The overarching purpose of this research is to understand whether Mixed Reality can enhance a surgeon’s manipulations skills during minimally invasive procedures. Minimally-invasive surgery (MIS) utilizes small cuts in the skin - or sometimes natural orifices - to deploy instruments inside a patient’s body, while a live video feed of the surgical site is provided by an endoscopic camera and displayed on a screen. MIS is associated with many benefits: small scars, less pain and shorter hospitalization time as compared to traditional open surgery. However, these benefits come at a cost: because surgeons have to work by looking at a monitor, and not down on their own hands, MIS disrupts their eye-hand coordination and makes even simple surgical maneuvers challenging to perform. In this study, we wish to use Mixed Reality technology to superimpose anatomical models over the surgical site and explore if it can be used to mitigate this problem.

Organ motion and inaccurate patient positioning may compromise radiation therapy outcome. With the aid of image guidance, it is possible to allow for a more accurate organ motion and motion control study, which could lead to the reduction of irradiated healthy tissues and possible dose escalation to the target volume to achieve better treatment results. The studies on the organ motion and image guidance were divided into the following four sections. The first, the interfractional setup uncertainties from day-to-day treatment and intrafractional internal organ motion within the daily treatment from five different anatomic sites were studied with Helical TomoTherapy unit. The pre-treatment mega voltage computed tomography (MVCT) provided the real-time tumor and organ shift coordinates, and can be used to improve the accuracy of patient positioning. The interfractional system errors and random errors were analyzed and the suggested margins for HN, brain, prostate, abdomen and lung were derived. The second, lung stereotactic body radiation therapy using the MIDCO BodyLoc whole body stereotactic localizer combined with TomoTherapy MVCT image guidance were investigated for the possible target and organ motion reduction. The comparison of 3D displacement with and without BodyLoc immobilization showed that, suppression of internal organ motion was improved by using BodyLoc in this study. The third, respiration related tumor motion was accurately studied with the four dimensional computed tomography (4DCT). Deformable registration between different breathing phases was performed to estimate the motion trajectory for lung tumor. Optimization is performed by minimizing the mean squared difference in intensity, and is implemented with a multi-resolution, gradient descent procedure. The fourth, lung tumor mobility and dosimetric benefits were compared with different PTV obtained from 3DCT and 4DCT. The results illustrated that the PTV3D not only included excess normal tissues but also might result in missed target tissue. The normal tissue complication probability (NTCP) from 4D plan was statistically significant smaller than 3D plan for both ipsilateral lung and heart.

A prototype system which has integrated a linear accelerator (linac) with a magnetic resonance imager (MRI) has been constructed at the Cross Cancer Institute. The real time operation of a linac-MRI system will require proper radio frequency (RF) shielding such that the MRI images can be acquired without extraneous RF noise from the linac. This thesis reports on the steps taken to successfully RF-shield the linac from the MRI such that the two devices can operate independently of one another. The RF noise from functioning multileaf collimators (MLC) is measured using near field probes and MRI images are acquired with the MLC near the MRI. This included measuring the RF noise as a function of applied magnetic field strength. Several measurement and simulation scenarios are discussed to determine the major sources of RF noise generation from the modulator of a linac. Finally RF power density levels are reported internally and externally to the RF cage which houses the linac and the MRI. The shielding effectiveness of the RF cage has been measured in the frequency range 1 50 MHz and is presented. MRI images of two phantoms are presented during linac operation. The MLC studies illustrate that the small RF noise produced by functioning MLC motors can be effectively shielded to avoid signal-to-noise degradation in the MRI image. A functioning MLC can be incorporated into a linac-MRI unit. The RF noise source investigations of the modulator of a linac illustrate that the major source of RF noise involves the operation of a magnetron. These studies also eliminate the pulse forming network (PFN) coil and the grid voltage spikes on the thyratron as possible major sources of RF noise. The main result is that for linac-MRI systems the modulator of a linac should be housed in a separate RF cage from the MRI. Finally imaging work with the linac operating illustrates that the accelerating structure of a linac and an MRI can be housed within the same RF cage. The 6 MV linac can be operated to produce radiation with no experientially measurable degradation in image quality due to RF effects. / Medical Physics

