Studies have shown that the conventionally estimated visibility and differential phase signals in grating-based Talbot-Lau imaging system are intrinsically biased signals. Since such bias are mainly caused by applying the conventional signal estimation approach on noisy data, therefore, it remains an open question whether there has a better signal estimation method to reduce such bias. To answer this question, we proposed an end-to-end supervised deep computed signal estimation network (XP-NET) to extract the three unknown signals, i.e., the absorption, the dark-field, and the phase contrast. Numerical phase stepping data generated from natural images are utilized to train the network. Afterwards, both numerical and experimental studies are performed to validate the performance of the proposed XP-NET method. Results show that for high radiation dose levels, signals retrieved from the XP-NET method are identical as obtained from the conventional analytical method. However, the XP-NET method has the capability of reducing phase signal bias by as much as 15% when the radiation dose levels gets lower. As the phase signal becomes more unbiased, the phase images get more accurate.
In this work, we realize the image-domain backproject-filter (BPF) CT image reconstruction using the convolutional neural network (CNN) method. Within this new CT image reconstruction framework, the acquired sinogram data is backprojected first to generate the highly blurred laminogram. Afterwards, the laminogram is feed into the CNN to retrieve the desired sharp CT image. Both numerical and experimental results demonstrate that this new CNN-based image reconstruction method is feasible to reconstruct CT images with maintained high spatial resolution and accurate pixel values from the laminogram as of from the conventional FBP method. The experimental results also show that the performance of this new CT image reconstruction network does not rely on the used radiation dose level. Due to these advantages, this proposed innovative CNN-based image-domain BPF type image reconstruction strategy provides promising prospects in generating high quality CT images for future clinical applications.
In this work, we present a novel convolutional neural network (CNN) enabled Moiré artifacts reduction framework for the three contrast mechanism images, i.e., the absorption image, the differential phase contrast (DPC) image, and the dark-field (DF) image, obtained from an x-ray Talbot-Lau phase contrast imaging system. By mathematically model the various potential non-ideal factors that may cause Moiré artifacts as a random fluctuation of the phase stepping position, rigorous theoretical analyses show that the Moiré artifacts on absorption images may have similar distribution frequency as of the detected phase stepping Moiré diffraction fringes, whereas, their periods on DPC and DF images may be doubled. Upon these theoretical findings, training dataset for the three different contrast mechanisms are synthesized properly using natural images. Afterwards, the three datasets are trained independently by the same modified auto-encoder type CNN. Both numerical simulations and experimental studies are performed to validate the performance of this newly developed Moiré artifacts reduction method. Results show that the CNN is able to reduce residual Moiré artifacts efficiently. With the improved signal accuracy, as a result, the radiation dose efficiency of the Talbot-Lau interferometry imaging system can be greatly enhanced.