Robust similarity metrics for the registration of 3D multimodal medical images

In this paper, we develop data driven registration algorithms, relying on pixel similarity metrics, that enable an accurate rigid registration of dissimilar single or multimodal 2D/3D medical images . Gross dissimilarities are handled by considering similarity measures related to robust M-estimators . Fast stochastic multigrid optimization algorithms are used to minimize these similarity metrics . The proposed robust similarity metrics are compared to the most popular standard similarity metrics on real MRI/MRI and MRI/SPECT image pairs showing gross dissimilarities . A blinded evaluation of the algorithm was performed, using as gold standard a prospective, marker-based registration method, by participating in a registration evaluation project (Vanderbilt University) . Our robust similarity measures compare favourably with all standard (non robust) techniques .Le recalage non supervisé d'images médicales volumiques reste un problème difficile en raison de l'importante variabilité et des grandes différences d'information pouvant apparaître dans des séquences d'images de même modalité ou dans des couples d'images multimodales. Nous présentons dans cet article des méthodes robustes de recalage rigide d'images 2D et 3D monomodales et multimodales, reposant sur la minimisation de mesures de similarité inter-images. Les méthodes proposées s'appuient sur la théorie de l'estimation robuste et mettent en oeuvre des M-estimateurs associés à des techniques d'optimisation stochastique multigrilles rapides. Ces estimateurs robustes sont évalués à travers le recalage d'images médicales volumiques monomodales (IRM/IRM) et multimodales (IRM/TEMP). Ils sont comparés aux autres fonctions de similarité classiques, proposées dans la littérature. Les méthodes de recalage robustes ont, en particulier, été validées dans le cadre d'un protocole comparatif mis en place par l'Université de Vanderbilt. Elles sont actuellement utilisées en routine clinique et conduisent, tant pour les images de même modalité que pour les images multimodales à une précision sous-voxel, comparable aux meilleures méthodes actuelles. Elles permettent de plus de recaler des couples d'images sur lesquels les méthodes classiques échouent

Une approche a contrario pour la détection de changements dans des images IRM multimodales 3D

La détection de changements significatifs entre deux images demeure un problème délicat. Dans ce contexte, une méthodologie récemment proposée dans [DMM03] émerge : l'approche a contrario. Il s'agit d'une approche non paramétrique présentant l'avantage de prendre en compte dans le processus de décision l'information contextuelle et différentes valeurs de seuil de détection. Nous étendons ici cette approche de manière à traiter des images multimodales desquelles sont extraites différentes images de mesure. Pour cela, deux règles de fusion sont développées de manière à combiner l'information provenant des images de mesure et celle provenant des différents seuils de détection. De plus, une nouvelle règle de décision, basée sur des tests de permutation, est proposée. La méthodologie a contrario est décrite dans la Section 1. Nous proposerons ensuite un nouveau cadre statistique dans la section 2. Enfin, la section 3 illustre l'application de la méthode pour de la détection de changements dans des images IRM dans le contexte de la sclérose en plaques

A Metric Multidimensional Scaling-Based Nonlinear Manifold Learning Approach for Unsupervised Data Reduction

Manifold learning may be seen as a procedure aiming at capturing the degrees of freedom and structure characterizing a set of high-dimensional data, such as images or patterns. The usual goals are data understanding, visualization, classification, and the computation of means. In a linear framework, this problem is typically addressed by principal component analysis (PCA). We propose here a nonlinear extension to PCA. Firstly, the reduced variables are determined in the metric multidimensional scaling framework. Secondly, regression of the original variables with respect to the reduced variables is achieved considering a piecewise linear model. Both steps parameterize the (noisy) manifold holding the original data. Finally, we address the projection of data onto the manifold. The problem is cast in a Bayesian framework. Application of the proposed approach to standard data sets such as the COIL-20 database is presented

SPM analysis of ictal-interictal SPECT in mesial temporal lobe epilepsy: relationships between ictal semiology and perfusion changes.

International audienceA combination of temporo-limbic hyperperfusion and extratemporal hypoperfusion was observed during complex partial seizures (CPS) in temporal lobe epilepsy (TLE). To investigate the clinical correlate of perfusion changes in TLE, we analyzed focal seizures of increasing severity using voxel-based analysis of ictal SPECT. We selected 26 pre-operative pairs of ictal-interictal SPECTs from adult mesial TLE patients, seizure-free after surgery. Ictal SPECTs were classified in three groups: motionless seizures (group ML, n=8), seizures with motor automatisms (MA) without dystonic posturing (DP) (group MA, n=8), and seizures with DP with or without MA (DP, n=10). Patients of group ML had simple partial seizures (SPS), while others had CPS. Groups of ictal-interictal SPECT were compared to a control group using statistical parametric mapping (SPM). In ML group, SPM analysis failed to show significant changes. Hyperperfusion involved the anteromesial temporal region in MA group, and also the insula, posterior putamen and thalamus in DP group. Hypoperfusion was restricted to the posterior cingulate and prefrontal regions in MA group, and involved more widespread associative anterior and posterior regions in DP group. Temporal lobe seizures with DP show the most complex pattern of combined hyper-hypoperfusion, possibly related both to a larger spread and the recruitment of more powerful inhibitory processes

Supplementary Material for: Functional Disconnectivity during Inter-Task Resting State in Dementia with Lewy Bodies

<b><i>Aims:</i></b> Limited research has been done on the functional connectivity in visuoperceptual regions in dementia with Lewy bodies (DLB) patients. This study aimed to investigate the functional connectivity differences between a task condition and an inter-task resting state condition within a visuoperceptual paradigm, in DLB patients compared with Alzheimer disease (AD) patients and healthy elderly control subjects. <b><i>Methods:</i></b> Twenty-six DLB, 29 AD, and 22 healthy subjects underwent a detailed clinical and neuropsychological examination along with a functional MRI during the different conditions of a visuoperceptual paradigm. Functional images were analyzed using group-level spatial independent component analysis and seed-based connectivity analyses. <b><i>Results:</i></b> While the DLB patients scored well and did not differ from the control and AD groups in terms of functional activity and connectivity during the task conditions, they showed decreased functional connectivity in visuoperceptual regions during the resting state condition, along with a temporal impairment of the default-mode network activity. Functional connectivity disturbances were also found within two attentional-executive networks and between these networks and visuoperceptual regions. <b><i>Conclusion:</i></b> We found a specific functional profile in the switching between task and resting state conditions in DLB patients. This result could help better characterize functional impairments in DLB and their contribution to several core symptoms of this pathology such as visual hallucinations and cognitive fluctuations