No description available.

Magnetic resonance spectroscopy has been widely used in medical applications, rendering precise evaluation and diagnosis in clinics. As the development of various tools for automatic spectra analysis, providing objective quantification of metabolites, absolute concentrations has been playing an important role in clinical studies and applications as well. In this study, we investigate the reliability and accuracy of absolute concentration quantified by LCModel. Ten healthy subjects were included. We compared the resultant concentrations calculated by internal water scaling and phantom calibration, both of which are provided by LCModel. Partial volume effect was also taken into account to improve the accuracy of absolute concentrations. Automatic segmentation was applied to volume of interest in order to separate gray matter and white matter, which will facilitate the further partial volume correction and thus better accuracy of absolute quantification.

Magnetic resonance spectroscopy is now in widespread use, which with various tools of spectra analysis can provide concentrations of metabolites. The influence of metabolites on human physiology is greatly. Due to the tiny variation of the concentration in various metabolites, the analytic method used in the quantitative determination of the absolute concentrations of metabolites plays an important role in this research area. In this thesis we present an analysis tool for segmentation of white matter, gray matte and cerebrospinal fluid using region growing with spatial space, and provide manual interaction for exception handling in this subject. Then we use this tool to analyze different percentages of white matter and gray matter with the default parameter by LCModel and correct partial volume effect. The results show that the proposed tool can improve significantly the accuracy in absolute quantitative analysis of concentration.

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.

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

Since its inception around 1975, Positron Emission Tomography (PET) has proved to be an important tool in medical research as it allows imaging of the brain function in vivo with high sensitivity. It has been widely used in clinical dementia research with [18F]2-Fluoro-2-Deoxy-D-Glucose (FDG) and amyloid tracers as imaging biomarkers in Alzheimer's Disease (AD). The high resolution offered by modern scanner technology has the potential to provide new insight into the interaction of structural and functional changes in AD. However, the high resolution of PET is currently limited by movement and resolution (even for high resolution dedicated brain PET scanner) which results in partial volume effects, the undersampling of activity within small structures. A modified frame-by-frame (FBF) realignment algorithm has been developed that uses estimates of the centroid of activity within the brain to detect movement and subsequently reframe data to correct for intra-frame movement. The ability of the centroid to detect motion was assessed and the added benefit of reframing data for real clinical scans with patient motion was evaluated through comparison with existing FBF algorithms. Visual qualitative analysis on 6 FDG PET scans from 4 blinded observers demonstrated notable improvements (ANOVA with Tukey test, p < 0.001) and time-activity curves were found to deliver biologically more plausible activity concentrations. A new method for Partial Volume Correction (PVC) is also proposed, PARtially-Segmented Lucy-Richardson (PARSLR),that combines the strength of image based deconvolution approach of the Lucy-Richardson (LR) Iterative Deconvolution Algorithm with a partial segmentation of homogenous regions. Such an approach is of value where reliable segmentation is possible for part but not all of the image volume or sub-volume. Its superior performance with respect to region-based methods like Rousset or voxel-based methods like LR was successfully demonstrated via simulations and measured phantom data. The approach is of particular importance for studies with pathological abnormalities where complete and accurate segmentation across or with a sub-volume of the image volume is challenging and for regions of the brain containing heterogeneous structures which cannot be accurately segmented from co-registered images. The developed methods have been shown to recover radioactivity concentrations from small structures in the presence of motion and limited resolution with higher accuracy when compared to existing methods. It is expected that they will contribute significantly to future PET studies where accurate quantitation in small or atrophic brain structures is essential.

No description available.

