A deep learning method for pinwheel artifact reduction in CT

Utkarsh Agrawal; Rajesh Langoju; Yasuhiro Imai; Risa Shigemasa; Bipul Das

doi:10.1117/12.2653607

3 April 2023 A deep learning method for pinwheel artifact reduction in CT

Utkarsh Agrawal, Rajesh Langoju, Yasuhiro Imai, Risa Shigemasa, Bipul Das

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124641W (2023) https://doi.org/10.1117/12.2653607
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Pinwheel artifact is caused in multidetector Computed Tomography (CT) helical scans due to under-sampling of z-planes during thin slice reconstruction. There are a few hardware-based solutions like flying focal spot (FFS), which aims at generating more samples during the acquisition, thus enabling aliasing free thin slice images. However, these methods are expensive both in manufacturing capability, as well as impact on hardware life. Deep learning (DL) based methods have shown significant improvement in pin-wheel artifact reduction. Most DL-based methods use images generated from FFS or similar hardware-based enhancement to train the deep learning network, thus restricting usability of these methods on systems without these hardware enhancements. This work proposes a novel DL method to generate pin-wheel free thin-slice images from helical scans for systems not equipped with these hardware capabilities. Artifact-free thin slice images, which are used as targets for artifact reduction network are generated through DL-based super-resolution along z-direction from thick slice images reconstructed from the same scan. The framework is trained with ~16000 coronal/sagittal slices from GE-Revolution system. Clinical image review and statistical analysis of the inferencing results have shown significant artifact reduction and improved diagnostic image quality while reducing noise. A Likert score study shows significant enhancement of proposed method over other image processing-based solutions available.

Citation Download Citation

Utkarsh Agrawal, Rajesh Langoju, Yasuhiro Imai, Risa Shigemasa, and Bipul Das "A deep learning method for pinwheel artifact reduction in CT", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641W (3 April 2023); https://doi.org/10.1117/12.2653607

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