Reduction of truncation artifact in stationary inverse-geometry digital tomosynthesis using convolutional neural network

Burnyoung Kim; Dobin Yim; Seungwan Lee

doi:10.1117/12.2547703

16 March 2020 Reduction of truncation artifact in stationary inverse-geometry digital tomosynthesis using convolutional neural network

Burnyoung Kim, Dobin Yim, Seungwan Lee

Author Affiliations +

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 113124I (2020) https://doi.org/10.1117/12.2547703
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Stationary inverse-geometry digital tomosynthesis (s-IGDT) has advantages in terms of motion artifact reduction and diagnostic efficiency improvement. However, truncation artifacts are caused in reconstructed images owing to the geometric characteristics of s-IGDT systems, and this drawback degrades the diagnostic accuracy. In order to overcome this limitation, we proposed a convolutional neural network (CNN)-based truncation artifact reduction method. We simulated a s-IGDT system with stationary X-ray source array and small detector. Also, we acquired s-IGDT images using 70 volumetric phantoms based on the SPIE-AAPM lung CT challenge dataset. The U-Net was used as the CNN architecture, and we trained the network through 207 s-IGDT images. We confirmed that the truncation artifacts with various patterns included in the prior images were clearly removed in the prediction images obtained by the trained network. Moreover, the quantitative evaluation showed that both of the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) improved when using the proposed method. The averaged SSIM and PSNR of the prediction images were approximately 6% and 25% higher than those of the prior images, respectively. In conclusion, the proposed model based on the CNN has superior performance in removing the truncation artifacts of s-IGDT images.

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Burnyoung Kim, Dobin Yim, and Seungwan Lee "Reduction of truncation artifact in stationary inverse-geometry digital tomosynthesis using convolutional neural network", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124I (16 March 2020); https://doi.org/10.1117/12.2547703

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KEYWORDS

Sensors

Convolutional neural networks

X-ray sources

Computer programming

Convolution

Detector arrays

Diagnostics

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