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X-Ray Scatter Estimation Using Deep Splines.

Authors :
Roser, Philipp
Birkhold, Annette
Preuhs, Alexander
Syben, Christopher
Felsner, Lina
Hoppe, Elisabeth
Strobel, Norbert
Kowarschik, Markus
Fahrig, Rebecca
Maier, Andreas
Source :
IEEE Transactions on Medical Imaging. Sep2021, Vol. 40 Issue 9, p2272-2283. 12p.
Publication Year :
2021

Abstract

X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics’ constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal’s frequency characteristics. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02780062
Volume :
40
Issue :
9
Database :
Academic Search Index
Journal :
IEEE Transactions on Medical Imaging
Publication Type :
Academic Journal
Accession number :
153301138
Full Text :
https://doi.org/10.1109/TMI.2021.3074712