Tenfold your Photons
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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.
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- 1.Zhong X, Strobel N, Birkhold A, et al. A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model. Int J Comput Assist Radiol Surg. 2019;14(1):53–61.Google Scholar
- 2.Badal A, Badano A. Accelerating monte carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. Med Phys. 2009;36(11):4878–4880.Google Scholar
- 3.Bert J, Perez-Ponce H, Bitar ZE, et al. Geant4-based monte carlo simulations on GPU for medical applications. Phys Med Biol. 2013;58(16):5593–5611.Google Scholar
- 4.Woodcock E, Murphy T, Hemmings P, et al. Techniques used in the GEM code for monte carlo neutronics calculations in reactors and other systems of complex geometry, ANL-7050. Argonne National Laboratory; 1965.Google Scholar
- 5.Roser P, Zhong X, Birkhold A, et al. Physics-driven learning of x-ray skin dose distribution in interventional procedures. Med Phys. 2019;46(10):4654–4665.Google Scholar
- 6.Miao B, Jeraj R, Bao S, et al. Adaptive anisotropic diffusion filtering of monte carlo dose distributions. Phys Med and Biol. 2003;48(17):2767–2781.Google Scholar
- 7.Kawrakow I. On the de-noising of monte carlo calculated dose distributions. Phys Med and Biol. 2002;47(17):3087–3103.Google Scholar
- 8.He K, Sun J, Tang X. Guided image filtering. IEEE Trans Pattern Anal Mach Intell. 2013;35(6):1397–1409.Google Scholar
- 9.Agostinelli S, Allison J, Amako K, et al. Geant4–a simulation toolkit. Nucl Instrum Meth A. 2003;506(3):250–303.Google Scholar
- 10.Zankl M, Petoussi-Henss N, Fill U, et al. Tomographic anthropomorphic models part IV: organ doses for adults due to idealized external photon exposures. Institute of Radiation Medicine (former Institute of Radiation Protection); 2002.Google Scholar