Biomedical subjects

Nicholas Ayache

Publications and source records attributed to Nicholas Ayache.

At least 19 recordsLinked to original sources

PubMed2006

Measuring brain variability by extrapolating sparse tensor fields measured on sulcal lines.

Modeling and understanding the variability of brain structures is a fundamental problem in neurosciences. Improved mathematical representations of structural brain variation are needed to help detect and understand genetic or disease related sources of abnormality, as well as to improve statistical power when integrating functional brain mapping data across subjects. In this paper, we develop a new mathematical model of normal brain variation based on a large set of cortical sulcal landmarks (72 per brain) delineated in each of 98 healthy human subjects scanned with 3D MRI (age: 51.8+/-6.2 years). We propose an original method to compute an average representation of the sulcal curves, which constitutes the mean anatomy. After affine alignment of the individual data across subjects, the second order moment distribution of the sulcal position is modeled as a sparse field of covariance tensors (symmetric, positive definite matrices). To extrapolate this information to the full brain, one has to overcome the limitations of the standard Euclidean matrix calculus. We propose an affine-invariant Riemannian framework to perform computations with tensors. In particular, we generalize radial basis function (RBF) interpolation and harmonic diffusion partial differential equations (PDEs) to tensor fields. As a result, we obtain a dense 3D variability map that agrees well with prior results on smaller subject samples. Moreover, "leave one (sulcus) out" tests show that our model is globally able to recover the missing information on brain variation when there is a consistent neighboring pattern of variability. Finally, we propose an innovative method to analyze the asymmetry of brain variability. As expected, the greatest asymmetries are found in regions that includes the primary language areas. Interestingly, any such asymmetries in anatomical variance, if it remains after anatomical normalization, could explain why there may be greater power to detect group activation in one hemisphere versus the other in fMRI studies.

Algorithms↗

PubMed2006

A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data.

This paper describes the construction of an atlas of the human basal ganglia. The successive steps of the construction were as follows. First a postmortem specimen was subjected to a MRI acquisition prior to extraction of the brain from the skull. The brain was then cryosectioned (70 microm thickness). One section out of ten (80 sections) was Nissl-stained with cresyl violet, another series of 80 sections was immunostained for the calcium binding protein calbindin. Contours of basal ganglia nuclei including their calbindin-stained functional subdivisions, fiber bundles and ventricles (n=80 structures) were traced from histological sections and digitized. A novelty of this atlas is the MRI acquisition, which represents the core data element of the study. MRI was used for the coregistration of the atlas data and permitted, through multimodal (Nissl, calbindin, images of cryosectioning, T1 and T2 MRI) and 3D optimization, the production of anatomically and geometrically consistent 3D surfaces, which can be sliced through any desired orientation. The atlas MRI is also used for its deformation to provide accurate conformation to the MRI of living patients, thus adding information at the histological level to the patient's MRI volume. This latter aspect will be presented in a forthcoming paper.

Basal Ganglia↗

PubMed2006

Robust mosaicing with correction of motion distortions and tissue deformations for in vivo fibered microscopy.

Real-time in vivo and in situ imaging at the cellular level can be achieved with fibered confocal microscopy. As interesting as dynamic sequences may be, there is a need for the biologist or physician to get an efficient and complete representation of the entire imaged region. For this demand, the potential of this imaging modality is enhanced by using video mosaicing techniques. Classical mosaicing algorithms do not take into account the characteristics of fibered confocal microscopy, namely motion distortions, irregularly sampled frames and non-rigid deformations of the imaged tissue. Our approach is based on a hierarchical framework that is able to recover a globally consistent alignment of the input frames, to compensate for the motion distortions and to capture the non-rigid deformations. The proposed global alignment scheme is seen as an estimation problem on a Lie group. We model the relationship between the motion and the motion distortions to correct for these distortions. An efficient scattered data approximation scheme is proposed both for the construction of the mosaic and to adapt the demons registration algorithm to our irregularly sampled inputs. Controlled experiments have been conducted to evaluate the performance of our algorithm. Results on several sequences acquired in vivo on both human and mouse tissue also demonstrate the relevance of our approach.

