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Abstract
4D digital tomosynthesis (DTS) techniques for image-guided radiation therapy (IGRT) are able to reduce radiation dose, scan and reconstruction time compared to 4D cone-beam computed tomography (CBCT). In spite of these benefits, the 4D DTS techniques cause the degradation of image quality due to an intrinsic imaging strategy and consequently reduce treatment accuracy. In this study, a deep learning-based convolutional neural network (CNN) framework was proposed for 4D DTS imaging. The proposed CNN framework consisted of the data restoration network based on a U-Net and the denoising network combined with a 2D wavelet transform, and the network training was implemented with clinical images. The quality of the 4D DTS images obtained from the proposed model was evaluated in terms of quantitative accuracy, spatial resolution and noise property. The results showed that the proposed CNN framework improved the quantitative accuracy of 4D DTS images by 3–19%, and the spatial resolution and noise for the proposed CNN framework were reduced by 2.24–7.33% and 8.92–40.07%, respectively, in comparison to other imaging models. These results represented that the degradation of the 4D DTS image quality can be recovered using the proposed CNN framework, and the proposed model is suitable for maintaining spatial resolution as well as suppressing noise and artifacts. In conclusion, the proposed CNN framework can be potentially used to improve the quality of 4D DTS images for the IGRT.

(a) U-Net 기반의 영상 복원 네트워크, (b) 잡음 저감화 네트워크

(a) 3차원 디지털단층영상, (b) 호흡동조 4차원 디지털단층영상, (c) U-Net 기반의 영상 복원 네트워크를 적용한 호흡동조 4차원 디지털단층영상, (d) 잡음 저감화 네트워크를 적용한 호흡동조 4차원 디지털단층영상, (e) U-Net 기반의 영상 복원 네트워크 및 잡음 저감화 네트워크를 동시 적용한 호흡동조 4차원 디지털단층영상

Affiliations

Seungwan Lee
Department of Radiological Science, College of Medical Science, Konyang University, 158 Gwanjeodong-ro, Daejeon, 35365, South Korea