Deep-learning-based image registration for nano-resolution tomographic reconstruction

Fu, Tianyu; Zhang, Kai; Wang, Yan; Li, Jizhou; Zhang, Jin; Yao, Chunxia; He, Qili; Wang, Shanfeng; Huang, Wanxia; Yuan, Qingxi; Pianetta, Piero; Liu, Yijin

doi:10.1107/s1600577521008481

DOE OSTI · 1871523

Deep-learning-based image registration for nano-resolution tomographic reconstruction

Fu, Tianyu·Zhang, Kai·Wang, Yan·Li, Jizhou·Zhang, Jin·Yao, Chunxia·He, Qili·Wang, Shanfeng·Huang, Wanxia·Yuan, Qingxi·Pianetta, Piero·Liu, Yijin

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Abstract

Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. We report the method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed.

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deep learning 73 NUCLEAR PHYSICS AND RADIATION PHYSICS full-field transmission X-ray microscopy image registration nano-tomography residual neural network

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Fu, Tianyu, Zhang, Kai, Wang, Yan, Li, Jizhou, Zhang, Jin, Yao, Chunxia, He, Qili, Wang, Shanfeng, Huang, Wanxia, Yuan, Qingxi, Pianetta, Piero, Liu, Yijin. 2021-09-13. Deep-learning-based image registration for nano-resolution tomographic reconstruction. https://doi.org/10.1107/s1600577521008481

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