Algorithms↗

PubMed2006

Log-Euclidean metrics for fast and simple calculus on diffusion tensors.

Diffusion tensor imaging (DT-MRI or DTI) is an emerging imaging modality whose importance has been growing considerably. However, the processing of this type of data (i.e., symmetric positive-definite matrices), called "tensors" here, has proved difficult in recent years. Usual Euclidean operations on matrices suffer from many defects on tensors, which have led to the use of many ad hoc methods. Recently, affine-invariant Riemannian metrics have been proposed as a rigorous and general framework in which these defects are corrected. These metrics have excellent theoretical properties and provide powerful processing tools, but also lead in practice to complex and slow algorithms. To remedy this limitation, a new family of Riemannian metrics called Log-Euclidean is proposed in this article. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. This new approach is based on a novel vector space structure for tensors. In this framework, Riemannian computations can be converted into Euclidean ones once tensors have been transformed into their matrix logarithms. Theoretical aspects are presented and the Euclidean, affine-invariant, and Log-Euclidean frameworks are compared experimentally. The comparison is carried out on interpolation and regularization tasks on synthetic and clinical 3D DTI data.

Algorithms↗

PubMed2006

Building maps of local apparent conductivity of the epicardium with a 2-D electrophysiological model of the heart.

In this paper, we address the problem of estimating the parameters of an electrophysiological model of the heart from a set of electrical recordings. The chosen model is the reaction-diffusion model on the transmembrane potential proposed by Aliev and Panfilov. For this model of the transmembrane, we estimate a local apparent two-dimensional conductivity from a measured depolarization time distribution. First, we perform an initial adjustment including the choice of initial conditions and of a set of global parameters. We then propose a local estimation by minimizing the quadratic error between the depolarization time computed by the model and the measures. As a first step we address the problem on the epicardial surface in the case of an isotropic version of the Aliev and Panfilov model. The minimization is performed using Brent method without computing the derivative of the error. The feasibility of the approach is demonstrated on synthetic electrophysiological measurements. A proof of concept is obtained on real electrophysiological measures of normal and infarcted canine hearts.

Action Potentials↗

PubMed2006

Reconstruction of coronary arteries from a single rotational X-ray projection sequence.

Cardiovascular diseases remain the primary cause of death in developed countries. In most cases, exploration of possibly underlying coronary artery pathologies is performed using X-ray coronary angiography. Current clinical routine in coronary angiography is directly conducted in two-dimensional projection images from several static viewing angles. However, for diagnosis and treatment purposes, coronary artery reconstruction is highly suitable. The purpose of this study is to provide physicians with a three-dimensional (3-D) model of coronary arteries, e.g., for absolute 3-D measures for lesion assessment, instead of direct projective measures deduced from the images, which are highly dependent on the viewing angle. In this paper, we propose a novel method to reconstruct coronary arteries from one single rotational X-ray projection sequence. As a side result, we also obtain an estimation of the coronary artery motion. Our method consists of three main consecutive steps: 1) 3-D reconstruction of coronary artery centerlines, including respiratory motion compensation; 2) coronary artery four-dimensional motion computation; 3) 3-D tomographic reconstruction of coronary arteries, involving compensation for respiratory and cardiac motions. We present some experiments on clinical datasets, and the feasibility of a true 3-D Quantitative Coronary Analysis is demonstrated.

Algorithms↗

PubMed2006

Differentiation of sCJD and vCJD forms by automated analysis of basal ganglia intensity distribution in multisequence MRI of the brain--definition and evaluation of new MRI-based ratios.

