Terahertz deep learning computed tomography in extreme sparse view

Jialin Li; Shuai Sun; Jianglei Di; Wei Zhang; Zeren Li

doi:10.1117/12.3040260

16 August 2024 Terahertz deep learning computed tomography in extreme sparse view

Jialin Li, Shuai Sun, Jianglei Di, Wei Zhang, Zeren Li

Proceedings Volume 13231, 4th International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2024); 132312I (2024) https://doi.org/10.1117/12.3040260
Event: 4th International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2024), 2024, Chongqing, China

Abstract

We introduce a novel deep learning-based framework to decrease data acquisition time and enhance the 3D reconstruction resolution of sparse view THz-CT. Using asymmetric convolution blocks and channel attention mechanisms, our network ASE-UNet effectively suppresses terahertz image artifact in extreme sparse views. Furthermore, a preprocessing step involving integrating bandwidth power effectively converts time-domain data into detailed spatiotemporal data. The results show that our method delivered superior reconstruction quality and is more efficient in data usage than traditional iterative algorithms

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jialin Li, Shuai Sun, Jianglei Di, Wei Zhang, and Zeren Li "Terahertz deep learning computed tomography in extreme sparse view", Proc. SPIE 13231, 4th International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2024), 132312I (16 August 2024); https://doi.org/10.1117/12.3040260

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