Approximate path seeking for statistical iterative reconstruction
Wu M, Yang Q, Maier A, Fahrig R (2015)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: SPIE
Book Volume: 9412
Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Event location: Orlando, FL, USA
ISBN: 9781628415025
DOI: 10.1117/12.2081442
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.
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APA:
Wu, M., Yang, Q., Maier, A., & Fahrig, R. (2015). Approximate path seeking for statistical iterative reconstruction. In Christoph Hoeschen, Despina Kontos, Christoph Hoeschen (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Orlando, FL, USA: SPIE.
MLA:
Wu, Meng, et al. "Approximate path seeking for statistical iterative reconstruction." Proceedings of the Medical Imaging 2015: Physics of Medical Imaging, Orlando, FL, USA Ed. Christoph Hoeschen, Despina Kontos, Christoph Hoeschen, SPIE, 2015.
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