Journal of X-Ray Science and Technology - Volume Pre-press, issue Pre-press

Authors: Almohiy, Hussain M. | Hussein, Khalid I. | Alqahtani, Mohammed S. | Rawashdeh, Mohammad | Elshiekh, Elhussaien | Alshahrani, Madshush M. | Saad, Mohammed | Foley, Shane | Saade, Charbel

Article Type: Research Article

Abstract: BACKGROUND: Computed Tomographic (CT) imaging procedures have been reported as the main source of radiation in diagnostic procedures compared to other modalities. To provide the optimal quality of CT images at the minimum radiation risk to the patient, periodic inspections and calibration tests for CT equipment are required. These tests involve a series of measurements that are time consuming and may require specific skills and highly-trained personnel. OBJECTIVE: This study aims to develop a new computational tool to estimate the dose of CT radiation outputs and assist in the calibration of CT scanners. It may also provide an …educational resource by which radiological practitioners can learn the influence of technique factors on both patient radiation dose and the produced image quality. METHODS: The computational tool was developed using MATLAB in order to estimate the CT radiation dose parameters for different technique factors. The CT radiation dose parameters were estimated from the calibrated energy spectrum of the x-ray tube for a CT scanner. RESULTS: The estimated dose parameters and the measured values utilising an Adult CT Head Dose Phantom showed linear correlations for different tube voltages (80 kVp, 100 kVp, 120 kVp, and 140 kVp), with R2 nearly equal to 1 (0.99). The maximum differences between the estimated and measured CTDIvol were under 5 %. For 80 kVp and low tube currents (50 mA, 100 mA), the maximum differences were under 10%. CONCLUSIONS: The prototyped computational model provides a tool for the simulation of a machine-specific spectrum and CT dose parameters using a single dose measurement. Show more

Keywords: Computed tomography (CT), x-ray energy spectrum, volume CT dose index (CTDI), dose length product (DLP), MATLAB

DOI: 10.3233/XST-200731

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-11, 2020

Authors: Hu, Zhanli | Chen, Zixiang | Zhou, Chao | Hong, Xuda | Chen, Jianwei | Zhang, Qiyang | Jiang, Changhui | Ge, Yongshuai | Yang, Yongfeng | Liu, Xin | Zheng, Hairong | Li, Zhicheng | Liang, Dong

Article Type: Research Article

Abstract: Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and …the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study. Show more

Keywords: Stationary digital breast tomosynthesis (s-DBT), carbon nanotube (CNT), image reconstruction, image accuracy, image resolution

DOI: 10.3233/XST-200668

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2020

Authors: Cui, Jiali | Guo, Hua | Wang, Huafeng | Chen, Fuqiang | Shu, Lixia | Li, Lihong C.

Article Type: Research Article

Abstract: Currently, cardiac computed tomography angiography (CTA) is widely applied to coronary artery disease diagnosis. Automatic segmentation of coronary artery has played an important role in coronary artery disease diagnosis. In this study, we propose and test a fully automatic coronary artery segmentation method that does not require any human-computer interaction. The proposed method uses a growing strategy and contains three main parts namely, (1) the initial seed detection that automatically detects the root points of the left and right coronary arteries where the ascending aorta meets the coronary arteries, (2) the growing strategy that searches for the neighborhood blocks to …decide the existence of coronary arteries with an improved convolutional neural network, and (3) the iterative termination condition that decides whether the growing iteration finishes. The proposed framework is validated using a dataset containing 32 cardiac CTA volumes from different patients for training and testing. Experimental results show that the proposed method obtained a Dice loss ranged from 0.70 to 0.83, which indicates that the new method outperforms the traditional methods such as level set. Show more

Keywords: Coronary artery segmentation, computed tomography angiography (CTA), growing algorithm, 3D U-net, deep learning

