Diffusion Tensor Imaging (DTI) is a medical imaging method that measures tissue structure. This is valuable when applied to the central nervous system (CNS) because it can provide structural information about white matter tracts. DTI of the spinal cord has been suggested as the next great leap in clinical diagnostics for spinal cord injury and disease because it may provide a measurable correlate of the physical structure of the cord with the associated functional deficit. Collecting precise structural information from the site of injury could be used to improve diagnostics and guide treatments. While these are the long term goals of DTI research, there are currently fundamental questions with regards to image resolution and motion-related artifacts in spinal cord which have not been thoroughly addressed. DTI is a sensitive imaging method which requires multiple mathematical calculations and approximations to complete. The limitations of the method compound with the limitations of imaging the spinal cord leading to the query: How reliable is DTI in the spinal cord? It is the goal of this study to begin to address these concerns. First, the effect of spinal cord motion on tissue discrimination was examined by comparing DTI results obtained in the presence and absence of a correctional measure for cardiac-induced motion called 'cardiac gating'. Tissue discriminability was found to be greatest in the cervical cord. Second, DTI results were subjected to two classification algorithms and compared with known anatomy to assess tissue discrimination accuracy as well as the types of associated errors. The proportion of errors in tissue classification was very high, presenting itself in all subjects. This result indicated that the DTI values associated with particular tissues were not unique to only those tissues. Finally, a theoretical model was implemented to assess the degree to which image resolution specifically affected the tissue classification accuracy obtained in the above experiments, as opposed to other errors such as MRI ghosting, blurring or distortions. It was found that DTI provides a systematically biased representation of spinal cord tissues. To overcome this limitation, future studies should concentrate efforts on increasing image resolution. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2010-09-24 02:29:32.619

This work explores if combining information from multiple Magnetic Resonance Imaging (MRI) modalities provides improved interpretation of brain biological architecture as each MR modality can reveal different characteristics of underlying anatomical structures. Structural MRI provides a means for high-resolution quantitative study of brain morphometry. Diffusion-weighted MR imaging (DWI) allows for low-resolution modeling of diffusivity properties of water molecules. Structural and diffusion-weighted MRI modalities are commonly used for monitoring the biological architecture of the brain in normal development or neurodegenerative disease processes. Structural MRI provides an overall map of brain tissue organization that is useful for identifying distinct anatomical boundaries that define gross organization of the brain. DWI models provide a reflection of the micro-structure of white matter (WM), thereby providing insightful information for measuring localized tissue properties or for generating maps of brain connectivity. Multispectral information from different structural MR modalities can lead to better delineation of anatomical boundaries, but careful considerations should be taken to deal with increased partial volume effects (PVE) when input modalities are provided in different spatial resolutions. Interpretation of diffusion-weighted MRI is strongly limited by its relatively low spatial resolution. PVE's are an inherent consequence of the limited spatial resolution in low-resolution images like DWI. This work develops novel methods to enhance tissue classification by addressing challenges of partial volume effects encountered from multi-modal data that are provided in different spatial resolutions. Additionally, this project addresses PVE in low-resolution DWI scans by introducing a novel super-resolution reconstruction approach that uses prior information from multi-modal structural MR images provided in higher spatial resolution. The major contributions of this work include: 1) Enhancing multi-modal tissue classification by addressing increased PVE when multispectral information come from different spatial resolutions. A novel method was introduced to find pure spatial samples that are not affected by partial volume composition. Once detecting pure samples, we can safely integrate multi-modal information in training/initialization of the classifier for an enhanced segmentation quality. Our method operates in physical spatial domain and is not limited by the constraints of voxel lattice spaces of different input modalities. 2) Enhancing the spatial resolution of DWI scans by introducing a novel method for super-resolution reconstruction of diffusion-weighted imaging data using high biological-resolution information provided by structural MRI data such that the voxel values at tissue boundaries of the reconstructed DWI image will be in agreement with the actual anatomical definitions of morphological data. We used 2D phantom data and 3D simulated multi-modal MR scans for quantitative evaluation of introduced tissue classification approach. The phantom study result demonstrates that the segmentation error rate is reduced when training samples were selected only from the pure samples. Quantitative results using Dice index from 3D simulated MR scans proves that the multi-modal segmentation quality with low-resolution second modality can approach the accuracy of high-resolution multi-modal segmentation when pure samples are incorporated in the training of classifier. We used high-resolution DWI from Human Connectome Project (HCP) as a gold standard for super-resolution reconstruction evaluation to measure the effectiveness of our method to recover high-resolution extrapolations from low-resolution DWI data using three evaluation approaches consisting of brain tractography, rotationally invariant scalars and tensor properties. Our validation demonstrates a significant improvement in the performance of developed approach in providing accurate assessment of brain connectivity and recovering the high-resolution rotationally invariant scalars (RIS) and tensor property measurements when our approach was compared with two common methods in the literature. The novel methods of this work provide important improvements in tools that assist with improving interpretation of brain biological architecture. We demonstrate an increased sensitivity for volumetric and diffusion measures commonly used in clinical trials to advance our understanding of both normal development and disease induced degeneration. The improved sensitivity may lead to a substantial decrease in the necessary sample size required to demonstrate statistical significance and thereby may reduce the cost of future studies or may allow more clinical and observational trials to be performed in parallel.

