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Regridding and data interpolation of projection domain and Radon domain for super-resolution tomograpic reconstruction

机译:超分辨率划线重建投影域和氡域的遗重和数据插值

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Radon domain can be filled by the Fourier transforms for projection images in a polar gridding format (radial lines for parallel projections, radon arcs for fan-beam projections). The Radon-based tomographic reconstruction requires regridding a polar radon domain into a rectilinear lattice before inverse Fourier transform. Since the radon domain is irregularly sampled by Fourier-transformed projections, i.e, oversampled around the central regions and undersampled at the peripheral regions, the polar-to-Cartesian coordinate grid conversion involves rebinning for oversampled central region, interpolation for undersampled peripheral region, and extrapolation for extending the peripheral boundary. In this paper, we propose a general data rebinning/interpolation/extrapolation scheme to deal with the radon domain regridding, which is a local convex combination with weights determined by a function of inverse distances. For filling the unavailable entries at peripheral regions, we propose to calculate the corresponding entries in the projection domain, rather than in the radon domain, by interpolations and extrapolations. The interpolation for peripheral region allows us investigate the angular sampling for computed tomography scanning. The extrapolation leads to super-resolution tomographic reconstruction. We find that data interpolation in projection domain may produce better results than in radon domain. This finding may be justified by the fact that the data distribution is more continuous in projection domain than in Fourier domain.
机译:氡域可以由傅里叶变换填充用于极性网格格式的投影图像(用于并联投影的径向线,用于风扇束投影的氡电弧)。基于氡的断层摄影重建需要将极氡域抛出到逆傅里叶变换之前的直线晶格中。由于氡域不规则地通过傅里叶变换的突起来采样,即,在中心区域周围过采样并在外围区域上施加遮盖,偏光到笛卡尔坐标网格转换涉及用于过采样的中央区域的标准,用于遮蔽外围区域的插值,以及延伸外围边界的外推。在本文中,我们提出了一般数据重构/插值/外推方案,以处理氡域轰隆性,这是一种局部凸组合,其具有由逆距离的函数决定的重量。为了在外围区域填充不可用的条目,我们建议通过插值和外推投影域中的相应条目,而不是在氡域中。外围区域的插值允许我们调查计算断层扫描的角度采样。外推导致超级分辨率断层切断重建。我们发现投影域中的数据插值可能会产生比在Radon域中更好的结果。这种发现可以通过数据分布在投影域中比傅里叶域中更加连续。

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