Journal of X-Ray Science and Technology - Volume 30, issue 2

Authors: Shen, Zhaoqiang | Zeng, Li | Gong, Changcheng | Guo, Yumeng | He, Yuanwei | Yang, Zhaojun

Article Type: Research Article

Abstract: In computed tomography (CT) image reconstruction problems, exterior CT is an important application in industrial non-destructive testing (NDT). Different from the limited-angle problem that misses part of the rotation angle, the rotation angle of the exterior problem is complete, but for each rotation angle, the projection data through the central region of the object cannot be collected, so that the exterior CT problem is ill-posed inverse problem. The results of traditional reconstruction methods like filtered back-projection (FBP) and simultaneous algebra reconstruction technique (SART) have artifacts along the radial direction edges for exterior CT reconstruction. In this study, we propose and …test an anisotropic relative total variation in polar coordinates (P-ARTV) model for addressing the exterior CT problem. Since relative total variation (RTV) can effectively distinguish edges from noises, and P-ARTV with different weights in radial and tangential directions can effectively enhance radial edges, a two-step iteration algorithm was developed to solve the P-ARTV model in this study. The fidelity term and the regularization term are solved in Cartesian and polar coordinate systems, respectively. Numerical experiments show that our new model yields better performance than the existing state-of-the-art algorithms. Show more

Keywords: Image reconstruction, computed tomography, exterior problem, polar coordinates, anisotropic relative total variation (ARTV)

DOI: 10.3233/XST-211042

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 343-364, 2022

Authors: Albahli, Saleh | Ahmad Hassan Yar, Ghulam Nabi

Article Type: Research Article

Abstract: BACKGROUND: Chest X-ray images are widely used to detect many different lung diseases. However, reading chest X-ray images to accurately detect and classify different lung diseases by doctors is often difficult with large inter-reader variability. Thus, there is a huge demand for developing computer-aided automated schemes of chest X-ray images to help doctors more accurately and efficiently detect lung diseases depicting on chest X-ray images. OBJECTIVE: To develop convolution neural network (CNN) based deep learning models and compare their feasibility and performance to classify 14 chest diseases or pathology patterns based on chest X-rays. METHOD: Several …CNN models pre-trained using ImageNet dataset are modified as transfer learning models and applied to classify between 14 different chest pathology and normal chest patterns depicting on chest X-ray images. In this process, a deep convolution generative adversarial network (DC-GAN) is also trained to mitigate the effects of small or imbalanced dataset and generate synthetic images to balance the dataset of different diseases. The classification models are trained and tested using a large dataset involving 91,324 frontal-view chest X-ray images. RESULTS: In this study, eight models are trained and compared. Among them, ResNet-152 model achieves an accuracy of 67% and 62% with and without data augmentation, respectively. Inception-V3, NasNetLarge, Xcaption, ResNet-50 and InceptionResNetV2 achieve accuracy of 68%, 62%, 66%, 66% and 54% respectively. Additionally, Resnet-152 with data augmentation achieves an accuracy of 83% but only for six classes. CONCLUSION: This study solves the problem of having fewer data by using GAN-based techniques to add synthetic images and demonstrates the feasibility of applying transfer learning CNN method to help classify 14 types of chest diseases depicting on chest X-ray images. Show more

Keywords: Convolution neural network (CNN), deep learning, chest diseases, chest X-ray images, radiographic findings, ResNet-152, inception-V3

DOI: 10.3233/XST-211082

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 365-376, 2022

Authors: Gai, Tiancheng | Thai, Theresa | Jones, Meredith | Jo, Javier | Zheng, Bin

Article Type: Research Article

Abstract: BACKGROUND: Pancreatic cancer is one of the most aggressive cancers with approximate 10% five-year survival rate. To reduce mortality rate, accurate detection and diagnose of suspicious pancreatic tumors at an early stage plays an important role. OBJECTIVE: To develop and test a new radiomics-based computer-aided diagnosis (CAD) scheme of computed tomography (CT) images to detect and classify suspicious pancreatic tumors. METHODS: A retrospective dataset consisting of 77 patients who had suspicious pancreatic tumors detected on CT images was assembled in which 33 tumors are malignant. A CAD scheme was developed using the following 5 steps namely, …(1) apply an image pre-processing algorithm to filter and reduce image noise, (2) use a deep learning model to detect and segment pancreas region, (3) apply a modified region growing algorithm to segment tumor region, (4) compute and select optimal radiomics features, and (5) train and test a support vector machine (SVM) model to classify the detected pancreatic tumor using a leave-one-case-out cross-validation method. RESULTS: By using the area under receiver operating characteristic (ROC) curve (AUC) as an evaluation index, SVM model yields AUC = 0.750 with 95% confidence interval [0.624, 0.885] to classify pancreatic tumors. CONCLUSIONS: Study results indicate that radiomics features computed from CT images contain useful information associated with risk of tumor malignancy. This study also built a foundation to support further effort to develop and optimize CAD schemes with more advanced image processing and machine learning methods to more accurately and robustly detect and classify pancreatic tumors in future. Show more

