Focal spot deconvolution using convolutional neural networks

Jan Kuntz; Joscha Maier; Marc Kachelrieß; Stefan Sawall

doi:10.1117/12.2513400

1 March 2019 Focal spot deconvolution using convolutional neural networks

Jan Kuntz, Joscha Maier, Marc Kachelrieß, Stefan Sawall

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109480Q (2019) https://doi.org/10.1117/12.2513400
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

The focal spot size of x-ray tubes as well as the pixel size and scintillator thickness limit the spatial resolution of projection images as they result in blurring and degradation of the system’s point spread function. Deblurring of those images has been a topic of research for several decades. However, it is not solved in general. In this manuscript the application of a convolutional neural network for the deblurring of x-ray projection images is presented and compared to a standard deblurrig technique. The advantages of the neural network in terms of image quality and applicability are demonstrated with simulations and measurements originating from table top and gantry based micro-CT systems.

Citation Download Citation

Jan Kuntz, Joscha Maier, Marc Kachelrieß, and Stefan Sawall "Focal spot deconvolution using convolutional neural networks", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109480Q (1 March 2019); https://doi.org/10.1117/12.2513400

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