A thermal drift correction method for laboratory nano CT based on outlier elimination

Mengnan Liu; Xiaoqi Xi; Yu Han; Siyu Tan; Lei Li; Bin Yan

doi:10.1117/12.2626906

2 March 2022 A thermal drift correction method for laboratory nano CT based on outlier elimination

Mengnan Liu, Xiaoqi Xi, Yu Han, Siyu Tan, Lei Li, Bin Yan

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 121580G (2022) https://doi.org/10.1117/12.2626906
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

The alignment of the acquired projections is quite necessary for accurate reconstruction of nano computer tomography (nano CT) due to thermal drift. In this paper, a method based on features outlier elimination (OE) is proposed to reduce the drift artifacts from the reconstruction slices, and a series of reference sparse projections are required. The rough alignment is realized after the extraction from the Speeded Up Robust Features (SURF) of both the original projections and the reference projections, of which the structure similarity (SSIM) is utilized to eliminate the outlier features. Then, the rest features are used for the further alignment for reconstruction. The simulation results show that the proposed method is more accurate and robust than image registration method based on entropy correlation coefficient (ECC) and traditional SURF. Scanning results of bamboo stick show that the proposed method can preserve the details of slices.

Citation Download Citation

Mengnan Liu, Xiaoqi Xi, Yu Han, Siyu Tan, Lei Li, and Bin Yan "A thermal drift correction method for laboratory nano CT based on outlier elimination", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 121580G (2 March 2022); https://doi.org/10.1117/12.2626906

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