X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module

Jinchao Feng; Hu Zhang; Mengfan Geng; Hanliang Chen; Kebin Jia; Zhonghua Sun; Zhe Li; Xu Cao; Brian W. Pogue

doi:10.1117/1.JBO.28.2.026004

16 February 2023 X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module

Jinchao Feng, Hu Zhang, Mengfan Geng, Hanliang Chen, Kebin Jia, Zhonghua Sun, Zhe Li, Xu Cao, Brian W. Pogue

Author Affiliations +

Journal of Biomedical Optics, Vol. 28, Issue 2, 026004 (February 2023). https://doi.org/10.1117/1.JBO.28.2.026004

Abstract

Significance

X-ray Cherenkov–luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy.

Aim

To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov-luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model.

Approach

A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and in vivo experiments.

Results

This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error (>94.1 % ), peak signal-to-noise ratio (>41.7 % ), and Pearson correlation (>19 % ) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP.

Conclusions

This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Jinchao Feng, Hu Zhang, Mengfan Geng, Hanliang Chen, Kebin Jia, Zhonghua Sun, Zhe Li, Xu Cao, and Brian W. Pogue "X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module," Journal of Biomedical Optics 28(2), 026004 (16 February 2023). https://doi.org/10.1117/1.JBO.28.2.026004

Received: 30 September 2022; Accepted: 19 January 2023; Published: 16 February 2023

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KEYWORDS

Image restoration

Reconstruction algorithms

X-rays

Tomography

Education and training

Transformers

Image quality

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