Keywords: Pancreatic cancer detection, classification of pancreatic tumor, computer-aided diagnosis (CAD), support vector machine, image segmentation using deep learning model

DOI: 10.3233/XST-211116

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 377-388, 2022

Authors: Yan, Cheng | Liu, Jing | Yang, Xue | Cai, Songqi | Lu, Xiuliang | Yang, Chun | Zeng, Mengsu | Zhou, Guofeng | Ji, Min

Article Type: Research Article

Abstract: BACKGROUND: Due to the limited temporal resolution and cardiac motion, coronary computed tomography angiography (CCTA) exam is one of the most challenging CT protocols which may require operating radiologist to apply additional phase adjustment or motion correction for image reconstruction. OBJECTIVE: To evaluate image quality between automatic and manual CCTA reconstruction in a 0.25 second rotation time, 16 cm coverage, single-beat, CT scanner with automated phase selection and AI-assisted motion correction. METHODS: CCTA exams of 535 consecutive patients were included. All exams were first reconstructed with an automatically selected phase. If there was an unacceptable motion artifact, …a manual reconstruction process was performed by radiologists. Additionally, automatic image series which consist of auto-phase selection and a follow-up motion correction were reconstructed. For these two manual and automatic image series, a four-point Likert scale rating system was used to evaluate image quality of the coronary artery segment by two experienced radiologists, according to the 18-segment model. RESULTS: Fifty-one patients (9.5%) did not have satisfactory image quality after auto-phase selection. In these patients, the heart rate during scanning was higher (78.3±18.4 bpm) than in the remaining 484 patients (68.9±13.1 bpm). Overall, 734 out of the 918 vessel segments were identified for quality evaluation among 51 patients. Automatic and manual image series were rated as having average Likert scores of 3.48±0.62 and 3.32±0.67 (P  < 0.001), respectively. CONCLUSIONS: Using a 0.25 second rotation speed, 16 cm z-coverage, CT scanner installed with an AI-assisted motion correction algorithm, the automatic image reconstruction with scanner equipped auto-phase-selection and motion correction algorithm outperforms manually controlled image reconstruction by radiologists. This suggests that the traditional CCTA exam reconstruction workflow could be altered allowing less radiologist involvement and becoming more efficient. Show more

Keywords: Computed tomography angiography, CTA protocol, artificial intelligence, image artifact correction, X-Ray computed tomography

DOI: 10.3233/XST-211048

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 389-398, 2022

Authors: Jeon, Pil-Hyun | Lee, Chang-Lae

Article Type: Research Article

Abstract: BACKGROUND: Expanding computed tomography (CT) detector coverage broadens the beam width, but inaccurate tube current application can reduce image quality at the boundaries between body regions with different attenuation values along the z-axis. OBJECTIVE: This study aims to develop and validate a new CT scanning technique with a fixed pitch to achieve higher imaging quality. METHODS: A cylindrical water phantom and an anthropomorphic chest phantom with different diameters represent a human body with different attenuation values. By optimizing the beam width and helical pitch, the pitch is fixed during scanning. The mean noise of the images …and the standard deviation were calculated, and the coefficient of variation (COV) was compared to evaluate the uniformity of image noise according to the beam width. RESULTS: At the boundaries between regions with different attenuation values, the 10 mm beam width (COV: 0.065) in the water phantom showed a 47.7% COV reduction of image noise compared with the 20 mm beam width (COV: 0.125). In addition, the 20 mm beam width (COV: 0.146) in the chest phantom showed a 29.3% COV reduction of image noise compared with the 40 mm beam width (COV: 0.206). Thus, as the beam was narrowed, the mean noise was similar, but the standard deviation was reduced. CONCLUSIONS: The proposed CT scanning technique with a fixed pitch, optimized beam width, and helical pitch demonstrates that image quality can be improved without increasing radiation dose at the boundary between regions with different attenuation values. Show more

Keywords: X-ray beam width, computed tomography (CT) scanning, helical pitch, CT image noise, CT image quality.

DOI: 10.3233/XST-211103

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 399-408, 2022