We present a method for the analysis of basal ganglia (including the thalamus) for accurate detection of human spongiform encephalopathy in multisequence magnetic resonance imaging (MRI) of the brain. One common feature of most forms of prion protein diseases is the appearance of hyperintensities in the deep grey matter area of the brain in T2-weighted magnetic resonance (MR) images. We employ T1, T2, and Flair-T2 MR sequences for the detection of intensity deviations in the internal nuclei. First, the MR data are registered to a probabilistic atlas and normalized in intensity. Then smoothing is applied with edge enhancement. The segmentation of hyperintensities is performed using a model of the human visual system. For more accurate results, a priori anatomical data from a segmented atlas are employed to refine the registration and remove false positives. The results are robust over the patient data and in accordance with the clinical ground truth. Our method further allows the quantification of intensity distributions in basal ganglia. The caudate nuclei are highlighted as main areas of diagnosis of sporadic Creutzfeldt-Jakob Disease (sCJD), in agreement with the histological data. The algorithm permitted the classification of the intensities of abnormal signals in sCJD patient FLAIR images with a higher hypersignal in caudate nuclei (10/10) and putamen (6/10) than in thalami. Defining normalized MRI measures of the intensity relations between the internal grey nuclei of patients, we robustly differentiate sCJD and variant CJD (vCJD) patients, in an attempt to create an automatic classification tool of human spongiform encephalopathies.

Algorithms↗

PubMed2006

A novel framework for the 3D analysis of spine deformation modes.

Three-dimensional classification of scoliosis is important. However, analyzing large databases of 3D spine models is a difficult and time consuming task. To facilitate this task a method that automatically extracts the most important deformation modes from a set of 3D spine models is proposed. The 3D spine models are first converted into vertebrae relative positions and orientations. Then, a variability model composed of the Fréchet mean and of a generalized covariance is computed. A principal component analysis is applied to that variability model and the extracted components are converted into deformation modes. Those modes are visualized by animating a 3D spine model where the deformation strength varies (for a given mode). The proposed method was applied to a group of 307 scoliotic patients and meaningful deformation modes were successfully extracted. For example, patients' growth, double curves, simple thoracic curves and lumbar lordosis were extracted in the first four deformation modes. Moreover, the obtained deformation modes are not disconnected from conventional surgical classifications since a logistic regression confirmed that there is a statistically significant relation between King's classification and the first four principal deformation modes. The proposed method successfully extracted important deformation modes from a set of 3D spine models and can be used to refine arbitrary classes (King's or Lenke's classes, for instance), thus helping the design of new clinically relevant 3D classifications.

Databases, Factual↗

PubMed2006

Assessment of brace local action on vertebrae relative poses.

Bracing is a widely used treatment of scoliosis, but there is still no consensus about its actual effect. Previous studies were based on global descriptors of the spine shape (Cobb angle, plane of maximal deformity, etc.). We present a new method to analyze braces effects at a finer scale and to find which vertebral levels are significantly affected by this treatment. The proposed method compares a group of patients treated with a brace and a control group. The 3D spine geometry of the patients from the two groups was digitized on two separate occasions: with and without brace (first group) or two times without brace (control group). The modifications of the vertebrae relative poses (combination of relative translation and rotation between consecutive vertebrae) were then extracted from 3D reconstructions. Centrality and dispersion measures of the relative poses modifications were computed using a method that take into account the non-linearity of the rotation matrix. Then, finally, multivariate hypothesis tests were used to compare the centrality and dispersion of the two groups. The method was applied to 28 untreated scoliotic patients and 41 patients treated with a Boston brace. Significant differences (p<0.01) between the centrality and dispersion measures of the relative poses modifications were respectively found from T1 to T6 and from T8 to L1. Those significant differences concords with the back flattening effect and the spatially limited correction found in other studies; however the proposed method offers a more specific evaluation of the localization of those effects.

Braces↗

PubMed2005

Polyrigid and polyaffine transformations: a novel geometrical tool to deal with non-rigid deformations - application to the registration of histological slices.

