Journal of X-Ray Science and Technology - Volume 27, issue 3

Authors: Lin, Chia-Hung | Kan, Chung-Dann | Chen, Wei-Ling | Huang, Ping-Tzan

Article Type: Research Article

Abstract: Pleural effusion is a pathologic symptom in which there is accumulation of body fluids around the lungs. A chest radiograph is a rapid examination technique and does not require complex setup for making a preliminary diagnosis of lung and heart diseases. In radiographic visualization, the symptom patterns appear as light or dark areas in the lung cavity. Computer-aided diagnosis is an automatic manner that can rapidly highlight the object region by preanalyzing medical images. It can improve the problems of manual inspection and allow diagnosis in remote medical facilities. Based on the ratios of lung anatomy, the automatic screening manner …based on pattern recognition can be viewed as pixel value detection in the bilateral lung cavities. In this study, a fractional-order convolution (FOC) process is used to enhance the original image for an accurate extrapolation of the desired object in an image. The specific object image feature can be improved, and an accurate quantification of the pleural effusion region can be obtained using the suitable ranges of fractional-order parameters. Based on the boundaries of homogeneous regions, the pixel ratios of the lung anatomy between normal and abnormal conditions can be computed. The pleural effusion sizes and volumes can be rapidly estimated through the number of pixel changes. The experimental results reveal that the feature maps are similar and stable on image enhancement and segmentation with two fractional-order enhancement masks, as fractional-order v  = 0.05 to 0.20 for mask 1# and v  = 0.80 to 0.95 for mask 2#, respectively. The results also demonstrate the feasibility of the study on combining two-dimensional image FOC-process and bounding box pixel analysis to estimate the moderate and large effusion sizes from 500–2,000 mL. Show more

Keywords: Pleural effusion, computer aided diagnosis, fractional-order convolution, fractional-order parameter

DOI: 10.3233/XST-180473

Citation: Journal of X-Ray Science and Technology, vol. 27, no. 3, pp. 517-535, 2019

Authors: Deng, Xiaojuan | Zhao, Yunsong | Li, Hongwei

Article Type: Research Article

Abstract: Reducing radiation dose while maintaining the quality of the reconstructed images is a major challenge in the computed tomography (CT) community. In light of the non-stationary Gaussian noise distribution, we developed a model that incorporates a noise-level weighted total variation (NWTV) regularization term for denoising the projection data. Contrary to the well-known edge-weighted total variation method, which aims for better edge preserving, the proposed NWTV tries to adapt the regularization with the spatially varying noise levels. Experiments on simulated data as well as the real imaging data suggest that the proposed NWTV regularization could achieve quite competitive results. For sinograms …with sharp edges, the NWTV could do a better job at balancing noise reduction and edge preserving, such that noise is removed in a more uniform manner. Another conclusion from our experiments is that the well-recognized stair-casing artifacts of TV regularization play little role in the reconstructed images when the NWTV method is applied to low-dose CT imaging data. Show more

Keywords: Denoising, weighted total variation, low-dose ct, non-stationary gaussian noise

DOI: 10.3233/XST-180469

Citation: Journal of X-Ray Science and Technology, vol. 27, no. 3, pp. 537-557, 2019

Authors: Gou, Junnian | Wu, Xiaoyuan | Dong, Haiying

Article Type: Research Article

Abstract: BACKGROUND: For sparse and limited angle projection Computed Tomography (CT), the reconstructed image usually suffers from considerable artifacts due to undersampled data. OBJECTIVE: To improve image reconstruction quality of sparse and limited angle projection CT, this study tested a novel reconstruction algorithm based on Dictionary Learning (DL) from sparse and limited projections. METHODS: The study used signal sparse representation and feature extraction to render the DL technology, which is constrained by L2 and Lp norms, respectively. A Lp Norm Dictionary Learning term is suitable for regular term of objective function for CT image reconstruction. This is …helpful for solving the objective function by combining algorithm of ART. Based on these features, the new algorithm of ART-DL-Lp is proposed for CT image reconstruction. The alternate solving strategy of the algorithm of “ART first, then adaptive DL” is provided in sequence. The impact on reconstruction results of ART-DL-Lp at different p values (0 < p  < 1) is also considered. RESULTS: For non-ideal projections with noise, the digital experiments show that ART-DL-Lp data were superior to those of ART, SART, and ART-DL-L2. Accordingly, the objective evaluation metrics for non-ideal situation of RMSE, MAE, PSNR, Residuals and SSIM are all better than those of contrasted three algorithms. The metrics curves of ART-DL-Lp algorithm are recorded as the best. In both incomplete projection situations, smaller p -value of ART-DL-Lp algorithm induces more close reconstructed images to the original form and better five objective evaluation metrics. CONCLUSIONS: Overall, the reconstruction efficiency of the proposed ART-DL-Lp for CT imaging using the noisy incomplete projections outperforms ART, SART and ART-DL-L2 algorithms. For ART-DL-Lp algorithm, lower p -values result in better reconstruction performance. Show more

Keywords: CT image reconstruction, incomplete projection, Lp dictionary learning, regular optimization

DOI: 10.3233/XST-180470

Citation: Journal of X-Ray Science and Technology, vol. 27, no. 3, pp. 559-572, 2019

Authors: Zhao, Xianyu | Jiang, Changhui | Zhang, Qiyang | Ge, Yongshuai | Liang, Dong | Liu, Xin | Yang, Yongfeng | Zheng, Hairong | Hu, Zhanli

Article Type: Research Article

Abstract:  Recently, low-dose computed tomography (CT) has become highly desirable due to the increasing attention paid to the potential risks of excessive radiation of the regular dose CT. However, ensuring image quality while reducing the radiation dose in the low-dose CT imaging is a major challenge. Compared to classical filtered back-projection (FBP) algorithms, statistical iterative reconstruction (SIR) methods for modeling measurement statistics and imaging geometry can significantly reduce the radiation dose, while maintaining the image quality in a variety of CT applications. To facilitate low-dose CT imaging, we in this study proposed an improved statistical iterative reconstruction scheme based on the …penalized weighted least squares (PWLS) standard combined with total variation (TV) minimization and sparse dictionary learning (DL), which is named as a method of PWLS-TV-DL. To evaluate this PWLS-TV-DL method, we performed experiments on digital phantoms and physical phantoms, and analyzed the results in terms of image quality and calculation. The results show that the proposed method is better than the comparison methods, which indicates the potential of applying this PWLS-TV-DL method to reconstruct low-dose CT images. Show more

Keywords: Low dose computed tomography, penalized weighted least squares, total variation, dictionary learning

DOI: 10.3233/XST-180477

Citation: Journal of X-Ray Science and Technology, vol. 27, no. 3, pp. 573-590, 2019