Biomedical subjects
Publications and source records attributed to F Behloul.
In a hereditary variant of cerebral amyloid angiopathy (CAA), cerebral hemorrhage with amyloidosis-Dutch type, supratentorial microbleeds were found to occur independently of the presence of hypertension, whereas hypertension probably contributed to the development of cerebellar microbleeds. This predictable hereditary variant of CAA may be a useful model to study microbleeds in relation to CAA.
In this paper a temporal covariance method designed to analyze a Magnetic resonance (MR) image sequence of myocardial perfusion is presented. This method is used to map the first-pass transit of a contrast agent (Gd-chelates) through the heart. A map of bolus transit delay is constructed pixel by pixel corresponding to a myocardial reference using a temporal covariance measure. The resulting covariance map is a parametric image representing regions with different temporal dynamics. The proposed method is evaluated in 14 patients with coronary artery disease and eight healthy volunteers. Under rest and stress, covariance method is able to reveal a perfusion defect in stenosed coronary-artery-related myocardium. Furthermore, the method presents the advantage of its easy implementation and real-time parametric map construction.
This paper describes a multimodality framework for computer-aided myocardial viability assessment based on neuro-fuzzy techniques. The proposed approach distinguishes two main levels: the modality-independent inference level and the modality-dependent application level. This two-level distinction releases the hard constraint of multimodality image registration. An abstract description template is used to describe the different myocardial functions (contractile function, perfusion, metabolism). Parameters extracted from different image modalities are combined to derive a diagnostic image. The neuro-fuzzy techniques make our system transparent, adaptive and easily extendable. Its effectiveness and robustness are demonstrated in a positron emission tomography/magnetic resonance imaging data fusion application.
This paper presents an efficient and accurate approach to myocardium extraction in Positron Emission Tomography (PET) images based on a careful application of soft computing techniques. PET images present a noisy background, making the automatic myocardium extraction and uptake quantification a difficult task. In this work a Self Organized Radial Basis Function Network (SRBFN) is designed to focus on the myocardium in an iterative process until the total extraction of the myocardium from the noisy background is achieved. Fuzzy sets and fuzziness measures are used to compute the error of the network. The method was tested on a set of nine images of different patients and its effectiveness is illustrated in two patients showing tracer uptake defects.
A new method using the covariance function as a measure of functional similarity is presented for dynamic analysis of a sequence of scintigraphic cardiac images taken throughout the cardiac cycle. The similarity between the temporal response of pixels in a reference region of the scintigraphic image series and the temporal response of the remaining pixels in the image sequence is calculated. The resulting covariance image is a functional image representing regions with different temporal dynamics. A box-plot representation of this image permits better interpretation for clinical decision making. This analysis allows visualization of the ventricular emptying pattern, which may be useful in studying motion or conduction abnormalities. Compared to Fourier analysis, our method does not make assumption that the data are periodic and that the transition between the first and the last frame of the study is smooth. The proposed method has been performed in one normal patient and twenty patients with abnormal ventricular contraction patterns, and there is no computational difficulty in its implementation. A comparison with the Fourier analysis is performed.