Deep‐learning‐based image registration for nano‐resolution tomographic reconstruction. (13th September 2021)

• Record Type: Journal Article
• Authors: Fu, Tianyu; Zhang, Kai; Wang, Yan; Li, Jizhou; Zhang, Jin; Yao, Chunxia; He, Qili; Wang, Shanfeng; Huang, Wanxia; Yuan, Qingxi; Pianetta, Piero; Liu, Yijin
• Is Part Of: Journal of synchrotron radiation. Volume 28:Part 6(2021)
• Page Start: 1909
• Page End: 1915
• Publication Date: 2021-09-13
• Subjects: full‐field transmission X‐ray microscopy -- nano‐tomography -- image registration -- deep learning -- residual neural network; Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S1600577521008481
• Languages: English
• ISSNs: 0909-0495

Abstract

Abstract : A deep‐learning‐based image jitter correction method for synchrotron nano‐resolution tomographic reconstruction with superior efficiency and accuracy is presented. 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. 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.