Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images

Segmentation of Fetal Pericerebral Spaces Based on Reconstructed High-Resolution MRI

International audienc

Correlation of Diffusion and Metabolic Alterations in Different Clinical Forms of Multiple Sclerosis

Diffusion tensor imaging (DTI) and MR spectroscopic imaging (MRSI) provide greater sensitivity than conventional MRI to detect diffuse alterations in normal appearing white matter (NAWM) of Multiple Sclerosis (MS) patients with different clinical forms. Therefore, the goal of this study is to combine DTI and MRSI measurements to analyze the relation between diffusion and metabolic markers, T2-weighted lesion load (T2-LL) and the patients clinical status. The sensitivity and specificity of both methods were then compared in terms of MS clinical forms differentiation. MR examination was performed on 71 MS patients (27 relapsing remitting (RR), 26 secondary progressive (SP) and 18 primary progressive (PP)) and 24 control subjects. DTI and MRSI measurements were obtained from two identical regions of interest selected in left and right centrum semioval (CSO) WM. DTI metrics and metabolic contents were significantly altered in MS patients with the exception of N-acetyl-aspartate (NAA) and NAA/Choline (Cho) ratio in RR patients. Significant correlations were observed between diffusion and metabolic measures to various degrees in every MS patients group. Most DTI metrics were significantly correlated with the T2-LL while only NAA/Cr ratio was correlated in RR patients. A comparison analysis of MR methods efficiency demonstrated a better sensitivity/specificity of DTI over MRSI. Nevertheless, NAA/Cr ratio could distinguish all MS and SP patients groups from controls, while NAA/Cho ratio differentiated PP patients from controls. This study demonstrated that diffusivity changes related to microstructural alterations were correlated with metabolic changes and provided a better sensitivity to detect early changes, particularly in RR patients who are more subject to inflammatory processes. In contrast, the better specificity of metabolic ratios to detect axonal damage and demyelination may provide a better index for identification of PP patients

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features.Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests.Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features.Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms

Contribution of high energy physics techniques to the medical imaging field

The purpose of this study was to show how advanced concepts of compact, lossless and "Time Of Flight" (TOF) capable electronics similar to those foreseen for the LHC and ILC experiments could be fairly and easily transferred to the medical imaging field through Positron Emission Tomography (PET). As a wish of explanation, the two overriding weaknesses of PET camera readout electronics, namely dead-time and timing resolution, were investigated analytically and with a Monte-Carlo simulator presently dedicated to this task. Results have shown there was room left for count rate enhancement through a huge decrease of the timing resolution well below the nanosecond. The novel electronics scheme suggested for PET in this paper has been partly inspired by the long experience led in High Energy Physics where the latter requirement is compulsory. Its structure entirely pipelined combined to a pixelation of the whole detector should allow dead-times to be suppressed, while the absence of devoted timing channel would remove the preponderant contributions to the timing resolution. To the common solution for timing would substitute an optimal filtering method witch clearly appears as a good candidate as timing resolution of a few tens of picoseconds may be achieved provided the shape of the signal is known and sufficient samples are available with enough accuracy. First investigations have yield encouraging results as a sampling frequency of 50 MHz with a 7 bits precision appears sufficient to ensure the 500ps coincidence timing resolution planed. At this point, there will be a baby step ahead to draw benefice from a TOF implementation to the design and the enormous noise variance enhancement that would come with.Comment: presented at EuroMedIm 2006 : 1st European Conference on Molecular Imaging Technology, Marseille 9-12 May 2006, 6 pp, 4 figures, submitted to NI

Spectroscopie par resonance magnetique nucleaire des lipides de la substance blanche cerebrale normale et pathologique : sclerose en plaques

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Reconstruction tomographique 3D en géométrie conique à trajectoire circulaire pour des prototypes d'imageur bimodal pour le petit animal

