A depolarization and attenuation experiment using the COMSTAR and CTS satellites

Monthly statistical data are presented on ground rainfall rate and attenuation of satellite downlinks at 11.7 GHz, 19.04 GHz, and 28.56 GHz and on cross-polarization isolation at 11.7 GHz. Regression equations for relating isolation to attenuation, attenuation to rain rate, and attenuation at one frequency to attenuation at another frequency are also included. Longer-term statistics are also presented and discussed

Validation of Tissue Modelization and Classification Techniques in T1-Weighted MR Brain Images

We propose a deep study on tissue modelization andclassification Techniques on T1-weighted MR images. Threeapproaches have been taken into account to perform thisvalidation study. Two of them are based on FiniteGaussian Mixture (FGM) model. The first one consists onlyin pure gaussian distributions (FGM-EM). The second oneuses a different model for partial volume (PV) (FGM-GA).The third one is based on a Hidden Markov Random Field(HMRF) model. All methods have been tested on a DigitalBrain Phantom image considered as the ground truth. Noiseand intensity non-uniformities have been added tosimulate real image conditions. Also the effect of ananisotropic filter is considered. Results demonstratethat methods relying in both intensity and spatialinformation are in general more robust to noise andinhomogeneities. However, in some cases there is nosignificant differences between all presented methods

A Statistical Framework for Partial Volume Segmentation

The literature about partial volume (PV) segmentation of MR images is rather limited, and a general methodology for robustly classifying images with severe partial voluming that works well in all cases, remains an open issue. In this paper, we present a statistical framework for PV segmentation that contains and extends existing techniques. We think of a partial volumed image as a downsampled version of a fictive higher-resolution image that does not contain partial voluming, and we estimate the model parameters of this underlying image using an Expectation-Maximization algorithm. This leads to an iterative approach that interleaves a statistical classification of the image voxels using spatial information and an according update of the model parameters. We demonstrate on simulated data that the use of appropriate spatial prior knowledge, in casu a Markov random field model, not only improves the classifications, but is often indispensable for robust parameter estimation as well. We also present results on 2-D slices of real high-resolution MR images of the brain, and conclude that general robust segmentation of lower-resolution images requires development of spatial models that accurately describe the shape of the brain

Co-registration of cortical magnetic stimulation and functional magnetic resonance imaging

FUNCTIONAL magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) are noninvasive techniques recently used to investigate cortical motor physiology. However, these modalities measure different phenomena, and in studies of human motor control they have given inconsistent results. We have developed a reproducible technique which co-registers TMS and fMRI, using a frameless method. In four normal subjects, the TMS map and fMRI activation were present on the primary motor cortex contralateral to the target hand, with some extension into primary sensory cortex. fMRI activation alone was also present in the medial motor cortex bilaterally and in the sensori-motor cortex ipsilateral to the target hand. This technique allows a more comprehensive evaluation of the physiologic events involved in motor control

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