Deep-learning-based image registration for nano-resolution tomographic reconstruction.
J Synchrotron Radiat
; 28(Pt 6): 1909-1915, 2021 Nov 01.
Article
en En
| MEDLINE
| ID: mdl-34738945
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.
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01-internacional
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MEDLINE
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En
Revista:
J Synchrotron Radiat
Asunto de la revista:
RADIOLOGIA
Año:
2021
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Article
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EEUU
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ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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USA