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KMID : 1137820170380050219
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의공학회지
2017 Volume.38 No. 5 p.219 ~ p.226
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Edge-Preserving Iterative Reconstruction in Transmission Tomography Using Space-Variant Smoothing
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Jeong J.-E.
Xue Ren
Lee Soo-Jin
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Abstract
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Penalized-likelihood (PL) reconstruction methods for transmission tomography are known to provide improved image quality for reduced dose level by efficiently smoothing out noise while preserving edges. Unfortunately, however, most of the edge-preserving penalty functions used in conventional PL methods contain at least one free parameter which controls the shape of a non-quadratic penalty function to adjust the sensitivity of edge preservation. In this work, to avoid difficulties in finding a proper value of the free parameter involved in a non-quadratic penalty function, we propose a new adaptive method of space-variant smoothing with a simple quadratic penalty function. In this method, the smoothing parameter is adaptively selected for each pixel location at each iteration by using the image roughness measured by a pixel-wise standard deviation image calculated from the previous iteration. The experimental results demonstrate that our new method not only preserves edges, but also suppresses noise well in monotonic regions without requiring additional processes to select free parameters that may otherwise be included in a non-quadratic penalty function.
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KEYWORD
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transmission tomography , iterative reconstruction , penalized likelihood , regularization , smoothing parameter , space-variant smoothing , penalty function
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