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Au cours des dix dernières années, l’utilisation de l’imagerie par tomographie par émission de positrons (TEP) s’est rapidement développée en oncologie. Certaines tumeurs non visibles en imagerie anatomique conventionnelle sont détectables en mesurant l'activité métabolique dans le corps humain par TEP. L’imagerie TEP est utilisée pour guider la délivrance de traitements locaux tels que par rayonnement ionisants ou ablation thermique. Pour la délivrance de ces traitements, segmenter la zone tumorale avec précision est primordial. Cependant, la faible résolution spatiale des images TEP rend la segmentation difficile. Plusieurs études ont démontré que la segmentation manuelle est sujette à une grande variabilité inter- et intra- individuelle et est fastidieuse. Pour ces raisons, de nombreux algorithmes de segmentation automatiques ont été développés. Cependant, peu de données fiables, avec des résultats histopathologiques existent pour valider ces algorithmes car il est expérimentalement difficile de les produire. Le travail méthodologique mis en place durant cette thèse a eu pour but de développer une méthode permettant de comparer les données histopathologiques aux données obtenue par TEP pour tester et valider des algorithmes de segmentation automatiques. Cette méthode consiste à réaliser des autoradiographies quantitatives de spécimens prélevés lors de biopsies guidées par TEP/tomodensitométrie (TDM); l’autoradiographie permettant d’imager la distribution du radiotraceur dans les échantillons avec une haute résolution spatiale. Les échantillons de tissus sont ensuite finement tranchés pour pouvoir être étudiés à l’aide d’un microscope. L’autoradiographie et les photomicrographes de l’échantillon de tissus sont ensuite recalés à l’image TEP, premièrement en les alignant avec l’aiguille à biopsie visible sur l’image TDM, puis en les transférant sur l’image TEP. Nous avons ensuite cherché à utiliser ces données pour tester deux algorithmes de segmentation automatique d'images TEP, le Fuzzy Locally Adaptive Bayesian (FLAB) développé au Laboratoire de Traitement de l'Information Médicale (LaTIM) à Brest, ainsi qu’une méthode de segmentation par seuillage. Cependant, la qualité de ces données repose sur la précision du recalage des images TEP, autoradiographiques et des micrographes. La principale source d’erreur dans le recalage de ces images venant de la fusion des images TEP/TDM, une méthode a été développée afin de quantifier la précision du recalage. Les résultats obtenus pour les patients inclus dans cette étude montrent que la précision de la fusion varie de 1.1 à 10.9 mm. En se basant sur ces résultats, les données ont été triées, pour finalement sélectionner les données acquises sur 4 patients jugées satisfaisantes pour tester les algorithmes de segmentation. Les résultats montrent qu’au point de la biopsie, les contours obtenus avec FLAB concordent davantage avec le bord de la lésion observé sur les micrographes. Cependant les deux méthodes de segmentation donnent des contours similaires, les lésions étant peu hétérogènes. / During the last decade, positron emission tomography (PET) has been finding broader application in oncology. Some tumors that are non-visible in standard anatomic imaging like computerized tomography (CT) or ultrasounds, can be detected by measuring in 3D the metabolic activity of the body, using PET imaging. PET images can also be used to deliver localized therapy like radiation therapy or ablation. In order to deliver localized therapy, the tumor border has to be delineated with very high accuracy. However, the poor spatial resolution of PET images makes the segmentation challenging. Studies have shown that manual segmentation introduces a large inter- and intra- variability, and is very time consuming. For these reasons, many automatic segmentation algorithms have been developed. However, few datasets with histopathological information are available to test and validate these algorithms since it is experimentally difficult to produce them. The aim of the method developed was to evaluate PET segmentation algorithms against the underlying histopathology. This method consists in acquiring quantitative autoradiography of biopsy specimen extracted under PET/CT guidance. The autoradiography allows imaging the radiotracer distribution in the biopsy specimen with a very high spatial accuracy. Histopathological sections of the specimen can then obtained and observed under the microscope. The autoradiography and the micrograph of the histological sections can then be registered with the PET image, by aligning them first with the biopsy needle seen on the CT image and then transferring them onto the PET image. The next step was to use this dataset to test two PET automatic segmentation algorithms: the Fuzzy Locally Adaptive Bayesian (FLAB) developed at the Laboratory of Medical Information Processing (LaTIM) in Brest, France, as well as a fix threshold segmentation method. However, the reliability of the dataset produced depends on the accuracy of the registration of the PET, autoradiography and micrograph images. The main source of uncertainty in the registration of these images comes from the registration between the CT and the PET. In order to evaluate the accuracy of the registration, a method was developed. The results obtained with this method showed that the registration error ranges from 1.1 to 10.9mm. Based on those results, the dataset obtained from 4 patients was judged satisfying to test the segmentation algorithms. The comparison of the contours obtained with FLAB and with the fixed threshold method shows that at the point of biopsy, the FLAB contour is closer than that to the histopathology contour. However, the two segmentation methods give similar contours, because the lesions were homogeneous.

