Parkinson's disease (PD) is a neurodegenerative disease that causes degeneration of nigral dopaminergic terminals in the caudate and the putamen regions of the striatum in the basal ganglia. According to current practice, when an unequivocal clinical diagnosis of PD cannot be made, a single-photon emission computed tomography scan using the DaTscan radionuclide (SPECT DaT scan) is ordered. However, the assessment of SPECT DaT scans in the diagnosis of PD depends on the subjective judgment of a radiologist, which can pose problems for the accuracy of the diagnosis. Furthermore, as research studies generally do not quantify SPECT DaT scans when using them, their conclusions are not based on standardized data. The aim of this paper is to propose a method of quantification for SPECT DaT scans, to be employed in diagnostic and research environments. The methodology proposed in this thesis project will eventually be used for a much larger multimodal imaging project investigating the connectivity changes in the brain related to cognitive and affective symptoms in PD patients. Each of the 4 subjects in this project underwent a SPECT DaT scan and an MPRAGE scan (Magnetization Prepared Rapid Gradient Echo), an anatomical MRI (magnetic resonance image). The SPECT DaT scans and the MPRAGEs were coregistered, and then a voxel-based quantification of the caudate and the putamen in the left and the right hemispheres was performed in every subject. First, the percentages of voxels with intensities exceeding various pericalcarine baselines were calculated. A pericalcarine baseline was used because the pericalcarine gyrus in the occipital lobe has been shown to have little to no dopaminergic activity, particularly on SPECT DaT scans. Next, asymmetry indices (AI) were calculated for two of the thresholds whereby the ratio of the percentage of voxels in the right to the left hemispheric region was taken. Wilcoxon Signed-Rank tests and bootstrapping analyses were performed on both the caudate and the putamen in all four subjects to determine the significance of any detected asymmetry. The quantification of the data and the AI values revealed asymmetries in the voxel intensities between the left and right hemispheres. This asymmetry was consistent with each subject's side of physical symptom onset. According to the bootstrapping analyses, this asymmetry was significant in five of the eight comparisons. In summary, this methodology has potential to bring greater objectivity to the use of SPECT DaT scans in the diagnosis of PD and in research through its anatomically accurate, voxel-based quantification.

Les travaux réalisés dans cette thèse se situent à l'interface des domaines de l'acquisition en imagerie par résonance magnétique (IRM) et du traitement d'image pour l'analyse automatique des structures cérébrales. La mesure de modifications structurelles telles que l'atrophie corticale nécessite l'application d'algorithmes de traitement d'image. Ceux-ci doivent compenser les artefacts en IRM tels que l'inhomogénéité du signal ou les effets de volume partiel (VP) pour permettre la segmentation des tissus cérébraux puis l'estimation d'épaisseur corticale. Nous proposons une nouvelle modélisation de VP proche de la physique de l'acquisition baptisée modèle bi-exponentiel qui vient concurrencer le traditionnel modèle linéaire. Il nécessite l'utilisation de deux images de contrastes différents parfaitement recalées. Ce modèle a été validé sur des simulations et des fantômes physique et numérique dans un premier temps. Parallèlement, la récente séquence MP2RAGE permet d'acquérir deux images co-recalées par acquisition et leur combinaison aboutit à l'obtention d'une image insensible aux inhomogénéités du signal et d'une carte de T1 des tissus imagés. Nous avons testé notre modèle sur des données in vivo MP2RAGE et avons montré que l'application du modèle linéaire de VP conduit à une sous-estimation systématique de la substance grise à l'échelle du voxel. Ces erreurs se propagent à l'estimation d'épaisseur corticale, biomarqueur très sensible aux effets de VP. Nos résultats plaident en faveur de l'hypothèse suivante : la modélisation de VP pour les images MP2RAGE doit être différente de celle employée pour des images obtenues avec des séquences plus classiques. Le modèle bi-exponentiel est une solution adaptée à cette séquence particulière. / The work developed in this thesis is within the scope of magnetic resonance imaging (MRI) acquisition and image processing for the automated analysis of brain structures. The measurement of structural modifications with time such as cortical atrophy requires the application of image processing algorithms. They must compensate for MRI artifacts such as intensity inhomogeneities or partial volume (PV) effects to allow for brain tissues segmentation then cortical thickness estimation. We suggest a new PV model relying on the physics of acquisition named bi-exponential model that differs from the commonly used linear model by modelling brain tissues and image acquisition. It requires the use of two differently contrasted and perfectly coregistered images. This model has been validated with simulations and physical and digital phantoms in a first place. In parallel, the recent MP2RAGE sequence provides two coregistered images and their combination results in a bias-field corrected image as well as a T1 map of the scanned tissues. We tested our model with in vivo MP2RAGE data and demonstrated that using the linear PV model leads to a systematic gray matter proportion underestimation in PV voxels. These errors result in cortical thickness underestimation. Our results favor the following assumption: PV modelling with MP2RAGE images must differ from the usual linear PV model applied for images obtained from more classic sequences. The bi-exponential model is an adapted solution to this particular sequence.

The inhomogeneous B1 field at higher main fields (B0) becomes more serious, leading to unsatisfactory MR image quality. To improve the signal homogeneity of routinely used T1-weighted image, usually acquired by a well-known sequence, Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE), a new pulse sequence, Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE), was proposed in 2009. This technique acquires two sets of high-resolution three- dimentional images at different inversion times after a series of inversion pulses. After any of two simple calculations of the raw images (Ratio or MP2RAGE reconstruction), the output volume was obtained with dramatically reduced spatial inhomogenuity of MR signal. In this study, the contrast-to-noise ratio (CNR) optimation at 3 T was implemented independently to reproduce the previous results of other group. After that, the simulation of scanning parameters was done to optimize CNR of brain tissue at 1.5 T according to different encoding methods, different pulse sequences, and different reconstruction algorithms. Phantom and human experiments were carried on a 1.5 T scanner for further validation. The results of phantom experiment showed that both MP2RAGE and Ratio reconstructions can achiever better B1 homogeneity than MPRAGE, even with the vendor-equipped correction packages, SCIC and PURE. In addition, the agreement was made between simulation and in-vivo imaging that MP2RAGE provides higher CNR than Ratio when centric encoding also outduels linear encoding.