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Apathy is the most frequent behavioral symptom in Alzheimer's disease and is also frequently reported in other brain organic disorders occurring in the elderly. Based on the literature, we hypothesized that apathy was related to an anterior cingulate hypofunction. Forty-one subjects were studied. According to ICD 10 diagnostic criteria, 28 patients had Alzheimer dementia (demented: diagnostic group 1), and 13 had organic personality disorders or mild cognitive impairment not attributable to dementia (nondemented: diagnostic group 2). Apathy was evaluated by the Neuro-Psychiatric Inventory. As a result each diagnostic group was divided into two symptomatic subgroups: apathetic or nonapathetic. Brain perfusion was measured by 99mTc-labeled bicisate (ECD) brain SPECT and the images were compared using Statistical Parametric Mapping (SPM96). We began by comparing apathetic vs nonapathetic patients, whatever their diagnostic group (whole population), then analyzed them within each group. Twenty-one subjects were apathetic (14 in group 1 and 7 in group 2) and 20 were not (14 in group 1 and 6 in group 2). For the whole population, the Zmap showed a significant decrease in ECD uptake for the apathetic patients in the anterior cingulate (P< 0.002) bilaterally. This area was also identified as hypoactive by SPM analysis in the demented (P< 0.035) and in the nondemented (P< 0.02) apathetic patient groups. Finally, conjunction analysis indicated that the anterior cingulate was the common hypoactive structure of the two apathetic subgroups (Z= 4.35, P< 0.0009). These results point to a close relationship between apathy and the anterior cingulate region.

We present in this paper a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent images by 4D points (xj,yj,zj,ij) and to define a global energy function based on this representation. For minimization, we propose a technique which does not require computing the derivatives of this criterion with respect to the parameters. It can be understood as an extension of the Iterative Closest Point algorithm or as an application of the formalism proposed by L. Cohen (Proceedings of the Fifth International Conference on Computer Vision (ICCV '95), Boston, June 1995). Two parameters enable us to develop a coarse-to-fine strategy both for resolution and for deformation. Our technique presents the advantage of minimizing a well-defined global criterion, to deal with various classes of transformations (for example, rigid, affine, volume spline, and radial basis functions), to be simple to implement, and to be efficient in practice. Results on real brain and heart 3D images are presented to demonstrate the validity of our approach. We also explain how one can compute basic statistics on the deformation parameters to constrain the set of possible deformations by learning and to discriminate between different groups.

Many classical image processing tasks can be realized as evaluations of a boolean function over subsets of an image. For instance, the simplicity test used in 3D thinning requires examining the 26 neighbors of each voxel and computing a single boolean function of these inputs. In this article, we show how Binary Decision Diagrams can be used to produce automatically very efficient and compact code for such functions. The total number of operations performed by a generated function is at most one test and one branching for each input value (e.g., in the case of 3D thinning, 26 tests and branchings). At each stage, the function is guaranteed to examine only the pertinent input data, i.e., the values which affect the result. As an example, we consider the 3D simplicity test in digital topology, and thinning processes. We produce functions much faster than our previously optimized implementations [19, 4] and than any other implementation we know of. In the case of 3D simplicity test, on average, at each voxel only 8.7 neighboring voxel values are examined.

En neuro-imagerie, les images de TEP au 18FDG fournissent une information unique de la fonction neuronale. Toutefois, les analyses quantitatives utilisées chez les humains sont moins bien adaptées chez les petits animaux. L’outil logiciel de référence, SPM, permet la comparaison voxel à voxel d’un individu à une population de référence normale. Une alternative à SPM, block matching(BM), devrait conduire à améliorer la sensibilité et les faux-positifs lors des comparaisons. Cette étude vise à évaluer l’utilisation de la méthode de normalisation BM pour l’analyse quantitative en TEP-FDG de cerveau de rats, avec une application au modèle rat pilocarpine-lithium caractéristique de l’épilepsie.

Statistical parametric mapping(SPM) permet une analyse quantitative voxel à voxel d’images de TEP-FDG cérébrales après une première étape de normalisation spatiale sur un modèle anatomique appelé « template ». Dans le cadre de bilans préopératoires de patients suivis pour une épilepsie temporale, cette étude avait pour objectif de tester un logiciel original de normalisation spatiale, le Block-Matching (BM) où des transformations sont réalisées sur des petits block de voxels, un principe permettant de minimiser les artéfacts et de se soustraire à l’utilisation d’un filtre post-traitement des images, contrairement à SPM, en conventionnel.