Abstract The use of image-guidance in surgery and radiotherapy has significantly improved patient outcome in neurosurgery and radiotherapy treatments. This work developed volume definition and verification techniques for image-guided applications, using a number of algorithms ranging from image processing to visualization. Stereoscopic visualization, volumetric tumor model overlaid on an ultrasound image, and visualization of the treatment geometry were experimented with on a neurosurgical workstation. Visualization and volume definition tools were developed for radiotherapy treatment planning system. The magnetic resonance inhomogeneity correction developed in this work, possibly the first published data-driven method with wide applicability, automatically mitigates the RF field inhomogeneity artefact present in magnetic resonance images. Correcting the RF inhomogeneity improves the accuracy of the generated volumetric models. Various techniques to improve region growing are also presented. The simplex search method and combinatory similarity terms were used to improve the similarity function with a low additional computational cost and high yield in region correctness. Moreover, the effects of different priority queue implementations were studied. A fast algorithm for calculating high-quality digitally reconstructed radiographs has been developed and shown to better meet typical radiotherapy needs than the two alternative algorithms. A novel visualization method, beam's light view, is presented. It uses texture mapping for projecting the fluence of a radiation field on an arbitrary surface. This work suggests several improved algorithms for image processing, segmentation, and visualization used in image-guided treatment systems. The presented algorithms increase the accuracy of image-guidance, which can further improve the applicability and efficiency of image-guided treatments.

Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i. e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i. e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

Recent work has demonstrated improvement of image quality with low atomic number (Z) linear accelerator (linac) targets and energies as low as 3.5 MV compared to a standard 6 MV therapeutic beam. In this work, the incident electron beam energy has been lowered to energies between 1.90 and 2.35 MeV. The improvement of megavoltage planar image quality with the use of carbon and aluminum linac targets has been assessed compared to a standard 6 MV therapeutic beam. Common electronic portal imaging devices contain a 1.0 mm copper conversion plate to increase detection efficiency of a therapeutic megavoltage spectrum. When used in imaging with a photon beam generated with a low-Z target, the conversion plate attenuates a substantial proportion of photons in the diagnostic range, thereby reducing the achievable image quality. Image quality as a function of copper plate thickness has been assessed for planar imaging and cone beam computed tomography.

Image guided radiation therapy (IGRT) is a new type of radiotherapy used to deliver lethal doses of radiation to mobile tumors, while preventing surrounding healthy structures from receiving high doses of radiation. It relies on image guidance to track the tumor and ensure its prescribed position in the radiation beam. The main goal of this work was to determine if a new three-dimensional ultrasound (3D US) image guidance device, called the Restitu System, could safely replace (or be used interchangeably with) an existing method involving x-ray images of implanted fiducial markers (FMs) for prostate IGRT. Using comparison statistics called 95 % limits of agreement (LOA), it was found that the new 3D US system did not produce measurements that agreed sufficiently closely to those made using the FM technique, and therefore, could not safely replace FMs for prostate IGRT. Ultrasound image quality and user variability were determined to have a significant impact on the agreement between the two methods. It was further shown that using the Restitu System offered no significant clinical advantages over a conventional patient re-positioning technique.