Benoit M. Dawant

Morphometric analysis of white matter lesions in MR images: method and validation

The analysis of MR images is evolving from qualitative to quantitative. More and more, the question asked by clinicians is how much and where, rather than a simple statement on the presence or absence of abnormalities. The authors present a study in which the results obtained with a semiautomatic, multispectral segmentation technique are quantitatively compared to manually delineated regions. The core of the semiautomatic image analysis system is a supervised artificial neural network classifier augmented with dedicated preand postprocessing algorithms, including anisotropic noise filtering and a surface-fitting method for the correction of spatial intensity variations. The study was focused on the quantitation of white matter lesions in the human brain. A total of 36 images from six brain volumes was analyzed twice by each of two operators, under supervision of a neuroradiologist. Both the intra- and interrater variability of the methods were studied in terms of the average tissue area detected per slice, the correlation coefficients between area measurements, and a measure of similarity derived from the kappa statistic. The results indicate that, compared to a manual method, the use of the semiautomatic technique not only facilitates the analysis of the images, but also has similar or lower intra- and interrater variabilities.

The adaptive bases algorithm for intensity-based nonrigid image registration

Nonrigid registration of medical images is important for a number of applications such as the creation of population averages, atlas-based segmentation, or geometric correction of functional magnetic resonance imaging (IMRI) images to name a few. In recent years, a number of methods have been proposed to solve this problem, one class of which involves maximizing a mutual information (Ml)-based objective function over a regular grid of splines. This approach has produced good results but its computational complexity is proportional to the compliance of the transformation required to register the smallest structures in the image. Here, we propose a method that permits the spatial adaptation of the transformations compliance. This spatial adaptation allows us to reduce the number of degrees of freedom in the overall transformation, thus speeding up the process and improving its convergence properties. To develop this method, we introduce several novelties: 1) we rely on radially symmetric basis functions rather than B-splines traditionally used to model the deformation field; 2) we propose a metric to identify regions that are poorly registered and over which the transformation needs to be improved; 3) we partition the global registration problem into several smaller ones; and 4) we introduce a new constraint scheme that allows us to produce transformations that are topologically correct. We compare the approach we propose to more traditional ones and show that our new algorithm compares favorably to those in current use.

Correction of intensity variations in MR images for computer-aided tissue classification

A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the segmentation and the quantitative analysis of MR images. However, the efficacy of these techniques is affected by acquisition artifacts such as inter-slice, intra-slice, and inter-patient intensity variations. Here a new approach to the correction of intra-slice intensity variations is presented. Results demonstrate that the correction process enhances the performance of backpropagation neural network classifiers designed for the segmentation of the images. Two slightly different versions of the method are presented. The first version fits an intensity correction surface directly to reference points selected by the user in the images. The second version fits the surface to reference points obtained by an intermediate classification operation. Qualitative and quantitative evaluation of both methods reveals that the first one leads to a better correction of the images than the second but that it is more sensitive to operator errors.

Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study

This work presents an investigation of the potential of artificial neural networks for classification of registered magnetic resonance and X-ray computer tomography images of the human brain. First, topological and learning parameters are established experimentally. Second, the learning and generalization properties of the neural networks are compared to those of a classical maximum likelihood classifier and the superiority of the neural network approach is demonstrated when small training sets are utilized. Third, the generalization properties of the neural networks are utilized to develop an adaptive learning scheme able to overcome interslice intensity variations typical of MR images. This approach permits the segmentation of image volumes based on training sets selected on a single slice. Finally, the segmentation results obtained both with the artificial neural network and the maximum likelihood classifiers are compared to contours drawn manually.

Integrating spatially resolved three-dimensional MALDI IMS with in vivo magnetic resonance imaging

We have developed a method for integrating three dimensional–volume reconstructions of spatially resolved matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) ion images of whole mouse heads with high-resolution images from other modalities in an animal-specific manner. This approach enabled us to analyze proteomic profiles from MALDI IMS data with corresponding in vivo data provided by magnetic resonance imaging.

