13 research outputs found

    Neural Deformable Cone Beam CT

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    In oral and maxillofacial cone beam computed tomography (CBCT), patient motion is frequently observed and, if not accounted for, can severely affect the usability of the acquired images. We propose a highly flexible, data driven motion correction and reconstruction method which combines neural inverse rendering in a CBCT setting with a neural deformation field. We jointly optimize a lightweight coordinate based representation of the 3D volume together with a deformation network. This allows our method to generate high quality results while accurately representing occurring patient movements, such as head movements, separate jaw movements or swallowing. We evaluate our method in synthetic and clinical scenarios and are able to produce artefact-free reconstructions even in the presence of severe motion. While our approach is primarily developed for maxillofacial applications, we do not restrict the deformation field to certain kinds of motion. We demonstrate its flexibility by applying it to other scenarios, such as 4D lung scans or industrial tomography settings, achieving state-of-the art results within minutes with only minimal adjustments

    Metadevice for intensity modulation with sub-wavelength spatial resolution

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    Effectively continuous control over propagation of a beam of light requires light modulation with pixelation that is smaller than the optical wavelength. Here we propose a spatial intensity modulator with sub-wavelength resolution in one dimension. The metadevice combines recent advances in reconfigurable nanomembrane metamaterials and coherent all-optical control of metasurfaces. It uses nanomechanical actuation of metasurface absorber strips placed near a mirror in order to control their interaction with light from perfect absorption to negligible loss, promising a path towards dynamic beam diffraction, light focusing and holography without unwanted diffraction artefacts

    A B-spline approach to Hermite subdivision

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    Feature Sensitive Surface Extraction from Volume Data

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    What diffraction limit?

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    Several approaches are capable of beating the classical 'diffraction limit'. In the optical domain, not only are superlenses a promising choice: concepts such as super-oscillations could provide feasible alternatives
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