Jean-Paul Armspach

Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution

The automatic analysis of subtle changes between MRI scans is an important tool for assessing disease evolution over time. Manual labeling of evolutions in 3D data sets is tedious and error prone. Automatic change detection, however, remains a challenging image processing problem. A variety of MRI artifacts introduce a wide range of unrepresentative changes between images, making standard change detection methods unreliable. In this study we describe an automatic image processing system that addresses these issues. Registration errors and undesired anatomical deformations are compensated using a versatile multiresolution deformable image matching method that preserves significant changes at a given scale. A nonlinear intensity normalization method is associated with statistical hypothesis test methods to provide reliable change detection. Multimodal data is optionally exploited to reduce the false detection rate. The performance of the system was evaluated on a large database of 3D multimodal, MR images of patients suffering from relapsing remitting multiple sclerosis (MS). The method was assessed using receiver operating characteristics (ROC) analysis, and validated in a protocol involving two neurologists. The automatic system outperforms the human expert, detecting many lesion evolutions that are missed by the expert, including small, subtle changes.

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.

IN VIVO DETERMINATION OF MULTIEXPONENTIAL T2 RELAXATION IN THE BRAIN OF PATIENTS WITH MULTIPLE SCLEROSIS

In vivo measurement of T2 relaxation times in multiple sclerosis (MS) lesions by magnetic resonance imaging (MRI) is potentially useful for the evaluation of the disease activity. Seven patients with definite MS were investigated over a period of three years (19 examinations), using a whole-body MRI scanner operating at 0.15 T with a specially designed high-power radio-frequency head coil. A modified CPMG sequence with a 180 degree pulse interval of TE = 6 msec and 128 echoes was used for the T2 relaxation measurement of the areas of increased signal (AIS) and white matter (WM). A biexponential T2 analysis of each pixel of the spin-echo images was computed. The T2 relaxation processes were found to be a monoexponential function in WM. The T2 relaxation times of apparently normal white matter in MS patients was significantly longer than in control subjects. The T2 relaxation curves of the AIS were found in most cases to fit a biexponential function characterized by a short and a long T2. T2 long relaxation times of AIS were spread out over a wide range (150-560 msec). The study of T2 long histograms shows that some AIS can be divided into two or three parts depending on the T2 long values. Each of these parts may correspond to a pathological process such as edema, demyelination and gliosis. Evolution of T2 relaxation times over a period of time cannot as yet be correlated with modifications in the clinical state.

Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains

In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.

Registration of MR/MR and MR/SPECT Brain Images by Fast Stochastic Optimization of Robust Voxel Similarity Measures

This paper describes a robust, fully automated algorithm to register intrasubject 3D single and multimodal images of the human brain. The proposed technique accounts for the major limitations of the existing voxel similarity-based methods: sensitivity of the registration to local minima of the similarity function and inability to cope with gross dissimilarities in the two images to be registered. Local minima are avoided by the implementation of a stochastic iterative optimization technique (fast simulated annealing). In addition, robust estimation is applied to reject outliers in case the images show significant differences (due to lesion evolution, incomplete acquisition, non-Gaussian noise, etc.). In order to evaluate the performance of this technique, 2D and 3D MR and SPECT human brain images were artificially rotated, translated, and corrupted by noise. A test object was acquired under different angles and positions for evaluating the accuracy of the registration. The approach has also been validated on real multiple sclerosis MR images of the same patient taken at different times. Furthermore, robust MR/SPECT image registration has permitted the representation of functional features for patients with partially complex seizures. The fast simulated annealing algorithm combined with robust estimation yields registration errors that are less than 1 degree in rotation and less than 1 voxel in translation (image dimensions of 128(3)). It compares favorably with other standard voxel similarity-based approaches.

Retrospective evaluation of a topology preserving non-rigid registration method

This paper proposes a comprehensive evaluation of a monomodal B-spline-based non-rigid registration algorithm allowing topology preservation in 3-D. This article is to be considered as the companion of [Noblet, V., Heinrich, C., Heitz, F., Armspach, J.-P., 2005. 3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization. IEEE Transactions on Image Processing, 14 (5), 553-566] where this algorithm, based on the minimization of an objective function, was introduced and detailed. Overall assessment is based on the estimation of synthetic deformation fields, on average brain construction, on atlas-based segmentation and on landmark mapping. The influence of the model parameters is characterized. Comparison between several objective functions is carried out and impact of their symmetrization is pointed out. An original intensity normalization scheme is also introduced, leading to significant improvements of the registration quality. The comparison benchmark is the popular demons algorithm [Thirion, J.-P., 1998. Image matching as a diffusion process: an analogy with Maxwells demons. Medical Image Analysis, 2 (3), 243-260], that exhibited best results in a recent comparison between several non-rigid 3-D registration methods [Hellier, P., Barillot, C., Corouge, I., Gibaud, B., Le Goualher, G., Collins, D.L., Evans, A., Malandain, G., Ayache, N., Christensen, G.E., Johnson, H.J., 2003. Retrospective evaluation of intersubject brain registration. IEEE Transactions on Medical Imaging, 22 (9), 1120-1130]. The topology preserving B-spline-based method proved to outperform the commonly available ITK implementation of the demons algorithms on many points. Some limits of intensity-based registration methods are also highlighted through this work.