We describe in this paper a novel kind of geometrical transformations, named polyrigid and polyaffine. These transformations efficiently code for locally rigid or affine deformations with a small number of intuitive parameters. They can describe compactly large rigid or affine movements, unlike most free-form deformation classes. Very flexible, this tool can be readily adapted to a large variety of situations, simply by tuning the number of rigid or affine components and the number of parameters describing their regions of influence. The displacement of each spatial position is defined by a continuous trajectory that follows a differential equation which averages the influence of each rigid or affine component. We show that the resulting transformations are diffeomorphisms, smooth with respect to their parameters. We devise a new and flexible numerical scheme to allow a trade-off between computational efficiency and closeness to the ideal diffeomorphism. Our algorithms are implemented within the Insight Toolkit, whose generic programming style offers rich facilities for prototyping. In this context, we derive an effective optimization strategy of the transformations which demonstrates that this new tool is highly suitable for inference. The whole framework is exemplified successfully with the registration of histological slices. This choice is challenging, because these data often present locally rigid deformations added during their acquisition, and can also present a loss of matter, which makes their registration even more difficult. Powerful and flexible, this new tool opens up large perspectives, in non-rigid 3D rigid registration as well as in shape statistics.

Algorithms↗

PubMed2005

Atlas-based automatic segmentation of MR images: validation study on the brainstem in radiotherapy context.

PURPOSE: Brain tumor radiotherapy requires the volume measurements and the localization of several individual brain structures. Any tool that can assist the physician to perform the delineation would then be of great help. Among segmentation methods, those that are atlas-based are appealing because they are able to segment several structures simultaneously, while preserving the anatomy topology. This study aims to evaluate such a method in a clinical context. METHODS AND MATERIALS: The brain atlas is made of two three-dimensional (3D) volumes: the first is an artificial 3D magnetic resonance imaging (MRI); the second consists of the segmented structures in this artificial MRI. The elastic registration of the artificial 3D MRI against a patient 3D MRI dataset yields an elastic transformation that can be applied to the labeled image. The elastic transformation is obtained by minimizing the sum of the square differences of the image intensities and derived from the optical flow principle. This automatic delineation (AD) enables the mapping of the segmented structures onto the patient MRI. Parameters of the AD have been optimized on a set of 20 patients. Results are obtained on a series of 6 patients' MRI. A comprehensive validation of the AD has been conducted on performance of atlas-based segmentation in a clinical context with volume, position, sensitivity, and specificity that are compared by a panel of seven experimented physicians for the brain tumor treatments. RESULTS: Expert interobserver volume variability ranged from 16.70 cm(3) to 41.26 cm(3). For patients, the ratio of minimal to maximal volume ranged from 48% to 70%. Median volume varied from 19.47 cm(3) to 27.66 cm(3) and volume of the brainstem calculated by AD varied from 17.75 cm(3) to 24.54 cm(3). Medians of experts ranged, respectively, for sensitivity and specificity, from 0.75 to 0.98 and from 0.85 to 0.99. Median of AD were, respectively, 0.77 and 0.97. Mean of experts ranged, respectively, from 0.78 to 0.97 and from 0.86 to 0.99. Mean of AD were, respectively, 0.76 and 0.97. CONCLUSIONS: Results demonstrate that the method is repeatable, provides a good trade-off between accuracy and robustness, and leads to reproducible segmentation and labeling. These results can be improved by enriching the atlas with the rough information of tumor or by using different laws of deformation for the different structures. Qualitative results also suggest that this method can be used for automatic segmentation of other organs such as neck, thorax, abdomen, pelvis, and limbs.

Algorithms↗

PubMed2005

Fast and simple calculus on tensors in the log-Euclidean framework.

Computations on tensors have become common with the use of DT-MRI. But the classical Euclidean framework has many defects, and affine-invariant Riemannian metrics have been proposed to correct them. These metrics have excellent theoretical properties but lead to complex and slow algorithms. To remedy this limitation, we propose new metrics called Log-Euclidean. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. Indeed, Log-Euclidean computations are Euclidean computations in the domain of matrix logarithms. Theoretical aspects are presented and experimental results for multilinear interpolation and regularization of tensor fields are shown on synthetic and real DTI data.

Algorithms↗

PubMed2005

Mosaicing of confocal microscopic in vivo soft tissue video sequences.

