Brain MRI segmentation and lesion detection using generalized Gaussian and Rician modeling

In this paper we propose a mixed noise modeling so as to segment the brain and to detect lesion. Indeed, accurate segmentation of multimodal (T1, T2 and Flair) brain MR images is of great interest for many brain disorders but requires to efficiently manage multivariate correlated noise between available modalities. We addressed this problem in1 by proposing an entirely unsupervised segmentation scheme, taking into account multivariate Gaussian noise, imaging artifacts,intrinsic tissue variation and partial volume effects in a Bayesian framework. Nevertheless, tissue classification remains a challenging task especially when one addresses the lesion detection during segmentation process2 as we did. In order to improve brain segmentation into White and Gray Matter (resp. WM and GM) and cerebro-spinal fluid (CSF), we propose to fit a Rician (RC) density distribution for CSF whereas Generalized Gaussian (GG) models are used to fit the likelihood between model and data corresponding to WM and GM. In this way, we present in this paper promising results showing that in a multimodal segmentation-detection scheme, this model fits better with the data and increases lesion detection rate. One of the main challenges consists in being able to take into account various pdf (Gaussian and non- Gaussian) for correlated noise between modalities and to show that lesion-detection is then clearly improved, probably because non-Gaussian noise better fits to the physic of MRI image acquisition

Relationship between aortic stiffness and a local pulsatile indice from the peripheral cutaneous microvasculature

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A new method to determine arterial distensibility in small arteries

Several methods allow to measure arterial distensibilty. One of them consists in estimating the direct distensibility (D) from diameter and distending blood pressure. Herein, we propose a new method to assess the distensibility in small arteries which is based on spectral analysis of time motion mode ultrasound images of radial arteries. A Fourier transform was performed on intensity of upper and lower walls. Spectral amplitude at heart frequency from both wall spectra was estimated and summed (SumAmp). SumAmp was then compared with direct distensibility. A significant correlation was found between SumAmp and D (r = 0.7, p = 0.02)

Laser speckle contrast imaging accurately measures blood flow over moving skin surfaces

Cutaneous blood flow (CBF) can be assessed non-invasively with lasers. Unfortunately, movement artefacts in the laser skin signal (LSsk) might sometimes compromise the interpretation of the data. To date, no method is available to remove movement artefacts point-by-point. Using a laser speckle contrast imager, we simultaneously recorded LSsk and the signal backscattered from an adjacent opaque surface (LSos). The completion of a first protocol allowed a definition of a simple equation to calculate the CBF from movement artefact-affected traces of LSsk and LSos. We then recorded LSsk and LSos before, during and for 5 min after the tourniquet ischemia, both when subjects (n = 8) were immobile or submitted to external passive movements of random intensity throughout the test. The typical post-occlusive reactive hyperemia trace was not identifiable within the LSsk recordings, with LSsk being 2 to 3 times higher during movements than in the immobile situation. After the calculation of CBF, traces in the immobile versus movement conditions were comparable, with the “r” cross-correlation coefficient being 0.930+/−0.010. Our method might facilitate future investigations in microvascular physiology and pathophysiology, specifically in subjects who have frequent or continuous involuntary movements

Mesures et analyses biomécaniques des interactions macrocirculation/microcirculation sanguines

L'étude de paramètres de type distensibilité des vaisseaux constituant le système cardiovasculaire est très pertinente pour le diagnostic de certaines pathologies. Nous proposons d'estimer la corrélation entre les propriétés biomécaniques des vaisseaux sanguins de gros calibre et ceux de petit calibre. Un protocole de mesure est présenté intégrant l'acquisition de signaux d'impédance bioélectrique et de fluxmétrie laser Doppler. Une analyse de ces données est effectuée à partir de diverses techniques de traitement du signal (analyses de flux, de pressions, etc), et d'une étude statistique

Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images

The authors propose a fast and effective solution for automatic segmentation of white matter lesions by using T1 and fluid‐attenuated inversion recovery (FLAIR) image modalities with no need for manual segmentation and atlas registration. Initially, a brain tissue segmentation method is used to segment the T1 image into cerebrospinal fluid (CSF), grey matter and white matter. Based on the obtained tissue segmentation results, the region of interest (ROI) of the FLAIR image is created by subtracting the CSF from the FLAIR image. Subsequently, the authors calculate the z‐score of the intensities in the ROI and define a threshold to perform a preliminary identification of abnormalities from normal tissues. The abnormalities obtained at this stage are used as the prior knowledge for the modified level‐set technique. The proposed level set method here is applied based on local Gaussian distribution to precisely detect the boundaries of the white matter lesions in the ROI. The level set method based on local Gaussian distribution fitting energy is robust to the intensity inhomogeneity of MR data and therefore capable of precisely extracting the boundaries of white matter lesions. Experimental analysis and quantitative comparisons with the peak‐seeking and state‐of‐the‐art white matter lesion segmentation (WMLS) techniques demonstrate that the algorithm is a stable and effective approach which significantly outperforms other trusted solutions for white matter lesion segmentation

Bi-exponential magnetic resonance signal model for partial volume computation.

International audienceAccurate quantification of small structures in magnetic resonance (MR) images is often limited by partial volume (PV) effects which arise when more than one tissue type is present in a voxel. PV may be critical when dealing with changes in brain anatomy as the considered structures such as gray matter (GM) are of similar size as the MR spatial resolution. To overcome the limitations imposed by PV effects and achieve subvoxel accuracy different methods have been proposed. Here, we describe a method to compute PV by modeling the MR signal with a biexponential linear combination representing the contribution of at most two tissues in each voxel. In a first step, we estimated the parameters (T1, T2 and proton density) per tissue. Then, based on the bi-exponential formulation one can retrieve fractional contents by solving a linear system of two equations with two unknowns, namely tissue magnetizations. Preliminary tests were conducted on images acquired on a specially designed physical phantom for the study of PV effects. Further, the model was tested on BrainWeb simulated brain images to estimate GM and white matter (WM) PV effects. Root mean squared error was computed between the BrainWeb ground truth and the obtained GM and WM PV maps. The proposed method outperformed traditionally used methods by 33% and 34% in GM and WM, respectively