The limited spatial resolution in positron emission tomography (PET) images leads to difficulties to measure correct uptake in tumours. This is called partial volume effects (PVE) and can lead to serious bias, especially for small tumours. Correct uptake values are valuable for evaluating therapies and can be used as a tool for treatment planning. The purpose of this project was to evaluate two methods for compensating for PVE. Also, a method for tumour delineation in PET-images was evaluated. The methods were used on images reconstructed with two algorithms, VUE-point HD (VP HD) and VP SharpIR. The evaluation was performed using a phantom including fillable spheres which were used to simulate tumours of different sizes. The first method used for PVE compensation was an iterative deconvolution method which to some degree restores the spatial resolution in the images. The tumour uptake was measured with volumes of interest (VOIs) based on a percentage of the maximum voxel value. The second method was to use recovery coefficients (RCs) as correction factors for the measured activity concentrations. These were calculated by convolving binary images of tumours with the point spread function (PSF). The binary images were achieved both from computed tomography (CT) images and from PET images with a threshold method for tumour delineation. The threshold method was based on both tumour activity and background activity, and was also compared with a conventional threshold technique. The results showed that images reconstructed with VP SharpIR can be used for activity concentration measurements with good precision for tumours larger than 13mm diameter. Smaller tumours benefit from RCs correction. The threshold method for tumour delineation showed substantially better results compared to the conventional threshold method. / Den begränsade spatiella upplösningen i bilder från positronemissions-tomografi (PET) leder till svårigheter i att mäta korrekt upptag i tumörer. Detta kallas partiella volymseffekter (PVE) och kan leda till stora fel, speciellt för små tumörer. Korrekta upptagsvärden är värdefulla vid behandlingsutvärdering och kan användas som ett verktyg för att planera behandlingar. Syftet med detta projekt var att utvärdera två metoder för att kompensera för PVE. Även en metod för tumöravgränsning i PET-bilder utvärderades. Metoderna användes på bilder som rekonstruerats med två olika algoritmer, VUE-point HD (VP HD) och VP SharpIR. Utvärderingen utfördes med ett fantom med sfärer som fylldes med aktivitet och därmed simulerade tumörer av olika storlekar. Den första metoden för PVE-kompensation var en iterativ avfaltningsmetod som, i viss mån, återställer bildernas spatiella upplösning. Upptaget i tumörerna mättes som medelupptaget i s.k. ”volumes of interests” (VOI:ar) som baserades på andelar av maximala voxelvärdet. Den andra metoden byggde på användning av s.k. recovery coefficients (RCs) som korrektionsfaktorer för de uppmätta aktivitetskoncentrationerna. Dessa beräknades genom att falta binära bilder av tumörerna med punktspridningsfunktionen (PSF). De binära bilderna framställdes både från bilder tagna med datortomografi (computed tomography, CT) och från PET-bilder med en tröskelmetod för tumöravgränsning. Tröskelmetoden baserades både på aktiviteten i tumören och på bakgrundsaktiviteten. Den jämfördes också med en konventionell tröskelmetod. Resultaten visade att bilder som rekonstruerats med VP SharpIR kan användas för mätning av aktivitetskoncentration med god precision för tumörer större än 13mm diameter. För mindre tumörer är det bättre att använda RC:s. Tröskelmetoden för tumöravgränsning visade avsevärt bättre resultat jämfört med den traditionella tröskelmetoden.

