Thermal drift correction method for laboratory nanocomputed tomography based on global mixed evaluation

Nanocomputed tomography (nanoCT) is an effective tool for the nondestructive observation of 3D structures of nanomaterials; however, it requires additional correction phantom to reduce artifacts induced by the focal drift of the X-ray source and mechanical thermal expansion. Drift correction without a correction phantom typically uses rapidly acquired sparse projections to align the original projections. The noise and brightness difference in the projections limit the accuracy of existing feature-based methods such as locality preserving matching (LPM) and random sample consensus (RANSAC). Herein, a rough-to-refined correction framework based on global mixed evaluation (GME) is proposed for precise drift estimation. First, a new evaluation criterion for projection alignment, named GME, which comprises the structural similarity (SSIM) index and average phase difference (APD), is designed. Subsequently, an accurate projection alignment is achieved to estimate the drift by optimizing the GME within the proposed correction framework based on the rough-to-refined outlier elimination strategy. The simulated 2D projection alignment experiments show that the accuracy of the GME is improved by 14x and 12x than that of the mainstream feature-based methods LPM and RANSAC, respectively. The proposed method is validated through actual 3D imaging experiments. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Nanocomputed tomography is an effective tool for observing 3D structures of nanomaterials, but it requires correction phantom to reduce artifacts. This study proposes a rough-to-refined correction framework based on global mixed evaluation for precise drift estimation.