DeepRAR: a CNN-based approach for CT and CBCT ring artifact reduction

Philip Trapp; Carlo Amato; Stefan Sawall; Marc Kachelrieß; Tim Vöth

doi:10.1117/12.2612667

4 April 2022 DeepRAR: a CNN-based approach for CT and CBCT ring artifact reduction

Philip Trapp, Carlo Amato, Stefan Sawall, Marc Kachelrieß, Tim Vöth

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 120311D (2022) https://doi.org/10.1117/12.2612667
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Ring artifacts are a well-known problem in computed tomography (CT) and in particular in cone-beam CT (CBCT). This work addresses the reduction of ring artifacts in CT acquisitions using a data-driven approach. Deep convolutional neural networks (CNNs) of different dimensionalities are trained to estimate the ring artifacts directly from an uncorrected volume. This approach has the advantage that neither raw-data has to be available, nor any kind of resampling of the data is necessary. In addition to ring artifacts, our networks are also trained to correct for partial ring artifacts as they may occur in spiral CT or CBCT. This study shows that ring artifacts can be reduced in image domain by these neural networks. Our results suggest that a three-dimensional network is most suitable for this task.

Conference Presentation

Citation Download Citation

Philip Trapp, Carlo Amato, Stefan Sawall, Marc Kachelrieß, and Tim Vöth "DeepRAR: a CNN-based approach for CT and CBCT ring artifact reduction", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120311D (4 April 2022); https://doi.org/10.1117/12.2612667

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