Biomedical subjects

Jean-Paul Armspach

Publications and source records attributed to Jean-Paul Armspach.

7 recordsLinked to original sources

PubMed2005

Region-growing segmentation of brain vessels: an atlas-based automatic approach.

PURPOSE: To propose an atlas-based method that uses both phase and magnitude images to integrate anatomical information in order to improve the segmentation of blood vessels in cerebral phase-contrast magnetic resonance angiography (PC-MRA). MATERIAL AND METHODS: An atlas of the whole head was developed to store the anatomical information. The atlas divides a magnitude image into several vascular areas, each of which has specific vessel properties. It can be applied to any magnitude image of an entire or nearly entire head by deformable matching, which helps to segment blood vessels from the associated phase image. The segmentation method used afterwards consists of a topology-preserving, region-growing algorithm that uses adaptive threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels that are selected according to their grayscale value and the variation of values in their neighborhood. The topology preservation is guaranteed because only simple points are selected during the growing process. RESULTS: The method was performed on 40 PC-MRA images of the brain. The results were validated using maximum-intensity projection (MIP) and three-dimensional surface rendering visualization, and compared with results obtained with two non-atlas-based methods. CONCLUSION: The results show that the proposed method significantly improves the segmentation of cerebral vascular structures from PC-MRA. These experiments tend to prove that the use of vascular atlases is an effective way to optimize vessel segmentation of cerebral images.

Adult↗

PubMed2005

Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping.

RATIONALE AND OBJECTIVES: Most methods used in functional MRI (fMRI) brain mapping require restrictive assumptions about the shape and timing of the fMRI signal in activated voxels. Consequently, fMRI data may be partially and misleadingly characterized, leading to suboptimal or invalid inference. To limit these assumptions and to capture the broad range of possible activation patterns, a novel statistical fMRI brain mapping method is proposed. It relies on hidden semi-Markov event sequence models (HSMESMs), a special class of hidden Markov models (HMMs) dedicated to the modeling and analysis of event-based random processes. MATERIALS AND METHODS: Activation detection is formulated in terms of time coupling between (1) the observed sequence of hemodynamic response onset (HRO) events detected in the voxel's fMRI signal and (2) the "hidden" sequence of task-induced neural activation onset (NAO) events underlying the HROs. Both event sequences are modeled within a single HSMESM. The resulting brain activation model is trained to automatically detect neural activity embedded in the input fMRI data set under analysis. The data sets considered in this article are threefold: synthetic epoch-related, real epoch-related (auditory lexical processing task), and real event-related (oddball detection task) fMRI data sets. RESULTS: Synthetic data: Activation detection results demonstrate the superiority of the HSMESM mapping method with respect to a standard implementation of the statistical parametric mapping (SPM) approach. They are also very close, sometimes equivalent, to those obtained with an "ideal" implementation of SPM in which the activation patterns synthesized are reused for analysis. The HSMESM method appears clearly insensitive to timing variations of the hemodynamic response and exhibits low sensitivity to fluctuations of its shape (unsustained activation during task). Real epoch-related data: HSMESM activation detection results compete with those obtained with SPM, without requiring any prior definition of the expected activation patterns thanks to the unsupervised character of the HSMESM mapping approach. Along with activation maps, the method offers a wide range of additional fMRI analysis functionalities, including activation lag mapping, activation mode visualization, and hemodynamic response function analysis. Real event-related data: Activation detection results confirm and validate the overall strategy that consists in focusing the analysis on the transients, time-localized events that are the HROs. CONCLUSION: All the experiments performed on synthetic and real fMRI data demonstrate the relevance of HSMESMs in fMRI brain mapping. In particular, the statistical character of these models, along with their learning and generalizing abilities are of particular interest when dealing with strong variabilities of the active fMRI signal across time, space, experiments, and subjects.

Adolescent↗

PubMed2005

3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization.

This paper deals with topology preservation in three-dimensional (3-D) deformable image registration. This work is a nontrivial extension of, which addresses the case of two-dimensional (2-D) topology preserving mappings. In both cases, the deformation map is modeled as a hierarchical displacement field, decomposed on a multiresolution B-spline basis. Topology preservation is enforced by controlling the Jacobian of the transformation. Finding the optimal displacement parameters amounts to solving a constrained optimization problem: The residual energy between the target image and the deformed source image is minimized under constraints on the Jacobian. Unlike the 2-D case, in which simple linear constraints are derived, the 3-D B-spline-based deformable mapping yields a difficult (until now, unsolved) optimization problem. In this paper, we tackle the problem by resorting to interval analysis optimization techniques. Care is taken to keep the computational burden as low as possible. Results on multipatient 3-D MRI registration illustrate the ability of the method to preserve topology on the continuous image domain.

Algorithms↗

PubMed2005

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models.