Les pathologies cardio-vasculaires d’origine athéroscléreuse, premières causes de mortalité dans les pays occidentaux, sont insuffisamment prises en charge par les outils de dépistage et de suivi thérapeutique actuels. La Tomographie par Emission de Positons (TEP) est susceptible d’apporter au clinicien des outils puissants pour le diagnostic et le suivi thérapeutique des patients, en particulier grâce au traceur Fluorodésoxyglucose marqué au fluor 18 ([18F]-FDG). Cependant, l’Effet de Volume Partiel (EVP), dû notamment à la résolution spatiale limitée dans les images (plusieurs millimètres) en regard des faibles dimensions (de l’ordre du millimètre) des VOlumes d’Intérêt (VOIs), et les fluctuations statistiques du signal mesuré présentent des défis pour une quantification fiable.Un modèle original de lésion athéromateuse, paramétré par ses dimensions et sa concentration d’activité, a été développé et des simulations Monte-Carlo d’acquisitions TEP au [18F]-FDG de 36 lésions ont été produites. A partir des acquisitions simulées, nous avons montré que le nombre d’itérations des reconstructions itératives, le post-filtrage appliqué et le moyennage dans le VOI,paramètres relevés comme hautement variables dans une revue de la littérature dédiée, peuvent induire des variations des valeurs de fixation mesurées d’un facteur 1.5 à 4. Nous avons montré qu’une modélisation de la réponse du tomographe pouvait réduire le biais de mesure d’environ 10% par rapport au biais mesuré sur une image reconstruite avec un algorithme itératif standard et pour un niveau de bruit comparable. Sur les images reconstruites, nous avons montré que la fixation mesurée reste très biaisée (sous-estimation de plus de 50% du SUV réel) et dépend fortement des dimensions de la lésion à cause de l’EVP. Un contraste minimum de 4 par rapport à l’activité sanguine est nécessaire pour qu’une lésion soit détectée. Sans correction d’EVP, la mesure présente une corrélation faible avec la concentration d’activité, mais est très corrélée à l’activité totale dans la lésion. L’application d’une correction d’EVP fournit une mesure moins sensible à la géométrie de la lésion et plus corrélée à la concentration d’activité mais réduit la corrélation à l’activité totale dans la lésion.En conclusion, nous avons montré que l’intégralité de la fixation du [18F]-FDG dans les lésions athéromateuses inflammatoires totale peut être caractérisée sur les images TEP. Cette estimée ne requiert pas de correction de l’EVP. Lorsque la concentration d’activité dans la lésion est estimée, les mesures sont très biaisées à cause de l’EVP. Ce biais peut être réduit en mesurant le voxel d’intensité maximale, dans les images reconstruites sans post-filtrage avec au moins 80 itérations incluant un modèle de réponse du détecteur. La mise en œuvre d’une correction d’EVP facilite la détection des changements d’activité métabolique indépendamment de changements de dimensions de la zone siègede l’inflammation. Une quantification absolue exacte de la concentration d’activité dans les lésions ne sera possible que via une amélioration substantielle de la résolution spatiale des détecteurs TEP. / Cardiovascular disease is the leading cause of death in western countries. New strategies and tools for diagnosis and therapeutic monitoring need to be developed to manage patients with atherosclerosis, which is one major cause of cardiovascular disease. Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a powerful imaging technique that can detect at early stages plaques prone to rupture. Yet, Partial Volume Effect (PVE), due to the small lesion dimensions (around 1 mm) with respect to the scanner spatial resolution (around 6 mm full width at half maximum), and statistical variations considerably challenge the precise characterization of plaques from PET images. An original model of atheromatous lesion parameterized by its dimensions and activity concentration, was developed. Thirty six Monte Carlo simulations of FDG-PET acquisitions were produced. Based on the simulations, we showed that the number of iterations in iterative reconstructions, the post filtering of reconstructed images and the quantification method in the Volume Of Interests (VOI) varied sharply in an analysis of the dedicated literature. Changes in one of these parameters only could induce variations by a factor of 1.5 to 4 in the quantitative index. Overall, inflammation remained largely underestimated (> 50% of the real uptake). We demonstrated that modeling the detector response could reduce the bias by 10% of its value in comparison to a standard OSEM recontruction and for an identical level of noise. In reconstructed images, we showed that the measured values depended not only on the real uptake but also on the lesion dimensions because of PVE. A minimum contrast of 4 with respect to blood activity was required for the lesion to be observable. Without PVE correction, the measured values exhibited a correlation with activity concentration but were much more correlated with the total uptake in the lesion. Applying a PVE correction leads to an activity estimate that was less sensitive to the geometry of the lesion. The corrected values were more correlated to the activity concentration and less correlated to the total activity. In conclusion, we showed that the total activity in inflammatory lesions could be assessed in FDG-PET images. This estimate did not require PVE correction. Tracer concentration estimates are largely biased due to PVE, and the bias can be reduced by measuring the maximum voxel in the lesion in images reconstructed with at least 80 iterations and by modeling the detector response. Explicit PVE correction is recommended to detect metabolic changes independent of geometric changes. An accurate estimation of plaque uptake will however require the intrinsic spatial resolution of PET scanners to be improved.

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.