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The authors report on a patient with Parkinson disease (PD) and severe obsessive-compulsive disorder (OCD), in whom bilateral stimulation of the subthalamic nucleus (STN) was used to treat both PD and OCD symptoms. This 49-year-old man had displayed symptoms of PD for 13 years. Progressively, his motor disability became severe despite optimal medical treatment. In parallel, he suffered severe OCD for 16 years, with obsessions of accumulation and compulsions of gathering and rubbing that lasted more than 8 hours per day. Bilateral high-frequency STN stimulation was performed to treat motor disability. After surgery (at 1-year follow up), motor and OCD symptoms were dramatically improved. The pre- and postoperative Yale-Brown Obsessive-Compulsive Scale scores were 32 and 1, respectively. No additional antiparkinsonian drugs were administered. This case and other recent reports indicate that OCD symptoms can be improved by deep brain stimulation, a finding that opens new perspectives in the surgical treatment of severe and medically intractable OCD.

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View synthesis brings geometric distortions which are not handled efficiently by existing image quality assessment metrics. Despite the widespread of 3-D technology and notably 3D television (3DTV) and free-viewpoints television (FTV), the field of view synthesis quality assessment has not yet been widely investigated and new quality metrics are required. In this study, we propose a new full-reference objective quality assessment metric: the View Synthesis Quality Assessment (VSQA) metric. Our method is dedicated to artifacts detection in synthesized viewpoints and aims to handle areas where disparity estimation may fail: thin objects, object borders, transparency, variations of illumination or color differences between left and right views, periodic objects… The key feature of the proposed method is the use of three visibility maps which characterize complexity in terms of textures, diversity of gradient orientations and presence of high contrast. Moreover, the VSQA metric can be defined as an extension of any existing 2D image quality assessment metric. Experimental tests have shown the effectiveness of the proposed method.

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