DOI: 10.3233/XST-200707

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-16, 2020

Authors: Adelman, Zeev | Joskowicz, Leo

Article Type: Research Article

Abstract: BACKGROUND: Repeat CT scanning is ubiquitous in many clinical situations, e.g. to follow disease progression, to evaluate treatment efficacy, and to monitor interventional CT procedures. However, it incurs in cumulative radiation to the patient which can be significantly reduced by using a region of interest (ROI) and the existing baseline scan. OBJECTIVE: To obtain a high-quality reconstruction of a ROI with a significantly reduced X-ray radiation dosage that accounts for deformations. METHODS: We present a new method for deformable registration and image reconstruction inside an ROI in repeat CT scans with a highly reduced X-ray radiation …dose based on sparse scanning. Our method uses the existing baseline scan data, a user-defined ROI, and a new sparse repeat scan to compute a high-quality repeat scan ROI image with a significantly reduced radiation dose. Our method first performs rigid registration between the densely scanned baseline and the sparsely scanned repeat CT scans followed by deformable registration with a low-order parametric model, both in 3D Radon space and without reconstructing the repeat scan image. It then reconstructs the repeat scan ROI without computing the entire repeat scan image. RESULTS: Our experimental results on clinical lung and liver CT scans yield a mean × 14 computation speedup and a × 7.6-12.5 radiation dose reduction, with a minor image quality loss of 0.0157 in the NRMSE metric. CONCLUSION: Our method is considerably faster than existing methods, thereby enabling intraoperative online repeat scanning that it is accurate and accounts for position, deformation, and structure changes at a fraction of the radiation dose required by existing methods. Show more

Keywords: sparse CT scanning, repeat scanning, ROI reconstruction, deformable registration, 3D Radon space

DOI: 10.3233/XST-200706

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-21, 2020

Authors: Fujiwara, Kohei | Wanxuan, Fang | Okino, Taichi | Sutherland, Kenneth | Furusaki, Akira | Sagawa, Akira | Kamishima, Tamotsu

Article Type: Research Article

Abstract: BACKGROUND: Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs are comparable with the conventional human scoring method regarding detectability of the radiographic progression of RA. Thus, automatic and accurate selection of relevant images (e.g. hand images) among radiographic images of various body parts is necessary for serial analysis on a large scale. OBJECTIVE: In this study we examined whether deep learning can select target images from a large number of stored images retrieved from a …picture archiving and communication system (PACS) including miscellaneous body parts of patients. METHODS: We selected 1,047 X-ray images including various body parts and divided them into two groups: 841 images for training and 206 images for testing. The training images were augmented and used to train a convolutional neural network (CNN) consisting of 4 convolution layers, 2 pooling layers and 2 fully connected layers. After training, we created software to classify the test images and examined the accuracy. RESULTS: The image extraction accuracy was 0.952 and 0.979 for unilateral hand and both hands, respectively. In addition, all 206 test images were perfectly classified into unilateral hand, both hands, and the others. CONCLUSIONS: Deep learning showed promise to enable efficiently automatic selection of target X-ray images of RA patients. Show more

Keywords: Rheumatoid arthritis, deep learning, image classification, convolutional neural network

DOI: 10.3233/XST-200694

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-8, 2020

Authors: Pan, Ruigen | Yang, Xueli | Shu, Zhenyu | Gu, Yifeng | Weng, Lihua | Jia, Yuezhu | Feng, Jianju

Article Type: Research Article

Abstract: OBJECTIVE: To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer. METHODS: Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9–10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the …value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer. RESULTS: Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P  < 0.05). Energy value of group GS = 6 is significantly higher than that of groups GS = 8 and 9–10 (P  < 0.005), which is similar between three groups (GS = 3 + 4, 8 and 9–10). The entropy and energy values correlate with GS (r  = 0.767, r = –0.692, P  < 0.05). Areas under ROC curves (AUC) of combination of entropy and energy are greater than that of using energy alone between groups GS = 6 and ≥7. Analogously, AUC of combination of entropy and energy are significantly higher than that of using entropy alone between groups GS≤3 + 4 and ≥4 + 3, as well as between groups GS≤4 + 3 and ≥8. CONCLUSION: Texture analysis on T2-weighted images of prostate cancer can evaluate Gleason score, especially using the combination of entropy and energy rendering better diagnostic efficiency. Show more

Keywords: Prostate cancer, magnetic resonance imaging, texture analysis, Gleason score

DOI: 10.3233/XST-200695

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-12, 2020

Authors: Lei, Pinggui | Zhang, Piaochen | Liu, Qianijao | Wang, Yan | Wang, Pingxian | Duan, Qinghong | Liu, Jing | Zhou, Shi | Qian, Wei | Jiao, Jun