In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs). Neural activation detection is formulated at the voxel level in terms of time coupling between the sequence of hemodynamic response onsets (HROs) observed in the fMRI signal, and an HSMESM of the hidden sequence of task-induced neural activations. The sequence of HRO events is derived from a continuous wavelet transform (CWT) of the fMRI signal. The brain activation HSMESM is built from the timing information of the input stimulation protocol. The rich mathematical framework of HSMESMs makes these models an effective and versatile approach for fMRI data analysis. Solving for the HSMESM Evaluation and Learning problems enables the model to automatically detect neural activation embedded in a given set of fMRI signals, without requiring any template basis function or prior shape assumption for the fMRI response. Solving for the HSMESM Decoding problem allows to enrich brain mapping with activation lag mapping, activation mode visualizing, and hemodynamic response function analysis. Activation detection results obtained on synthetic and real epoch-related fMRI data demonstrate the superiority of the HSMESM mapping method with respect to a real application case of the statistical parametric mapping (SPM) approach. In addition, the HSMESM mapping method appears clearly insensitive to timing variations of the hemodynamic response, and exhibits low sensitivity to fluctuations of its shape.

Algorithms↗

PubMed2004

On the modeling, construction, and evaluation of a probabilistic atlas of brain perfusion.

To detect subtle, abnormal perfusion patterns in brain single photon emission computer tomography (SPECT) images, it is necessary to develop quantitative methods in which computer-aided statistical analysis takes advantage of information present in databases of normal subjects. The purpose of this study was to evaluate and examine aspects of the creation and the modeling power of three statistical models for representing brain perfusion as observed in ECD-SPECT. The first model is a local model of voxel-by-voxel mean and variance. The second model is a PCA-based global model that accounts for covariance patterns in the images. The third model is an original model that is a non-linear extension to the second model. This model is based on robust statistics for modeling abnormalities. To evaluate the models, a leave-one-out procedure combined with simulations of abnormal perfusion patterns was adopted. Abnormal perfusion patterns were simulated at different locations in the brain, with different intensities and different sizes. The procedure yields receiver operator characteristics (ROC) that present a combined measure of model-fit and model-sensitivity at detecting abnormalities. The scheme can further be used to compare models as well as the influence of different preprocessing steps. In particular, the influence of different registration approaches is studied and analyzed. The results show that the original non-linear model always performed better than the other models. Finally, location-dependent detection performance was found. Most notably, a higher variation of perfusion was observed in the right frontal cortex than in the other locations studied.

Algorithms↗

PubMed2003

A robust statistics-based global energy function for the alignment of serially acquired autoradiographic sections.

Autoradiographic analysis of the functional changes occurring in the rat brain are most often performed on coronal sections that allow a good insight into the events occurring at the structural level but lacks the 3D context which is necessary to fully understand the involvement of the brain structures in specific situations like focal seizures with or without generalization. Therefore a robust, fully-automated algorithm for the registration of serially acquired autoradiographic sections is presented. The method accounts for the main difficulties of autoradiographic alignment: corrupted data (cuts and tears), dissimilarities or discontinuities between slices, non parallel or missing slices. The approach relies on the minimization of a global energy function based on robust statistics. The energy function measures the similarity between a slice and its neighborhood in the 3D volume. No particular direction is privileged in the method, so that global offsets, biases in the estimation or error propagations are avoided. The method is evaluated qualitatively and quantitatively on real autoradiographic data. Rat brain autoradiographic volumes are reconstructed with registration errors less than 1 degree in rotation and less than 1 pixel in translation.

Algorithms↗

PubMed2002

Co-occurrence of infantile epileptic seizures and childhood paroxysmal choreoathetosis in one family: clinical, EEG, and SPECT characterization of episodic events.

The co-occurrence of infantile convulsions and childhood paroxysmal choreoathetosis (ICCA syndrome) has recently been reported in several families. The pattern of familial clustering observed is consistent with a single locus mutation which has been mapped onto the pericentromeric region of chromosome 16. We studied the main clinical, electroencephalogram (EEG), and single photon emission computed tomography (SPECT) characteristics of episodic events in a new family presenting clinical features similar to that described in the ICCA syndrome. In the first year of life, a mother and her two daughters suffered from rare afebrile seizures lasting from 30 seconds to 15 minutes. Ictal EEG recording in one daughter at 7 months of age showed bilateral polyspikes with a posterior predominance. In the three patients, epileptic seizures regressed within a few weeks, and never reoccurred. At the age of 7 and 12 years, respectively, the two daughters presented daily brief (20 seconds to 1 minute) involuntary choreoathetotic episodes. In 10 of these attacks, EEG did not show any epileptiform abnormalities. In both sisters, an ictal SPECT was performed during a choreoathetotic episode. Subtracting the ictal SPECT from the interictal SPECT coregistered to magnetic resonance imaging (MRI) revealed significant modifications in the local cerebral perfusion in the sensorimotor cortex, the supplementary motor areas, and pallidum. Carbamazepine completely suppressed paroxysmal dyskinesias. These observations, together with literature data, suggest that in this syndrome, depending on brain maturation, the same genetic abnormality may result in different paroxysmal neurological symptoms.

Athetosis↗