Retrospective intermodality registration techniques for images of the head: surface-based versus volume-based

A blinded evaluation of two groups of retrospective image registration techniques was performed using as a gold standard a prospective marker-based registration method, and we compared the performance of one group with the other. By grouping the techniques as volume-based or surface-based, we could make some interesting conclusions. In order to ensure blindness, all retrospective registrations were performed by participants who had no knowledge of the gold-standard results until after their results had been submitted. Image volumes of three modalities (X-ray CT, MRI and PET) were obtained from patients undergoing neurosurgery on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to outside collaborators, who then performed retrospective registrations on the volumes, calculating transformations from CT to MRI and/or from PET to MRI. The accuracy of each registration was then evaluated. The accuracy is measured at multiple volumes of interest. The volume-based techniques in this study tended to give substantially more accurate and reliable results than the surface-based ones for the CT-to-MRI registration tasks, and slightly more accurate results for the PET-to-MRI tasks. Analysis of these results revealed that the rotational component of error was more pronounced for the surface-based group. It was also apparent that all of the registration techniques we examined have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Amphetamine-Induced Displacement of ( 18 F) Fallypride in Striatum and Extrastriatal Regions in Humans

This study examined D-amphetamine (D-AMPH)-induced displacements of [18F] fallypride in striatal and extrastriatal regions and the correlations of these displacements with cognition, affect, and sensation-seeking behavior. In all, 14 normal subjects, six females and eight males (ages 21–32, mean age 25.9 years), underwent positron emission tomography (PET) with [18F]fallypride before and 3 h after a 0.43 mg/kg oral dose of D-AMPH. Levels of dopamine (DA) D2 receptor density were calculated with the reference region method of Lammerstma. Percent displacements in striatal and extrastriatal regions were calculated for the caudate, putamen, ventral striatum, medial thalamus, amygdala, substantia nigra, and temporal cortex. Correlations of changes in cognition, affect, and sensation seeking with parametric images of D-AMPH-induced DA release were computed. Significant displacements were seen in the caudate, putamen, ventral striatum substantia nigra, and temporal cortex with a trend level change in the amygdala. Greatest displacements were seen in striatal subdivisions—5.6% in caudate, 11.2% in putamen, 7.2% in ventral striatum, and 6.6% in substantia nigra. Lesser decrements were seen in amygdala—4.4%, temporal cortex—3.7%, and thalamus—2.8%. Significant clusters of correlations of regional DA release with cognition and sensation-seeking behavior were observed. The current study demonstrates that [18F]fallypride PET studies using oral D-AMPH (0.43 mg/kg) can be used to study D-AMPH-induced DA release in the striatal and extrastriatal regions in humans, and their relationship with cognition and sensation-seeking behavior.

Dopamine D2 receptor levels in striatum, thalamus, substantia nigra, limbic regions, and cortex in schizophrenic subjects.

BACKGROUND Studies in schizophrenic patients have reported dopaminergic abnormalities in striatum, substantia nigra, thalamus, anterior cingulate, hippocampus, and cortex that have been related to positive symptoms and cognitive impairments. METHODS [(18)F]fallypride positron emission tomography studies were performed in off-medication or never-medicated schizophrenic subjects (n = 11, 6 men, 5 women; mean age of 30.5 +/- 8.0 [SD] years; 4 drug-naive) and age-matched healthy subjects (n = 11, 5 men, 6 women, mean age of 31.6 +/- 9.2 [SD]) to examine dopamine D(2) receptor (DA D(2)r) levels in the caudate, putamen, ventral striatum, medial thalamus, posterior thalamus, substantia nigra, amygdala, temporal cortex, anterior cingulate, and hippocampus. RESULTS In schizophrenic subjects, increased DA D(2)r levels were seen in the substantia nigra bilaterally; decreased levels were seen in the left medial thalamus. Correlations of symptoms with ROI data demonstrated a significant correlation of disorganized thinking/nonparanoid delusions with the right temporal cortex ROI (r = .94, p = .0001), which remained significant after correction for multiple comparisons (p < .03). Correlations of symptoms with parametric images of DA D(2)r levels revealed no significant clusters of correlations with negative symptoms but significant clusters of positive correlations of total positive symptoms, delusions and bizarre behavior with the lateral and anterior temporal cortex, and hallucinations with the left ventral striatum. CONCLUSIONS The results of this study demonstrate abnormal DA D(2)r-mediated neurotransmission in the substantia nigra consistent with nigral dysfunction in schizophrenia and suggest that both temporal cortical and ventral striatal DA D(2)r mediate positive symptoms.