La reconstruction tomographique de données de transmission par rayons X est abordée dans ce travail. La problématique concerne la reconstruction 3D à partir de projections coniques acquises le long d'une trajectoire circulaire. L'étude est menée dans le cadre de deux projets de prototypes de micro-imageurs TEP/TDM pour le petit animal. Les contraintes de ces prototypes liées à l'acquisition simultanée des données pour les deux modalités produisent d'importantes données manquantes. Deux formules originales sont proposées pour prendre en compte la géométrie 3D excentrée d'une part et la géométrie 2D avec détecteur cylindrique à faible rayon de courbure d'autre part. Par ailleurs, différentes méthodes de correction des artéfacts hors plan médian sont étudiées sur des simulations. Des reconstructions de données réelles obtenues à partir de prototypes de simples microTDM sont données.The tomographic reconstruction from X-ray transmission data is approached in this work. We address 3D reconstruction from cone beam projections acquired along a circular source trajectory. The study is undertaken within the framework of two projects of prototypes of PET/CT micro-scanners for the small animal, whose objective is to allow the simultaneous acquisition of the data of transmission and emission. Technological constraints relatied to the simultaneous acquisition of the data imply signifant missing data. Two original formulae are developped to allow for off-centered geometry on the one hand, and fourth generation 2D geometry on the other hand. Various cone beam artefacts compensation methods are also studied on simulations. Real data reconstructions obtained from acquisitions on single microCT scanner are finally displayed.VILLEURBANNE-DOC'INSA LYON (692662301) / SudocSudocFranceF

Modélisation du débit sanguin cérébral absolu par méthode isotoique au Xénon-133 pour une utilisation clinique

En marge des techniques morphologiques (scanner X, IRM), la tomographie par émission mono-photonique (TEMP) constitue aujourd'hui la modalité d'imagerie fonctionnelle la plus répandue en clinique. Néanmoins, la méthode de mesure absolue du débit sanguin cérébral (DSC) nécessite un appareillage dédié, coûteux et limités à l'exploration du cerveau. L'objectif de notre travail était donc de développer une méthode de mesure absolue du DSC (mL/min/100g) pour une -caméra double-tête rotative quotidiennement utilisée dans les centres de Médecine Nucléaire. Nous avons développé un modèle compartimental de diffusion du 133Xe à partir de simulations permettant d'évaluer les paramètres physiques et leurs limites. Pour valider cette approche théorique, le travail s'est poursuivi par la mise au point d'un fantôme physique de perfusion permettant d'étalonner notre méthode de mesure absolue du DSC et de définir les conditions optimales d'utilisation de la -caméra VariCam. Des examens au 133Xe chez des sujets volontaires sains ont montré que les modèles classiques de calcul du DSC conduisaient à une sous-estimation importante. Cette analyse a permis de développer un nouveau modèle basé sur les équations de Kety et Schmidt, mieux adapté aux conditions d'examen. Enfin, nous avons effectué un travail de validation clinique en comparant notre méthode de mesure absolue du DSC au 133Xe avec les méthodes de mesure relative de la perfusion au 99mTc-HmPAO. L'application de ces deux techniques chez des patients a permis de mettre en évidence l'existence d'une bonne corrélation des valeurs de DSC dans diverses pathologies (diminution ou augmentation). La technique de calcul du DSC par inhalation de 133Xe reste la méthode de référence lorsqu'on désire une quantification absolue. Par notre travail, nous avons montré que certains des inconvénients la caractérisant peuvent être corrigés, permettant ainsi son application à toute -camera double-tête rotative utilisée en milieu hospitalierLYON1-BU.Sciences (692662101) / SudocSudocFranceF

A genetic algorithm-based model for longitudinal changes detection in white matter fiber-bundles of patient with multiple sclerosis

International audienceAnalysis of white matter (WM) tissue is essential to understand the mechanisms of neurodegenerative pathologies like multiple sclerosis (MS). Recently longitudinal studies started to show how the temporal component is important to investigate temporal diffuse effects of neurodegenerative pathologies.Diffusion tensor imaging (DTI) constitutes one of the most sensitive techniques for the detection and characterization of brain related pathological processes and allows also the reconstruction of WM fibers. The analysis of spatial and temporal pathological changes along the fibers are thus possible by merging quantitative maps with structural information provided by DTI.In this work, we present a new genetic algorithm (GA) based method to analyze longitudinal changes occurring along WM fiber-bundles. In the first part of this paper, we describe the data processing pipeline, including data registration and fiber tract post-processing. In the second part, we focus our attention to the description of our GA model. In the last part, we show the tests we performed on simulated and real MS longitudinal data. Our method reached a high level of precision, recall and F-Measure in the detection of longitudinal pathological alterations occurring along different WM fiber-bundles