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Penumbral blur is one of the major limitations of the high spatial resolution micro-CT, due to a nonideal large focal spot. Penumbral blur hinders the ability to resolve small features that may only be a few pixels in size. Reducing the focal spot size by decreasing the x-ray tube power is a straightforward solution, but it leads to prolonged scan durations. In this paper, we propose to mitigate the penumbral blur by learning the inverse finite focal spot model. First, we derived the finite focal spot model that builds a relationship from the ideal point source projection to the finite focal spot projection. Based on the derived model, we numerically compute a paired projection dataset. Second, we utilized two neural networks-U-net, and convolution modulation-based U-net (CMU-net) -to learn the inverse finite focal spot model. The goal is to estimate the ideal point source projection from the actual finite focal spot projection. CMU-net, which introduces convolution modulation blocks into the contracting path of the U-net, is proposed to boost the robustness of the U-net. Finally, the standard filtered back-projection (FBP) is employed for reconstruction using the estimated ideal point projection. The experiments show that both U-net and CMU-net can effectively reduce the penumbral blur, whereas CMU-net demonstrates better performance on the real data. Experiments on real measured data demonstrate that CMU-net is more robust than U-net and can effectively resolve fine details. This method has great potential in improving the efficiency of micro-CT acquisition. It allows increasing the tube power since our method can computationally compensate for the blur caused by an increased focal spot size.

Interior tomography is a promising technique that can be used to image large objects with high acquisition efficiency. However, it suffers from truncation artifacts and attenuation value bias due to the contribution from the parts of the object outside the ROI, which compromises its ability of quantitative evaluation in material or biological studies. In this paper, we present a hybrid source translation scanning mode for interior tomography, called hySTCT-where the projections inside the ROI and outside the ROI are finely sampled and coarsely sampled respectively to mitigate truncation artifacts and value bias within the ROI. Inspired by our previous work-virtual projection-based filtered backprojection (V-FBP) algorithm, we develop two reconstruction methods-interpolation V-FBP (iV-FBP) and two-step V-FBP (tV-FBP)-based on the linearity property of the inverse Radon transform for hySTCT reconstruction. The experiments demonstrate that the proposed strategy can effectively suppress truncated artifacts and improve the reconstruction accuracy within the ROI.

Dynamic computed tomography (CT) is usually employed to image motion objects, such as beating heart, coronary artery and cerebral perfusion, etc. Recently, to further improve the temporal resolution for aperiodic industrial process imaging, the swinging multi-source CT (SMCT) systems and the corresponding swinging multi-source prior image constrained compressed sensing (SM-PICCS) method were developed. Since the SM-PICCS uses the L 1 -norm of image gradient, the edge structures in the reconstructed images are blurred and motion artifacts are still present. Inspired by the advantages in terms of image edge preservation and fine structure recovering, the L 0 -norm of image gradient is incorporated into the prior image constrained compressed sensing, leading to an L 0 -PICCS Algorithm 1Table 1The parameters of L0-PICCS (δ 1 ,δ 2 ,λ1*,λ2*) for numerical simulation.Sourceswδ 1 (10-2)δ 2 (10-2)λ1*(10-2)λ2*(10-8)Noise-free510522.001.525522.001.55035002.00471014.33332.00500025522.00500050222.005000Noise51062002.505002554502.501.55054502.901.571027.385.91.5810000258.285.91.5850050522.001.5. The experimental results confirm that the L 0 -PICCS outperforms the SM-PICCS in both visual inspection and quantitative analysis.