Ce mémoire est consacré à la compensation des effets de flous dans une image, communément appelés effets de volume partiel (EVP), avec comme objectif d’application l’amélioration qualitative et quantitative des images en médecine nucléaire. Ces effets sont la conséquence de la faible résolutions spatiale qui caractérise l’imagerie fonctionnelle par tomographie à émission mono-photonique (TEMP) ou tomographie à émission de positons (TEP) et peuvent être caractérisés par une perte de signal dans les tissus présentant une taille comparable à celle de la résolution spatiale du système d’imagerie, représentée par sa fonction de dispersion ponctuelle (FDP). Outre ce phénomène, les EVP peuvent également entrainer une contamination croisée des intensités entre structures adjacentes présentant des activités radioactives différentes. Cet effet peut conduire à une sur ou sous estimation des activités réellement présentes dans ces régions voisines. Différentes techniques existent actuellement pour atténuer voire corriger les EVP et peuvent être regroupées selon le fait qu’elles interviennent avant, durant ou après le processus de reconstruction des images et qu’elles nécessitent ou non la définition de régions d’intérêt provenant d’une imagerie anatomique de plus haute résolution(tomodensitométrie TDM ou imagerie par résonance magnétique IRM). L’approche post-reconstruction basée sur le voxel (ne nécessitant donc pas de définition de régions d’intérêt) a été ici privilégiée afin d’éviter la dépendance aux reconstructions propres à chaque constructeur, exploitée et améliorée afin de corriger au mieux des EVP. Deux axes distincts ont été étudiés. Le premier est basé sur une approche multi-résolution dans le domaine des ondelettes exploitant l’apport d’une image anatomique haute résolution associée à l’image fonctionnelle. Le deuxième axe concerne l’amélioration de processus de déconvolution itérative et ce par l’apport d’outils comme les ondelettes et leurs extensions que sont les curvelets apportant une dimension supplémentaire à l’analyse par la notion de direction. Ces différentes approches ont été mises en application et validées par des analyses sur images synthétiques, simulées et cliniques que ce soit dans le domaine de la neurologie ou dans celui de l’oncologie. Finalement, les caméras commerciales actuelles intégrant de plus en plus des corrections de résolution spatiale dans leurs algorithmes de reconstruction, nous avons choisi de comparer de telles approches en TEP et en TEMP avec une approche de déconvolution itérative proposée dans ce mémoire. / Partial Volume Effects (PVE) designates the blur commonly found in nuclear medicine images andthis PhD work is dedicated to their correction with the objectives of qualitative and quantitativeimprovement of such images. PVE arise from the limited spatial resolution of functional imaging witheither Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography(SPECT). They can be defined as a signal loss in tissues of size similar to the Full Width at HalfMaximum (FWHM) of the PSF of the imaging device. In addition, PVE induce activity crosscontamination between adjacent structures with different tracer uptakes. This can lead to under or overestimation of the real activity of such analyzed regions. Various methodologies currently exist tocompensate or even correct for PVE and they may be classified depending on their place in theprocessing chain: either before, during or after the image reconstruction process, as well as theirdependency on co-registered anatomical images with higher spatial resolution, for instance ComputedTomography (CT) or Magnetic Resonance Imaging (MRI). The voxel-based and post-reconstructionapproach was chosen for this work to avoid regions of interest definition and dependency onproprietary reconstruction developed by each manufacturer, in order to improve the PVE correction.Two different contributions were carried out in this work: the first one is based on a multi-resolutionmethodology in the wavelet domain using the higher resolution details of a co-registered anatomicalimage associated to the functional dataset to correct. The second one is the improvement of iterativedeconvolution based methodologies by using tools such as directional wavelets and curveletsextensions. These various developed approaches were applied and validated using synthetic, simulatedand clinical images, for instance with neurology and oncology applications in mind. Finally, ascurrently available PET/CT scanners incorporate more and more spatial resolution corrections in theirimplemented reconstruction algorithms, we have compared such approaches in SPECT and PET to aniterative deconvolution methodology that was developed in this work.