Occupancy of Striatal and Extrastriatal Dopamine D2 Receptors by Clozapine and Quetiapine

Clozapine and quetiapine have a low incidence of extrapyramidal side effects at clinically effective doses, which appears to be related to their significantly lower occupancy of striatal dopamine D2 receptors (DA D2r) compared to typical antipsychotic drugs (APDs). Animal studies have indicated that clozapine and quetiapine produce selective effects on cortical and limbic regions of the brain and in particular on dopaminergic neurotransmission in these regions. Previous PET and SPECT studies have reported conflicting results regarding whether clozapine produces preferential occupancy of cortical DA D2r. To examine whether clozapine and/or quetiapine produce preferential occupancy of DA D2r in cortex and limbic regions, we studied the occupancy of putamenal, ventral striatal, thalamic, amygdala, substantia nigra, and temporal cortical DA D2r using PET with [18F]fallypride in six schizophrenic subjects receiving clozapine monotherapy and in seven schizophrenic subjects receiving quetiapine monotherapy. Doses were chosen clinically to minimize psychopathology at tolerable levels of side effects such as drowsiness. All had minimal positive symptoms at the time of the study. Regional receptor occupancies were estimated using mean regional DA D2r levels calculated for 10 off-medication schizophrenic subjects. Both clozapine and quetiapine produced lower levels of putamenal DA D2r occupancy than those reported for typical APDs, 47.8 and 33.5%, respectively. Clozapine produced preferential occupancy of temporal cortical vs putamenal DA D2r, 59.8% (p=0.05, corrected for multiple comparisons), and significantly lower levels of occupancy in the substantia nigra, 18.4% (p=0.0015, corrected for multiple comparisons). Quetiapine also produced preferential occupancy of temporal cortical DA D2r, 46.9% (p=0.03, corrected for multiple comparisons), but did not spare occupancy of substantia nigra DA D2r. The therapeutic effects of clozapine and quetiapine appear to be achieved at less than the 65% threshold for occupancy seen with typical APDs, consistent with the involvement of non-DA D2r mechanisms in at least partially mediating the therapeutic effects of these drugs. Preferential occupancy of cortical DA D2r, sparing occupancy of substantia nigra receptors, and non-DA D2r-mediated actions may contribute to the antipsychotic actions of these and other atypical APDs.

Computer-aided placement of deep brain stimulators: from planningto intraoperative guidance

In current practice, optimal placement of deep-brain stimulators (DBSs) used to treat movement disorders in patients with Parkinsons disease and essential tremor is an iterative procedure. A target is chosen preoperatively based on anatomical landmarks identified on magnetic resonance images. This point is used as an initial position that is refined intraoperatively using both microelectrode recordings and macrostimulation. In this paper, we report on our current progress toward developing a system for the computer-assisted preoperative selection of target points and for the intraoperative adjustment of these points. The system consists of a deformable atlas of optimal target points that can be used to select automatically the preoperative target, of an electrophysiological atlas, and of an intraoperative interface. Results we have obtained show that automatic prediction of target points is an achievable goal. Our results also indicate that electrophysiological information could be used to resolve structures not visible in anatomic images, thus improving both preoperative and intraoperative guidance. Our intraoperative system has reached the stage of a working prototype and we compare targeting accuracy as well as the number of paths needed to reach the targets with our system and with the method in current clinical use.