Stéphanie Bricq

Assessing spatial resolution versus sensitivity from laser speckle contrast imaging: application to frequency analysis

For blood perfusion monitoring, laser speckle contrast (LSC) imaging is a recent non-contact technique that has the characteristic of delivering noise-like speckled images. To exploit LSC images for quantitative physiological measurements, we developed an approach that implements controlled spatial averaging to reduce the detrimental impact of the noise and improve measurement sensitivity. By this approach, spatial resolution and measurement sensitivity can be traded-off in a flexible way depending on the quantitative prospect of the study. As an application, detectability of the cardiac activity from LSC images of forearm using power spectrum analysis is studied through the construction of spatial activity maps offering a window on the blood flow perfusion and its regional distribution. Comparisons with results obtained with signals of laser Doppler flowmetry probes are performed.

What are normal relaxation times of tissues at 3 T

The T1 and T2 relaxation times are the basic parameters behind magnetic resonance imaging. The accurate knowledge of the T1 and T2 values of tissues allows to perform quantitative imaging and to develop and optimize magnetic resonance sequences. A vast extent of methods and sequences has been developed to calculate the T1 and T2 relaxation times of different tissues in diverse centers. Surprisingly, a wide range of values has been reported for similar tissues (e.g. T1 of white matter from 699 to 1735ms and T2 of fat from 41 to 371ms), and the true values that represent each specific tissue are still unclear, which have deterred their common use in clinical diagnostic imaging. This article presents a comprehensive review of the reported relaxation times in the literature in vivo at 3T for a large span of tissues. It gives a detailed analysis of the different methods and sequences used to calculate the relaxation times, and it explains the reasons of the spread of reported relaxation times values in the literature.

3D Brain MRI segmentation based on robust Hidden Markov Chain

In this paper, we present a robust method to estimate parameters of hidden Markov chains (HMC) in order to segment brain MR images. Indeed, parameter estimation can be very sensitive to the presence of outliers in the data. We propose to use the trimmed likelihood estimator (TLE) to extract such outliers and to accurately estimate the parameters of different tissue classes in a robust way. Moreover neighborhood information is included in the model by using hidden Markov chains. Experimental results on 2D synthetic data and on 3D brain MRI are included to validate this approach.

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.

Transgenic mice with mutations in Nkx2.5 gene: animal model proposal to study non compaction

Methods We analyzed 17 mice divided in 5 groups : 4 wild mice, 4 heterozygous for NKx2.5 allele, 2 homozygous at D1011 of embryonic age (trabeculation stage in mice), 4 homozygous at D13-14 of embryonic age (compaction stage in mice), 3 homozygous after birth. MRI scans were performed 60 days after birth with a preclinical 11.75 T MR system. High resolution cine imaging in small axis view, at the mid base-apex axis was performed. Segmentation of compacted (C) and non-compacted (NC) mass was performed with a semiautomatic software. Papillary muscles were segmented using semi-automatic thresholding and included in the compacted mass. Blood was removed from trabeculae using the same threshold tool. 4 Mice were sacrified and the whole heart was removed and sectioned in the transversal axis. Then immunofluorescence staining was performed on sections at the mid ventricular level corresponding to the image acquired in the short axis by MRI imaging to better delineate C and NC mass. Histological images were manually analysed using ImageJ to validate our method. All values are presented as median. Interexamination reproducibility was assessed using Bland-Altman analysis (BA) and by computing the correlation coefficient. Differences between groups were assessed using a Kruskall Wallis test or Mann Whitney U test when appropriate. Results were considered significant with a p < 0.05.

Evaluation of trabeculated mass in patient with non compaction: do we need criteria reappraisal?

Background There is no gold standard for the diagnosis of patients with Left ventricular non compaction (LVNC). There are 2D criteria in sonography with Jenni’s criteria (Jenni et al, Heart 2001) and MRI with Petersen’s criteria (Petersen et al, Journal of the American College of Cardiology 2005). Jacquier (Jacquier et al, European Heart Journal 2010) has proposed a 3 D evaluation of the non compacted mass with a threshold of 20% for the non compacted (NC)/ compacted (C) mass ratio. They manually delineate the trabeculation and do not suppress blood. The aim of this study was to assess the effect of blood suppression with a semi-automatic software on this threshold.

Automatic classification of tissues using T1 and T2 relaxation times from prostate MRI: A step towards generation of PET/MR attenuation map.

This paper presents a new methodology providing the first step towards generating attenuation maps for PET/MR systems based solely on MR information. The new method segments and classifies the attenuation-differing regions of the patients pelvis based on acquired T1- and T2-weighted MR data sets and anatomical-based knowledge by computing the tissue specific T1 and T2 relaxation times, using a robust implementation of the weighted fuzzy C-means algorithm and applying a novel process to detect bones. We have demonstrated the feasibility of this approach by correctly segmenting and classifying six differing regions of structural and anatomical importance: fat, muscle, prostate, air, background and bones.

Segmentation of compacted and non compacted left ventricular mass with a semi-automatic method

Background The diagnostic of left ventricular (LV) non compaction is still a challenge in clinical practice. There is a lack of reference method able to automatically quantify the total amount of LV trabeculations. Many algorithms have been proposed to determine endocardium and epicardium borders ranging from semi-automated to fully automated methods. However most of the methods were not designed to take into account papillary muscles and trabeculae. Here we propose a global framework to detect non-compacted, endocardium and epicardium contours with minimal user interaction, taking into account papillary muscles and trabeculae. Preliminary results on the normal non-compacted mass obtained from 20 healthy volunteers are presented.

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).