Shape recovery for sparse‐data tomography |
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Authors: | Heikki Haario Aki Kallonen Marko Laine Esa Niemi Zenith Purisha Samuli Siltanen |
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Institution: | 1. Lappeenrannan Teknillinen Yliopisto, Lappeenranta, Finland;2. Department of Physics, University of Helsink, Finland;3. Finnish Meteorological Institute, Helsinki, Finland;4. Department of Mathematics and Statistics, University of Helsinki, Finland;5. Department of Mathematics, Universitas Gadjah Mada, Yogyakarta, Indonesia |
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Abstract: | A two‐dimensional sparse‐data tomographic problem is studied. The target is assumed to be a homogeneous object bounded by a smooth curve. A nonuniform rational basis splines (NURBS) curve is used as a computational representation of the boundary. This approach conveniently provides the result in a format readily compatible with computer‐aided design software. However, the linear tomography task becomes a nonlinear inverse problem because of the NURBS‐based parameterization. Therefore, Bayesian inversion with Markov chain Monte Carlo sampling is used for calculating an estimate of the NURBS control points. The reconstruction method is tested with both simulated data and measured X‐ray projection data. The proposed method recovers the shape and the attenuation coefficient significantly better than the baseline algorithm (optimally thresholded total variation regularization), but at the cost of heavier computation. |
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Keywords: | CAD MCMC NURBS reverse engineering shape recovery X‐ray tomography |
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