Pedro Guimarães

GABA deficit in the visual cortex of patients with neurofibromatosis type 1: genotype-phenotype correlations and functional impact.

Alterations in the balance between excitatory and inhibitory neurotransmission have been implicated in several neurodevelopmental disorders. Neurofibromatosis type 1 is one of the most common monogenic disorders causing cognitive deficits for which studies on a mouse model (Nfl(+/-)) proposed increased γ-aminobutyric acid-mediated inhibitory neurotransmission as the neural mechanism underlying these deficits. To test whether a similar mechanism translates to the human disorder, we used magnetic resonance spectroscopy to measure γ-aminobutyric acid levels in the visual cortex of children and adolescents with neurofibromatosis type 1 (n = 20) and matched control subjects (n = 26). We found that patients with neurofibromatosis type 1 have significantly lower γ-aminobutyric acid levels than control subjects, and that neurofibromatosis type 1 mutation type significantly predicted cortical γ-aminobutyric acid. Moreover, functional imaging of the visual cortex indicated that blood oxygen level-dependent signal was correlated with γ-aminobutyric acid levels both in patients and control subjects. Our results provide in vivo evidence of γ-aminobutyric acidergic dysfunction in neurofibromatosis type 1 by showing a reduction in γ-aminobutyric acid levels in human patients. This finding is relevant to understand the physiological profile of the disorder and has implications for the identification of targets for therapeutic strategies.

Ocular fundus reference images from optical coherence tomography

Two-dimensional images computed from three-dimensional optical coherence tomography (OCT) data are intrinsically aligned with it, allowing to accurately position a retinal OCT scan within the ocular fundus. In this work, we aim to compute an OCT fundus reference image with improved retinal vasculature extension and contrast over traditional approaches. Based on the shadow casted by hemoglobin on the outer layers of the retina, we compute three independent images from the OCT volumetric data (including the traditional fundus reference image). Combining these images, a fourth one is created that is able to outperform the other three, both quantitatively and qualitatively (as evaluated by retina specialists). The vascular network extension provided by this method was also compared with widely used fundus imaging modalities, showing that it is similar to that achieved with color fundus photography. In this way, the proposed method is an important starting point to the segmentation of the vascular tree and provides users with a detailed fundus reference image.

Two-dimensional segmentation of the retinal vascular network from optical coherence tomography

Abstract. The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.

3D retinal vascular network from optical coherence tomography data

The retinal vascular network is directly observable by non-invasive techniques, and changes of its status have been associated to retinal and cardiac pathologies. In order to infer on these changes, studies have been performed using 2D fundus images. However, measurements such as vessel tortuosity or bifurcation angle suffer from missing depth information. n nIn this work we aim to consider the retinal vascular network in 3D as imaged by optical coherence tomography (OCT). We take advantage of proprietary software developed by our research group able to segment the vascular network from OCT fundus reference images (personal communication). This approach allows for the comparison between vessel and non-vessel A-scans and thus to highlight differences such as the hyper-reflectivity and the shadows casted by vessels.

On the relevance of the 3D retinal vascular network from OCT data

Summary Abnormal patterns of the retinal vascular system have been associated with several heart and cerebral diseases. However, these correlations were obtained using 2D imaging of the eye fundus, disregarding information on the third component (depth) of the vascular system. In this paper, we show that the depth component is relevant and should not be disregarded in this analysis. In this way, the relevance of analysis of the retinal vascular network as a biometrical marker for the general health status of an individual can be increased.

Fully-Automatic Multimodal Co-Registration of Retinal Fundus Images

Optical coherence tomography (OCT), color fundus photography (CFP), and fluorescein angiography (FA) are imaging modalities widely used in ophthalmology, and their coregistration is of major interest. The integrated overview ofmultiple images of the human retina may open new perspectives for the understanding of changes in the human eye in health and disease, thus improving our knowledge on retinal diagnostics.

Explicit and Semi-implicit Complex-Diffusion Schemes for Optical Coherence Tomography Despeckling

In this paper we illustrate and compare the use of explicit and semi-implicit finite difference schemes for nonlinear complex diffusion with different boundary conditions in the context of medical imaging despeckling, namely for Optical Coherence Tomography images of the eye fundus. Performance metrics are shown to illustrate the feasibility and performance of these numerical schemes.

Segmentation processes and pattern recognition in retina and brain imaging

Prior studies have suggested a connection between the deprivation of visual stimuli and the degeneration of cortical visual areas. Retinal and cortical thickness allows inferring on the integrity of the retina and the cortex, respectively. We intend to develop a novel methodology to allow establishing the correlation between retinal and cortical thickness, automatically and in vivo. Cortical thickness requires proper visual stimuli to establish to which location within the ocular fundus a given visual cortex location refers to. Through retinotopic mapping the link between the visual space and the visual cortex can be established, although with low resolution. Automated software that maps the visual area and interpolates retinotopic data was developed.