Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning

Roser P, Zhong X, Birkhold A, Preuhs A, Syben-Leisner C, Hoppe E, Strobel N, Kowarschik M, Fahrig R, Maier A (2020)

Publication Type: Book chapter / Article in edited volumes

Publication year: 2020

Journal

Edited Volumes: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Pages Range: 199-208

ISBN: 9783030597122

DOI: 10.1007/978-3-030-59713-9_20

Abstract

Scattered radiation is a major concern impacting X-ray image-guided procedures in two ways. First, back-scatter significantly contributes to patient (skin) dose during complicated interventions. Second, forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. While conventionally employed anti-scatter grids improve image quality by blocking X-rays, the additional attenuation due to the anti-scatter grid at the detector needs to be compensated for by a higher patient entrance dose. This also increases the room dose affecting the staff caring for the patient. For skin dose quantification, back-scatter is usually accounted for by applying pre-determined scalar back-scatter factors or linear point spread functions to a primary kerma forward projection onto a patient surface point. However, as patients come in different shapes, the generalization of conventional methods is limited. Here, we propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector as well as the back-scatter affecting the patient skin dose. Knowing the forward-scatter, we can correct X-ray projections, while a good estimate of the back-scatter component facilitates an improved skin dose assessment. To simultaneously estimate forward-scatter as well as back-scatter, we propose a multi-task approach for joint back- and forward-scatter estimation by combining X-ray physics with neural networks. We show that, in theory, highly accurate scatter estimation in both cases is possible. In addition, we identify research directions for our multi-task framework and learning-based scatter estimation in general.

Authors with CRIS profile

Involved external institutions

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

How to cite

APA:

Roser, P., Zhong, X., Birkhold, A., Preuhs, A., Syben-Leisner, C., Hoppe, E.,... Maier, A. (2020). Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning. In Anne L. Marte, lPurang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. 199-208).

MLA:

Roser, Philipp, et al. "Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning." Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Ed. Anne L. Marte, lPurang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, 2020. 199-208.

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