Approximate path seeking for statistical iterative reconstruction

Meng Wu; Qiao Yang; Andreas Maier; Rebecca Fahrig

doi:10.1117/12.2081442

18 March 2015 Approximate path seeking for statistical iterative reconstruction

Meng Wu, Qiao Yang, Andreas Maier, Rebecca Fahrig

Author Affiliations +

Proceedings Volume 9412, Medical Imaging 2015: Physics of Medical Imaging; 94121D (2015) https://doi.org/10.1117/12.2081442
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Statistical iterative reconstruction (IR) techniques have demonstrated many advantages in X-ray CT reconstruction. The statistical iterative reconstruction approach is often modeled as an optimization problem including a data fitting function and a penalty function. The tuning parameter values that regulate the strength of the penalty function are critical for achieving good reconstruction results. However, appropriate tuning parameter values that are suitable for the scan protocols and imaging tasks are often difficult to choose. In this work, we propose a path seeking algorithm that is capable of generating a series of IR images with different strengths of the penalty function. The path seeking algorithm uses the ratio of the gradients of the data fitting function and the penalty function to select pixels for small fixed size updates. We describe the path seeking algorithm for penalized weighted least squares (PWLS) with a Huber penalty function in both the directions of increasing and decreasing tuning parameter value. Simulations using the XCAT phantom show the proposed method produces path images that are very similar to the IR images that are computed via direct optimization. The root-mean- squared-error of one path image generated by the proposed method relative to full iterative reconstruction is about 6 HU for the entire image and 10 HU for a small region. Different path seeking directions, increment sizes and updating percentages of the path seeking algorithm are compared in simulations. The proposed method may reduce the dependence on selection of good tuning parameter values by instead generating multiple IR images, without significantly increasing the computational load.

Citation Download Citation

Meng Wu, Qiao Yang, Andreas Maier, and Rebecca Fahrig "Approximate path seeking for statistical iterative reconstruction", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94121D (18 March 2015); https://doi.org/10.1117/12.2081442

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