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Robust Automated Regularization Factor Selection for Statistical Reconstructions

Author: Bergner, F. · Brendel, B. · Noel, P.B. · Dobritz, M. · Koehler, T.
Type:Conference paper
Institution: Philips Research
Source:2nd International Conference on Image Formation in X-Ray Computed Tomography, Salt Lake City, USA, 24-27 June 2012
Identifier: MS 33.251
Rights: (c) 2012 The Author(s)


Statistical, iterative reconstruction techniques have become a major research topic in the CT sector. These techniques promise a better system model, which is used for the inversion of the tomographic problem, and therefore better reconstruction results. Due to the ill–posedness of these problems, regularization is required in the cost functions in order to stabilize the algorithm and to reduce the noise in the resulting images. The strength of the regularization is usually changed by using an appropriate multiplicative factor, which in most cases has to be determined empirically with major efforts. This paper describes a new automated selection of this factor by using a quality criterion and a regulator, which controls the multiplicative factor over the iterations to a desired level. The method is light–weight, robust and also applicable for other iterative methods like de–noising.

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