Single-view cone beam CT reconstruction with Swin transformer  based deep learning

Shien Huang; Yonghong Song; Junyan Rong; Tianshuai Liu; Dong Huang; Hongbing Lu

doi:10.1117/12.2654310

3 April 2023 Single-view cone beam CT reconstruction with Swin transformer based deep learning

Shien Huang, Yonghong Song, Junyan Rong, Tianshuai Liu, Dong Huang, Hongbing Lu

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124643Z (2023) https://doi.org/10.1117/12.2654310
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Cone beam CT (CBCT) imaging with sparse-view can effectively reduce the radiation dose risk. The convolution-based end-to-end deep learning methods have been used in single-view CBCT image reconstruction, which can minimize radiation dose and achieve fast CBCT imaging. However, these methods ignore the mismatch between the local feature extraction ability of convolutional neural network (CNN) and the global features of the projection image. To address this issue, we propose a novel deep learning network architecture based on Swin Transformer for single-view CBCT reconstruction. First, we use the Swin Transformer network block to construct a single-view projection feature extraction module, then through the feature transformation module, we convert the 2D features learned from projection into 3D feature tensors, and finally get the 3D volume image in the generative network. This paper is the first attempt to use the Swin Transformer model for single-view CBCT reconstruction. Experimental results with the mouse datasets demonstrate that the proposed model outperforms the convolution-based end-to-end deep learning model in reducing artifacts and preserving the image accuracy.

Citation Download Citation

Shien Huang, Yonghong Song, Junyan Rong, Tianshuai Liu, Dong Huang, and Hongbing Lu "Single-view cone beam CT reconstruction with Swin transformer based deep learning", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124643Z (3 April 2023); https://doi.org/10.1117/12.2654310

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