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.
X-ray nanotomography has become an important analysis tool in a wide range of fields. However, the imaging quality is often affected by drift from focal spot movement and mechanical instability. An improved horizontal drift correction method for X-ray nanotomography based on trajectory of sinogram centroid (TSC) is proposed. This method requires neither auxiliary marks nor additional projections. A sliding window TSC fitting method is utilized. The sum of the squared errors (SSE) is calculated between the trajectory and standard sinusoidal curve. The one corresponding to the minimum SSE is chosen to obtain the horizontal drift from the original TSC, which is then used to align the projections. The proposed method is evaluated by both simulation results of the Shepp-Logan phantom and nanotomographic results of the honeybee mouthpart. The results show that this method can quickly and effectively correct the projection horizontal drift.
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