No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Laser speckle contrast imaging (LSCI) is a non-invasive method for assessment of microcirculatory blood flow. The technique is based on analysis of speckle patterns to build 2D maps of perfusion with high spatial and temporal resolution. A drawback of the method is that it is highly sensitive to motion artifacts since the perfusion estimates are based on quantification of the motion blurring in the images. The camera is today limited to a bulky stand for good measurements, but even as it is fixed, it does not ensure that the patient is completely still. In many clinical settings, it would be advantageous to have a more flexible camera and to be able to detect if an image is influenced by external motion. Multi-exposure laser speckle contrast imaging (MELSCI) is an extension to LSCI that utilizes the contrast from multiple exposure times. The gain in information has paved way for more accurate perfusion estimates. The technique has been limited due to its computational complexity, but recently a real time system has been developed. The goals of this thesis was twofold, firstly find a quantifiable measure of motion artifacts to be able to evaluate and compare LSCI and MELSCI. Secondly, propose an algorithm that detects movements in LSCI recordings. Motion artifacts in LSCI and MELSCI were investigated by developing a setup where repeatable movements could be made. Measurements of a hand influenced by motions of different speeds and directions were acquired and the relative difference between motion and static states were calculated and compared for the two systems. The relative difference of the MELSCI measurements were lower for all speeds above 0.57 mm/s, indicating more robustness to motion artifacts. A detection algorithm using image registration to calculate the instantaneous speed in each frame of the recording was developed. The method successfully detects movements perpendicular to the camera and shows that the intensity images of an LSCI recording can be used to give a direct indication of when movement has occurred.

Finite Element (FE) head models are very convenient tools forthe study of Traumatic Brain Injuries (TBIs) but lack significantanatomical details for the investigation of morphology or age-dependantinjury mechanisms. In this context, the use of deformable registrationalgorithms for the generation of personalized head models is veryconsistent for the development of improved protection systems likehelmets. This thesis presents the performances of the registrationpipeline Demons combined to the Difformable Registration via AttributesMatching and Mutual-SaliencyWeighting (DRAMMS) for the generationof FE head models. Twelve subject-specific models are formed bymorphing the baseline mesh with the displacement fields resultingfrom the registration methods. The obtained models are assessedand compared through the evaluation of elements’ quality by analysisof the distortion index distribution. The Dice similarity coefficientis also calculated to estimate the personalization accuracy of theapplied pipeline. The Demons+DRAMMS registration pipeline showssatisfactory personalization accuracy for cranial mask and internalbrain structures. No significant degradation of mesh quality dueto the morphing process or specific subject morphology is observed.The present work corroborates previous study regarding the use ofDemons+DRAMMS registration pipeline for generating subject-specifichead models and validates the performances of the registration methodsand the repeatability of the morphing process for this purpose.

<p>The image-guided radiation therapy (IGRT) protocol used at Juravinski Cancer Center for post-prostatectomy patients involves acquiring a kV cone beam computed tomography (CBCT) image at each fraction and shifting the treatment couch to align surgical clips. This IGRT strategy is promising but its dosimetric impact is unknown, it requires significant resources, and delivers non-negligible doses to normal tissues. The objective of this work is to evaluate this IGRT protocol and investigate possible alternatives.</p> <p>IGRT delivered dose is reconstructed by deforming the planning CT to the CBCT images acquired at each fraction, computing dose on the deformed images, and inversely transforming the dose back to the original geometry. The treatments of six patients were evaluated under four scenarios: no guidance (Non-IGRT), daily guidance as performed clinically (IGRT), guidance on alternating days (Alt-IGRT), and daily automated guidance (Auto-IGRT). For one patient, the impact of reducing the planning target volume (PTV) margin to five (IGRT-5) and eight (IGRT-8) mm isotropic was also evaluated.</p> <p>With the standard clinical PTV margin of ten/seven mm, the evaluated alternatives produced similar results. The minimum dose to the CTV was decreased by 1.6±1.0, 1.2±0.7, and 0.8±0.8 Gy for Non-IGRT, Alt-IGRT, and IGRT, respectively. IGRT with manual shifting did not appear to significantly improve the delivered treatment dose compared to Auto-IGRT (difference in CTV minimum dose was 1.2±2.1Gy). Doses to the organs at risk varied but in general, an increased volume of the bladder and rectum received low doses while smaller portions received high doses. The IGRT-5 and -8 analyses showed the same CTV dose can be delivered with significant reduction in normal tissue exposure. Overall, the desired doses are delivered during IGRT although much of this may be attributed to the large PTV margins currently employed clinically.</p> / Master of Science (MSc)

<p>Minimally invasive operations have gained popularity over open surgical procedures in the recent years. These procedures, require the surgeon to perform highly specialized tasks including manipulation of tools through small incisions on the surface of the skin while looking at the images that are displayed on a screen. Therefore, effective training is required for the surgeons prior to performing such procedures on patients.</p> <p>In this thesis I explored a novel idea for creating a training system for arthroscopic surgery. Previously obtained CT images of a patient model and the surgical tools are manipulated to create a library of fluoroscopy images. The surgical tools are tracked (a mechanical tracker and an electromagnetic tracker used in each iterations) in order to generate a spacial relationship between the patient model and the surgical tools. The position and orientation information from the tracking system is translated into the image coordinate frame. These homologous points in the two images (of surgical tools and the patient model), are used to co-register and overlay the two images and create a virtual fluoroscopy image.</p> <p>The output image and the system performance was found to be very good and quite similar to that of a fluoroscopy system. The registration accuracy was evaluated using Root Mean Square Target Registration Error (RMS TRE). The RMS TRE for the system setup with the mechanical tracker was evaluated at 2:0 mm, 2:1 mm, and 2:5 mm, for 4, 5, and 6 control points, respectively. In the system setup with the electromagnetic tracking system the RMS TRE was evaluated at 7:6 mm, 12:4 mm, and 11:3 mm, for 5, 7, and 9 control points, respectively. The acceptable range of error for arthroscopy procedures has been proposed to be 1-2 mm.</p> <p>It was concluded that by using a tracking system, which is not prone to interference and allows for a wide range of motion this system can be completed to the point of manufacturing and use in training new surgeons.</p> / Master of Applied Science (MASc)