XDose: toward online cross-validation of experimental and computational X-ray dose estimation
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ORIGINAL ARTICLE
XDose: toward online cross-validation of experimental and computational X-ray dose estimation Philipp Roser1,3 · Annette Birkhold2 · Alexander Preuhs1 · Philipp Ochs2 · Elizaveta Stepina2 · Norbert Strobel4 · Markus Kowarschik2 · Rebecca Fahrig2 · Andreas Maier1,3 Received: 29 May 2020 / Accepted: 19 November 2020 © The Author(s) 2020
Abstract Purpose As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently. Methods A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters. Results We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under 10% for all measurement points. Conclusions Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site. Keywords Anthropomorphic phantom · Dosimetry · MOSFET · Monte Carlo simulation
Introduction
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Philipp Roser [email protected]
1
Pattern Recognition Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
2
Innovation, Advanced Therapies, Siemens Healthcare GmbH, 91301 Forchheim, Germany
3
Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
4
Institute of Medical Engineering Schweinfurt, University of Applied Sciences Würzburg–Schweinfurt, 97421 Schweinfurt, Germany
Due to the growing number of fluoroscopically guided interventi
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