Learning-based occupational x-ray scatter estimation

Maul N, Roser P, Birkhold A, Kowarschik M, Zhong X, Strobel N, Maier A (2022)

Publication Language: English

Publication Type: Journal article

Publication year: 2022

Journal

Physics in Medicine and Biology IOP Publishing

Book Volume: 67

Journal Issue: 7

DOI: 10.1088/1361-6560/ac58dc

Abstract

Objective. During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the staff's awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive. Yet, in the interventional environment, immediate feedback to the personnel is desirable. Approach. In this work, we propose deep neural networks to mitigate the computational effort of MC simulations. Our learning-based models consider detailed models of the (outer) patient shape and (inner) anatomy, additional objects in the room, and the x-ray tube spectrum to cover imaging settings encountered in real interventional settings. We investigate two cases of scatter prediction. First, we employ network architectures to estimate the full three-dimensional (3D) scatter distribution. Second, we investigate the prediction of two-dimensional (2D) intensity projections that facilitate the intra-procedural visualization. Main results. Depending on the dimensionality of the estimated scatter distribution and the network architecture, the mean relative error of each network is in the range of 12% and 14% compared to MC simulations. However, 3D scatter distributions can be estimated within 60 ms and 2D distributions within 15 ms. Significance. Overall, our method is suitable to support the online assessment of scattered ionizing radiation in the interventional environment and can help to lower the occupational radiation risk.

Authors with CRIS profile

Noah Maul Lehrstuhl für Informatik 5 (Mustererkennung) Philipp Roser Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE) Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt DE Germany (DE)

How to cite

APA:

Maul, N., Roser, P., Birkhold, A., Kowarschik, M., Zhong, X., Strobel, N., & Maier, A. (2022). Learning-based occupational x-ray scatter estimation. Physics in Medicine and Biology, 67(7). https://dx.doi.org/10.1088/1361-6560/ac58dc

MLA:

Maul, Noah, et al. "Learning-based occupational x-ray scatter estimation." Physics in Medicine and Biology 67.7 (2022).

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