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Seabra J.,Institute for Systems and Robotics
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

Carotid plaques are the main cause of neurological symptoms due to distal embolization or flow reduction. An objective classification of such lesions into symptomatic or asymptomatic is crucial for optimal treatment planning. Source

Tahri O.,Institute for Systems and Robotics | Mezouar Y.,University Blaise Pascal | Chaumette F.,French Institute for Research in Computer Science and Automation | Corke P.,Queensland University of Technology
IEEE Transactions on Robotics

This paper proposes a generic decoupled image-based control scheme for cameras obeying the unified projection model. The scheme is based on the spherical projection model. Invariants to rotational motion are computed from this projection and used to control the translational degrees of freedom (DOFs). Importantly, we form invariants that decrease the sensitivity of the interaction matrix to object-depth variation. Finally, the proposed results are validated with experiments using a classical perspective camera as well as a fisheye camera mounted on a 6-DOF robotic platform. © 2010 IEEE. Source

Rodrigues I.C.,Institute for Systems and Robotics | Rodrigues I.C.,Polytechnic Institute of Lisbon | Sanches J.M.R.,Institute for Systems and Robotics | Sanches J.M.R.,University of Lisbon
IEEE Transactions on Image Processing

Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented. © 2010 IEEE. Source

Sousa I.,Institute for Systems and Robotics | Sousa I.,University of Lisbon | Sousa I.,Siemens AG | Vilela P.,Hospital da Luz | And 2 more authors.

It has recently been proposed that hypocapnic cerebrovascular reactivity (CVR) can be assessed by measuring the blood oxygenation level dependent (BOLD) response to paced deep breathing (PDB) tasks inducing mild hypocapnia and vasoconstriction. In this work, we aim to assess the test-retest reproducibility and inter-subject variability of BOLD CVR measurements obtained using a PDB task and different methods to analyse the associated BOLD signal. The respiratory protocol consisted of alternating 40s of PDB with normal free breathing; expired CO2 pressure levels (PETCO2) were continuously monitored. CVR was quantified using either a timecourse curve analysis (TCA) approach, where the magnitude of response peaks is emphasized, or general linear modelling (GLM) including optimisation of the BOLD response latencies. The GLM fit was carried out using two types of response regressors: one that was computed as the convolution of PETCO2 traces with a gamma function and another that consisted of the convolution of PDB paradigm blocks with a physiological model of the respiratory response. Haemodynamic response latencies were optimised either on a voxel basis or for the whole imaging region. We found that the GLM method based on PDB task or PETCO2 traces and voxelwise optimisation of response latencies provided the most reproducible measures of CVR. For the average grey matter CVR, the inter-subject coefficient of variation (CVinter) / intra-subject coefficient of variation (CVintra) / intra-class correlation coefficient (ICC) were 20%/8%/0.8 and 27%/8%/0.9, using the task and PETCO2 timecourses, respectively. In terms of the spatial reproducibility, the group mean (±standard deviation) of the spatial ICC (ICCspatial) was 1.04±0.23 and 1.02±0.26, for the task and PETCO2 timecourses, respectively. These results indicate generally good reproducibility of the hypocapnic CVR maps obtained using the proposed PDB task and analysis methodology. This suggests that such protocol may therefore offer a promising alternative to conventional vasoactive challenges, which avoids their discomfort and difficulty. © 2014 Elsevier Inc. Source

Leite M.,University of Lisbon | Leite M.,Institute for Systems and Robotics | Leal A.,Centro Hospitalar Psiquiatrico Of Lisbon | Figueiredo P.,University of Lisbon | Figueiredo P.,Institute for Systems and Robotics
Frontiers in Neurology

Simultaneous electroencephalogram (EEG)-functional Magnetic Resonance Imaging (fMRI) recordings have seen growing application in the evaluation of epilepsy, namely in the characterization of brain networks related to epileptic activity. In EEG-correlated fMRI studies, epileptic events are usually described as boxcar signals based on the timing information retrieved from the EEG, and subsequently convolved with a hemodynamic response function to model the associated Blood Oxygen Level Dependent (BOLD) changes. Although more flexible approaches may allow a higher degree of complexity for the hemodynamics, the issue of how to model these dynamics based on the EEG remains an open question. In this work, a new methodology for the integration of simultaneous EEG-fMRI data in epilepsy is proposed, which incorporates a transfer function from the EEG to the BOLD signal. Independent component analysis of the EEG is performed, and a number of metrics expressing different models of the EEG-BOLD transfer function are extracted from the resulting time courses.These metrics are then used to predict the fMRI data and to identify brain areas associated with the EEG epileptic activity.The methodologywas tested on both ictal and interictal EEG-fMRI recordings from one patient with a hypothalamic hamartoma. When compared to the conventional analysis approach, plausible, consistent, and more significant activations were obtained. Importantly, frequency-weighted EEG metrics yielded superior results than those weighted solely on the EEG power, which comes in agreement with previous literature. Reproducibility, specificity, and sensitivity should be addressed in an extended group of patients in order to further validate the proposed methodology and generalize the presented proof of concept. © 2013 Leite, Leal and Figueiredo. Source

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