Fibered confocal microscopy allows in vivo and in situ imaging with cellular resolution. The potentiality of this imaging modality is extended in this work by using video mosaicing techniques. Two novelties are introduced. A robust estimator based on statistics for Riemannian manifolds is developed to find a globally consistent mapping of the input frames to a common coordinate system. A mosaicing framework using an efficient scattered data fitting method is proposed in order to take into account the non-rigid deformations and the irregular sampling implied by in vivo fibered confocal microscopy. Results on 50 images of a live mouse colon demonstrate the effectiveness of the proposed method.

Algorithms↗

PubMed2005

Incorporating statistical measures of anatomical variability in atlas-to-subject registration for conformal brain radiotherapy.

Deforming a digital atlas towards a patient image allows the simultaneous segmentation of several structures. Such an intersubject registration is difficult as the deformations to recover are highly inhomogeneous. A priori information about the local amount of deformation to expect is precious, since it allows to optimally balance the quality of the matching versus the regularity of the deformation. However, intersubject variability makes it hard to heuristically estimate the degree of deformation. Indeed, the sizes and shapes of various structures differ greatly and their relative positions vary in a rather complex manner. In this article, we perform a statistical study of the deformations yielded by the registration of an image database with an anatomical atlas, and we propose methods to re-inject this information into the registration. We show that this provides more accurate segmentations of brain structures.

Algorithms↗

PubMed2005

Hybrid formulation of the model-based non-rigid registration problem to improve accuracy and robustness.

We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term). The robustness of the algorithm is achieved through the introduction of an outliers rejection step in this gradual registration process. We ensure the validity of the deformation by the use of a biomechanical model of the brain specific to the patient, discretized with the finite element method. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift up to 13 mm.

Algorithms↗

PubMed2005

Retrospective cross-evaluation of an histological and deformable 3D atlas of the basal ganglia on series of Parkinsonian patients treated by deep brain stimulation.

In functional neurosurgery, there is a growing need for accurate localization of the functional targets. Since deep brain stimulation (DBS) of the Vim thalamic nucleus has been proposed for the treatment of Parkinson's disease, the target has evolved toward the globus pallidus and subthalamic nucleus (STN) and the therapeutic indications have enlarged to include psychiatric disorders such as Tourette syndrome or obsessive compulsive disorders. In these pathologies, the target has been restrained to smaller functional subterritories of the basal ganglia, requiring more refined techniques to localize smaller and smallerbrain regions, often invisible in routine clinical MRI. Different strategies have been developed to identify such deep brain targets. Direct methods can identify structures in the MRI itself, but only the larger ones. Indirect methods are based on the use of anatomical atlases. The present strategy comprised a 3D histological atlas and the MRI of the same brain specimen, and deformation methodology developped to fit the atlas toward the brain of any given patient. In this paper, this method is evaluated in the aim of being applied to further studies of anatomo-clinical correlation. The accuracy of the method is first discussed, followed by the study of short series of Parkinsonian patients treated by DBS, allowing to compare the deformed atlas with various per- and post-operative data.

Basal Ganglia↗

PubMed2005

New ratios for the detection and classification of CJD in multisequence MRI of the brain.

We present a method for the analysis of deep grey brain nuclei for accurate detection of human spongiform encephalopathy in multisequence MRI of the brain. We employ T1, T2 and FLAIR-T2 MR sequences for the detection of intensity deviations in the internal nuclei. The MR data are registered to a probabilistic atlas and normalised in intensity prior to the segmentation of hyperintensities using a foveal model. Anatomical data from a segmented atlas are employed to refine the registration and remove false positives. The results are robust over the patient data and in accordance to the clinical ground truth. Our method further allows the quantification of intensity distributions in basal ganglia. sCJD patient FLAIR images are classified with a more significant hypersignal in caudate nuclei (10/10) and putamen (6/10) than in thalami. Defining normalised MRI measures of the intensity relations between the internal grey nuclei of patients, we robustly differentiate sCJD and variant CJD (vCJD) patients, as an attempt towards the automatic detection and classification of human spongiform encephalopathies.

Algorithms↗

PubMed2005

Robust nonrigid registration to capture brain shift from intraoperative MRI.

We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.

Algorithms↗