With the appearance of the field-emission cold cathode x-ray source, the fast response and small in size innovate the traditional imaging system. Although the field-emission x-ray source array generates multiple novel imaging modalities, it still faces a long stand-off distance between source and object. To realize a portable, smart, extremely low dose imaging modality, researchers proposed a concept to design a two-dimensional array field-emission source, namely the flat-panel source. In this paper, a real imaging system based on the ZnO field-emission flat-panel source is designed. Currently, the real flat-panel source faces an extremely low dose and non-addressable situation. Hence, the measurement based on the flat-panel source is overlapped and without an application potential. We first try to realize the imaging ability of the flat-panel source by designing a rebinning algorithm. With the analysis on the overlapped measurement, a high-order prior is introduced into the rebinning algorithm to improve the performance. Simulation and real data experiments verified our proposed method. Compared to the no high-order prior, the proposed algorithm can recover a more distinct measurement.
As an alternative to conventional sources, field emission x-ray cold cathodes of nanomaterials have been developed in recent years. Many different imaging geometries with this kind of source have been proposed, which has the merits of fast response, low energy consumption, and individually addressable switching ability. In this work, we proposed a novel digital tomosynthesis (DTS) geometry based on field emission flat-panel X-ray source array (FEF X-ray source array) and a reconstruction method based on this new geometry. The new DTS with designed lighting mode has shorter acquisition time and lower dose compared with the traditional DTS scheme. Due to the designed lighting mode, it cannot use a traditional reconstruction algorithm. The proposed reconstructed algorithm builds the relation on photons to solve the reconstruction problem. The simulated result shows that the proposed method can obtain 5pl / mm in the X-Y plane and 2pl / mm in the Z plane which indicates the potential of the proposed reconstruction modality.
In flat-panel based cone beam computed tomography (CBCT), ring artifacts always exist and degrade the quality of reconstructed images. In this work, we propose a convolutional neural network (CNN) based ring artifact reduction algorithm in CT images, which fuses the information from the original and corrected images to eliminate the artifacts. The proposed method consists of two steps. First, we establish a database consisting of three types of images for training, artifact-free, ring artifact and pre-corrected images. Second, the original and pre-corrected images are input to the trained CNN to generate an image with less artifacts. To further reduce the artifacts, by using image mutual correlation, pixels in the pre-corrected image and the CNN output image, which are less sensitive to artifacts, are combined to generate a hybrid corrected image. Both simulated and real data experiments were performed to verify the proposed method. Experimental results show that the proposed method can effectively suppress the ring artifacts without introducing processing distortion to the image structure.
Conventional Cone-Beam Computed Tomography (CBCT) acquisition suffers from motion blurring artifacts at the region of the thorax, and consequently, it may result in inaccuracy in localizing the target of treatment and verifying delivered dose in radiation therapy. Although 4D-CBCT reconstruction technology is available to alleviate the motion blurring artifacts with the strategy of projection sorting followed by independent reconstruction, under-sampling streaking artifacts and noise are observed in the set of 4D-CBCT images due to relatively fewer projections and large angular spacing in each phase. Aiming at improving the overall quality of 4D-CBCT images, we explored the performance of the deep learning model on 4D-CBCT images, which has been paid little attention before. Inspired by the high correlation among the 4D-CBCT images at different phases, we incorporated a prior image reconstructed from full-sampled projections beforehand into a lightweight structured convolutional neural network (CNN) as one input channel. The prior image used in the CNN model can guide the final output image to restore detailed features in the testing process, so it is referred to as Prior-guided CNN. Both simulation and real data experiments have been carried out to verify the effectiveness of our CNN model. Experimental results demonstrate the effectiveness of the proposed CNN regarding artifact suppression and preservation of anatomical structures. Quantitative evaluations also indicate that 33.3% and 21.2% increases in terms of Structural Similarity Index (SSIM) have been achieved by our model when comparing with gated reconstruction and images tested on CNN without prior knowledge, respectively.
Regularization parameter selection is pivotal in optimizing reconstructed images which controls a balance between fidelity and penalty term. Images reconstructed with the optimal regularization parameter will keep the detail preserved and the noise restrained at the same time. In previous work, we have used CT image statistics to select the optimal regularization parameter by calculating the second order derivates of image variance (Soda-curve). But same as L-curve method, it also needs multiple reconstruction in different regularization parameters which will spend plenty of time. In this paper, we dive into the relationship between image statistics changes and regularization parameter during the iteration. Meanwhile, we propose a method based on the empirical regularity found in the iterations to tune the regularization parameter automatically in order to maintain the image quality. Experiments show that the images reconstructed with the regularization parameters tuned by the proposed method have higher image quality as well as less time when compared to L-curve based results.
There are growing concerns on the effect of the radiation, which can be decreased by reducing X-ray tube current. However, this manner will lead to the degraded image due to the quantum noise. In order to alleviate the problem, multiple methods have been explored both during reconstruction and in post-processing. Recently, Denoising Auto-Encoder(DAE) has drawn much attention which can generate clean images from corrupted input. Inspired by the idea of DAE, during the low dose acquisition, the noisy projection can be regarded as corrupted images. In this paper, we proposed a denoising method based on projection domain. First, the DAE is train from stimulation noisy data coupled with original data. Then utilize the DAE to correct noisy projection and get denoised image from statistical iterative reconstruction. With the implement of DAE in projection domain, the reconstructions show clearer details in soft tissue and have higher SSIM (structural similarity index) than other denoising methods in image domain.