Tenfold your photons: A physically-sound approach to filtering-based variance reduction of monte-carlo-simulated dose distributions

Roser P, Birkhold A, Preuhs A, Kowarschik M, Fahrig R, Maier A (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer

Pages Range: 113-118

Conference Proceedings Title: Informatik aktuell

Event location: Berlin DE

ISBN: 9783658292669

DOI: 10.1007/978-3-658-29267-6_24

Abstract

X-ray dose constantly gains interest in the interventional suite. With dose being generally diffcult to monitor reliably, fast computational methods are desirable. A major drawback of the gold standard based on Monte Carlo (MC) methods is its computational complexity. Besides common variance reduction techniques, filter approaches are often applied to achieve conclusive results within a fraction of time. Inspired by these methods, we propose a novel approach. We down-sample the target volume based on the fraction of mass, simulate the imaging situation, and then revert the down-sampling. To this end, the dose is weighted by the mass energy absorption, up-sampled, and distributed using a guided filter. Eventually, the weighting is inverted resulting in accurate high resolution dose distributions. The approach has the potential to considerably speed-up MC simulations since less photons and boundary checks are necessary. First experiments substantiate these assumptions. We achieve a median accuracy of 96.7% to 97.4% of the dose estimation with the proposed method and a down-sampling factor of 8 and 4, respectively. While maintaining a high accuracy, the proposed method provides for a tenfold speed-up. The overall findings suggest the conclusion that the proposed method has the potential to allow for further effciency.

Authors with CRIS profile

Philipp Roser Lehrstuhl für Informatik 5 (Mustererkennung) Alexander Preuhs Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens AG DE Germany (DE)

How to cite

APA:

Roser, P., Birkhold, A., Preuhs, A., Kowarschik, M., Fahrig, R., & Maier, A. (2020). Tenfold your photons: A physically-sound approach to filtering-based variance reduction of monte-carlo-simulated dose distributions. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 113-118). Berlin, DE: Springer.

MLA:

Roser, Philipp, et al. "Tenfold your photons: A physically-sound approach to filtering-based variance reduction of monte-carlo-simulated dose distributions." Proceedings of the International workshop on Algorithmen - Systeme - Anwendungen, 2020, Berlin Ed. Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm, Springer, 2020. 113-118.

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