Magnetic resonance angiography: From anatomical knowledge modeling to vessel segmentation

Magnetic resonance angiography (MRA) has become a common way to study cerebral vascular structures. Indeed, it enables to obtain information on flowing blood in a totally non-invasive and non-irradiant fashion. MRA exams are generally performed for three main applications: detection of vascular pathologies, neurosurgery planning, and vascular landmark detection for brain functional analysis. This large field of applications justifies the necessity to provide efficient vessel segmentation tools. Several methods have been proposed during the last fifteen years. However, the obtained results are still not fully satisfying. A solution to improve brain vessel segmentation from MRA data could consist in integrating high-level a priori knowledge in the segmentation process. A preliminary attempt to integrate such knowledge is proposed here. It is composed of two methods devoted to phase contrast MRA (PC MRA) data. The first method is a cerebral vascular atlas creation process, composed of three steps: knowledge extraction, registration, and data fusion. Knowledge extraction is performed using a vessel size determination algorithm based on skeletonization, while a topology preserving non-rigid registration method is used to fuse the information into the atlas. The second method is a segmentation process involving adaptive sets of gray-level hit-or-miss operators. It uses anatomical knowledge modeled by the cerebral vascular atlas to adapt the parameters of these operators (number, size, and orientation) to the searched vascular structures. These two methods have been tested by creating an atlas from a 18 MRA database, and by using it to segment 30 MRA images, comparing the results to those obtained from a region-growing segmentation method.

White Matter Atrophy and Cognitive Dysfunctions in Neuromyelitis Optica

Neuromyelitis optica (NMO) is an inflammatory disease of central nervous system characterized by optic neuritis and longitudinally extensive acute transverse myelitis. NMO patients have cognitive dysfunctions but other clinical symptoms of brain origin are rare. In the present study, we aimed to investigate cognitive functions and brain volume in NMO. The study population consisted of 28 patients with NMO and 28 healthy control subjects matched for age, sex and educational level. We applied a French translation of the Brief Repeatable Battery (BRB-N) to the NMO patients. Using SIENAx for global brain volume (Grey Matter, GM; White Matter, WM; and whole brain) and VBM for focal brain volume (GM and WM), NMO patients and controls were compared. Voxel-level correlations between diminished brain concentration and cognitive performance for each tests were performed. Focal and global brain volume of NMO patients with and without cognitive impairment were also compared. Fifteen NMO patients (54%) had cognitive impairment with memory, executive function, attention and speed of information processing deficits. Global and focal brain atrophy of WM but not Grey Matter (GM) was found in the NMO patients group. The focal WM atrophy included the optic chiasm, pons, cerebellum, the corpus callosum and parts of the frontal, temporal and parietal lobes, including superior longitudinal fascicle. Visual memory, verbal memory, speed of information processing, short-term memory and executive functions were correlated to focal WM volumes. The comparison of patients with, to patients without cognitive impairment showed a clear decrease of global and focal WM, including brainstem, corticospinal tracts, corpus callosum but also superior and inferior longitudinal fascicles. Cognitive impairment in NMO patients is correlated to the decreased of global and focal WM volume of the brain. Further studies are needed to better understand the precise origin of cognitive impairment in NMO patients, particularly in the WM.

Cannabis-related Stroke Myth or Reality?

Cannabis, which is the most widely used recreational substance in the world, is considered by many consumers as safe with few negative side effects.1 This opinion is somehow strengthened by the fact that cannabis was also shown to have therapeutic applications.2 Cannabis is obtained from the plant Cannabis sativa and its varieties Cannabis indica and Cannabis americana .3 The 2 main preparations derived from cannabis are marijuana and hashish.2 The principal psychoactive cannabinoid in cannabis is delta 9 tetrahydrocannabinol4, and the potency of different preparations of cannabis that relates to tetrahydrocannabinol content is extremely variable.3 The plasma half-life of tetrahydrocannabinol isμ56 hours in occasional users and 28 hours in chronic users.5 Psychopharmacological acute effects associated with cannabis use are euphoria, increased self-confidence, relaxation, and a general sense of well being.3 Except for nausea associated with cancer chemotherapy, most of the potential beneficial effects are not approved by many administrations around the world. Indeed, the more common effects described as beneficial are glaucoma, analgesia, appetite in AIDS patients, tremor, Parkinson disease, spasticity in multiple sclerosis, epilepsia, anxiolytic, or antidepressive actions.1,3 However, several important negative side effects associated with cannabis are also observed. Indeed, in selected patients, acute psychiatric and behavioral abnormalities, such as anxiety, panic, and attentional abnormalities, have been reported.3,6 Risk of psychotic disorders or symptoms is higher in regular users of cannabis.6 Furthermore, psychological and physical dependence are described as chronic effects of cannabis use.6 As for other drugs, cannabis withdrawal syndrome, including anxiety, depressed mood, and sleep difficulties, may occur in heavy users on cessation.6,7 Also, somatic negative effects, such as cardiovascular complications (myocardial infarction, ventricular tachycardia, and sudden death), peripheral events (peripheral arteritis and kidney infarction), and …

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

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).