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{Objective: }X-ray scatter leads to signal bias and degrades the image quality in CT imaging. Conventional real-time scatter estimation and correction methods include the scatter kernel superposition (SKS) methods, which approximate X-ray scatter field as a convolution of the scatter sources and scatter propagation kernels to reflect the spatial spreading of scatter X-ray photons. SKS methods are fast to implement but generally suffer from low accuracy due to the difficulties in determining the scatter kernels. {Approach: }To address such a problem, this work describes a new scatter estimation and correction method by combining the concept of SKS methods and convolutional neural network. Unlike conventional SKS methods which estimate the scatter amplitude and the scatter kernel based on the value of an individual pixel, the proposed method generates the scatter amplitude maps and the scatter width maps from projection images through a neural network, from which the final estimated scatter field is calculated based on a convolution process. {Main Results: }By incorporating physics in the network design, the proposed method requires fewer trainable parameters compared with another deep learning-based method (Deep Scatter Estimation). Both numerical simulations and physical experiments demonstrate that the proposed SKS-inspired convolutional neural network outperforms the conventional SKS method and other deep learning-based methods in both qualitative and quantitative aspects. {Significance: } The proposed method can effectively correct the scatter-related artifacts with a SKS-inspired convolutional neural network design.

In computed tomography, high attenuation occurs when x-rays pass through a dense region or a long path in the scanning object. In this case, only limited photons reach the detector, which causes photon starvation artifacts. The artifacts usually appear as streaks along the directions with high attenuation. It might lower the discrimination of minor structures and lead to misdiagnosis. Applying a local filter to the projection data adaptively is a common solution, however, if the parameters of projection-based filter are not well selected, new artifacts and noise might appear in the final image. In this paper, a post image processing technique was developed to suppress the photon starvation streak artifacts. Based on the directional characteristics of streaks, a semi-adaptive anisotropic diffusion filter was applied to the high frequency sub-bands after wavelet transformation (WASA). Qualitative and quantitative experiments were performed on phantom data and clinical data to prove the effectiveness of this method for photon starvation artifact suppression.

This paper presents a simple and efficient tunable microstrip band pass filter based on the reflected group delay method. The effect of varactor location on the filter performance is investigated in details to guide the filter design. The measured results are consistent with both analytical and simulation results.Index Terms -Band pass filter, microstrip filter, reflected group delay, tunable filter.