set of vectors. The algorithm was evaluated using tumor trajectories taken from seven lung cancer patients, a 3D printed patient tumor and a virtual dynamic multi-leaf collimator (DMLC) system. The feasibility of detecting and tracking motion at the field edge was examined with a proof-of-concept implementation that included (1) an algorithm that detected local motion, and (2) a control algorithm that adapted the virtual MLC. To compensate for system latency, a generalized neural network, using both offline (treatment planning data) and online (during treatment delivery) learning, was implemented for tumor motion prediction. Results and Conclusions. The algorithm tracked the global motion of the target with an accuracy of around 0.5 mm. While the accuracy is similar to other methods, this approach does not require manual delineation of the target and can, therefore, provide real-time autonomous motion estimation during treatment. Motion at the treatment field edge was tracked with an accuracy of -0.4 ± 0.3 mm. This proof-of-concept simulation demonstrated that it is possible to adapt MLC leaves based on the motion detected at the field edges. Unplanned intrusions of external organs-at-risk could be shielded. A generalized network with a prediction error of 0.59 mm, and a shorter initial learning period (compared to previous studies) was achieved. This network may be used as a plug-and-play predictor in which tumor position could be predicted at the start of treatment and the need for pretreatment data and optimization for individual patients may be avoided. / February 2017

An improved method is introduced for prediction of local tumor control following lung stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) patients using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET). A normalized background-corrected tumor maximum Standard Uptake Value (SUVcmax) is introduced using the mean uptake of adjacent aorta (SUVref), instead of the maximum uptake of lung tumor (SUVmax). This method minimizes the variations associated with SUVmax and objectively demonstrates a strong correlation between the low SUVcmax (< 2.5-3.0) and local control of post lung SBRT. The false positive rates of both SUVmax and SUVcmax increase with inclusion of early (<6 months) PET scans, therefore such inclusion is not recommended for assessing local tumor control of post lung SBRT. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2013.

Intensity Modulated Radiation Therapy (IMRT) is a well-known type of external beam radiation therapy. The advancement in technology has had an inevitable influence in radiation oncology as well that has led to a newer and faster dose delivery technique called Volumetric Modulated Arc Therapy (VMAT). Since the presence of the VMAT modality in clinics in the late 2000, there have been many studies in order to compare the results of the VMAT modality with the current popular modality IMRT for various tumor sites in the body such as brain, prostate, head and neck, cervix and anal carcinoma. This is the first study to compare VMAT with IMRT for breast cancer. The results show that the RapidArc technique in Eclipse version 11 does not improve all aspects of the treatment plans for the breast cases automatically and easily, but it needs to be manipulated by extra techniques to create acceptable plans thus further research is needed. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

Image guided surgery (IGS), which has been developing fast recently, benefits significantly from the superior accuracy of robots and magnetic resonance imaging (MRI) which is a great soft tissue imaging modality. Teleoperation is especially desired in the MRI because of the highly constrained space inside the closed-bore MRI and the lack of haptic feedback with the fully autonomous robotic systems. It also very well maintains the human in the loop that significantly enhances safety. This dissertation describes the development of teleoperation approaches and implementation on an example system for MRI with details of different key components. The dissertation firstly describes the general teleoperation architecture with modular software and hardware components. The MRI-compatible robot controller, driving technology as well as the robot navigation and control software are introduced. As a crucial step to determine the robot location inside the MRI, two methods of registration and tracking are discussed. The first method utilizes the existing Z shaped fiducial frame design but with a newly developed multi-image registration method which has higher accuracy with a smaller fiducial frame. The second method is a new fiducial design with a cylindrical shaped frame which is especially suitable for registration and tracking for needles. Alongside, a single-image based algorithm is developed to not only reach higher accuracy but also run faster. In addition, performance enhanced fiducial frame is also studied by integrating self-resonant coils. A surgical master-slave teleoperation system for the application of percutaneous interventional procedures under continuous MRI guidance is presented. The slave robot is a piezoelectric-actuated needle insertion robot with fiber optic force sensor integrated. The master robot is a pneumatic-driven haptic device which not only controls the position of the slave robot, but also renders the force associated with needle placement interventions to the surgeon. Both of master and slave robots mechanical design, kinematics, force sensing and feedback technologies are discussed. Force and position tracking results of the master-slave robot are demonstrated to validate the tracking performance of the integrated system. MRI compatibility is evaluated extensively. Teleoperated needle steering is also demonstrated under live MR imaging. A control system of a clinical grade MRI-compatible parallel 4-DOF surgical manipulator for minimally invasive in-bore prostate percutaneous interventions through the patient’s perineum is discussed in the end. The proposed manipulator takes advantage of four sliders actuated by piezoelectric motors and incremental rotary encoders, which are compatible with the MRI environment. Two generations of optical limit switches are designed to provide better safety features for real clinical use. The performance of both generations of the limit switch is tested. MRI guided accuracy and MRI-compatibility of whole robotic system is also evaluated. Two clinical prostate biopsy cases have been conducted with this assistive robot.

Intra-operative imaging is sometimes available to assist in needle biopsy, but typical open- loop insertion does not account for unmodeled needle deflection or target shift. Robotic closed-loop compensation for deviation from an initial straight-line trajectory can reduce the targeting error, using image-guidance for rotational control of an asymmetric bevel tip. By pairing closed-loop trajectory compensation with a hands-on cooperatively controlled needle insertion, a physician's control of the procedure can be maintained while incorporating benefits of robotic accuracy. Additionally, if puncture of a membrane can be detected, an enhanced haptic response can assist the physician in perceived anatomical localization of the needle tip. Functionality was implemented on a needle placement robot suitable for use in the MR environment and capable of holding a typical clinically used biopsy gun. The robot is configured for cooperatively controlled needle insertion with continuous closed-loop image- guided needle rotation. The robot and custom controller were tested for their effect on the Signal-to-Noise ratio (SNR) of MR images, and the results showed an approximate drop of only 12% in signal when the robot was present, and no additional signal drop when the robot was powered on or moving. The hardware and software subsystems were developed for clinical translation, and after each was validated in the lab they were integrated into the clinical environment to mimic the workflow of MRI-guided targeted biopsy. The full system was evaluated in-bore at Brigham and Women’s Hospital in Boston, MA where experiments for real-time puncture detection and MR image-guided targeted needle insertions under cooperative control were performed. Results showed overall targeting accuracy was 3.42mm RMS, improving accuracy by approximately 50% as compared to clinical trials of prostate biopsy using manual needle insertion. A cooperatively controlled robotic biopsy is more likely to gain acceptance by physicians over teleoperation due to maintaining proximity to the surgical site, but regulatory hurdles regarding robotic needle insertion still exist. The current robotic system framework is suitable for clinical use as it was fully validated in-bore, but some modifications could be made to increase the likelihood of regulatory approval. With these modifications the system could be ready for cadaver and pre- clinical animal trials within one year, and ready for in-human clinical trials in the next two to three years.