Article Type: Research Article

Abstract: OBJECTIVE: To study the diagnostic value of real-time ultrasound shear wave elastography (US-SWE) in evaluating the histological stages of nonalcoholic fatty liver disease (NAFLD) in a rabbit model. MATERIALS AND METHODS: Twenty-one 8-week-old rabbits were fed a high-fat, high-cholesterol diet (experimental groups), and seven rabbits were fed a standard diet (control group). All rabbits underwent real-time US-SWE at various time points to document the histological stages of NAFLD. We categorized the histological stages as normal, NAFL, borderline nonalcoholic steatohepatitis (NASH), and NASH. We measured the elastic modulus of the liver parenchyma and analyzed the diagnostic efficacy of real-time …US-SWE using the area under receiver operating characteristic curve (AUC) for the four histological stages. RESULTS: The mean, minimum, and maximum elastic modulus increase for NAFL, borderline NASH, and NASH. For the mean, minimum, and maximum elastic modulus, AUCs are 0.891 (95% confidence interval [CI]: 0.716–0.977), 0.867 (95% CI: 0.686–0.965), and 0.789(95% CI:0.594–0.919) for differentiating normal liver from liver with NAFLD, respectively; AUCs are 0.846 (95% CI: 0.660–0.954), 0.818 (95% CI: 0.627–0.937), and 0.797(95% CI:0.627–0.913) for differentiating normal liver or liver with NAFL from liver with borderline NASH or NASH, respectively; AUCs are 0.889 (95% CI: 0.713–0.976), 0.787 (95% CI: 0.591–0.918), and 0.895 (95% CI:0.720–0.978) for differentiating liver with NASH from liver with borderline NASH or normal liver, respectively. CONCLUSIONS: Real-time US-SWE is an accurate, noninvasive technique for evaluating the histological stages of NAFLD by measuring liver stiffness. We recommend using the mean elastic modulus to differentiate the histological stages, with the minimum and maximum elastic modulus as valuable complements. Show more

DOI: 10.3233/XST-200676

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-11, 2020

Authors: Zhang, Liqun | Chen, Ke | Han, Lin | Zhuang, Yan | Hua, Zhan | Li, Cheng | Lin, Jiangli

Article Type: Research Article

Abstract: BACKGROUND: Calcification is an important criterion for classification between benign and malignant thyroid nodules. Deep learning provides an important means for automatic calcification recognition, but it is tedious to annotate pixel-level labels for calcifications with various morphologies. OBJECTIVE: This study aims to improve accuracy of calcification recognition and prediction of its location, as well as to reduce the number of pixel-level labels in model training. METHODS: We proposed a collaborative supervision network based on attention gating (CS-AGnet), which was composed of two branches: a segmentation network and a classification network. The reorganized two-stage collaborative semi-supervised model …was trained under the supervision of all image-level labels and few pixel-level labels. RESULTS: The results show that although our semi-supervised network used only 30% (289 cases) of pixel-level labels for training, the accuracy of calcification recognition reaches 92.1%, which is very close to 92.9% of deep supervision with 100% (966 cases) pixel-level labels. The CS-AGnet enables to focus the model’s attention on calcification objects. Thus, it achieves higher accuracy than other deep learning methods. CONCLUSIONS: our collaborative semi-supervised model has a preferable performance in calcification recognition, and it reduces the number of manual annotations of pixel-level labels. Moreover, it may be of great reference for the object recognition of medical dataset with few labels. Show more

Keywords: Thyroid nodule, calcification recognition, deep learning, attention mechanism, semi supervision, collaborative supervision.

DOI: 10.3233/XST-200740

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 2020

Authors: Mayte, Buchbender | Birte, Koch | Rainer, Kesting Marco | Edward, Matta Ragai | Werner, Adler | Anna, Seidel | Martin, Schmitt Christian

Article Type: Research Article

Abstract: BACKGROUND/OBJECTIVE: In this retrospective study, we aimed to investigate a new 3D evaluation method for evaluating bone regeneration after cystectomy of odontogenic cysts. METHODS: The study included 26 patients who underwent cystectomies between 2012 and 2017 and had received either fillings or non-fillings with autologous iliac crest. Bony regeneration was analyzed using 3D imaging software and comparing identical regions of interest (ROIs) that were determined by exact overlays of the postoperative cone beam computer tomography (CBCT) or computer tomography (CT) images. Outcome measures, including volume changes according to the defect size and configuration, patient age, the entity and …distribution of the cysts, were collected. RESULTS: Twenty-six patients (5 women and 21 men) had 30 defects, including nine keratocysts, seven radicular cysts and 14 dentigerous cysts. A total of 73% of the defects were in the mandible. The mean 3D follow-up time was 12 months. According to the 3D evaluation of bony regeneration, the defect size and configuration showed no significant differences between the groups (filled or non-filled with 15 defects per group). CONCLUSIONS: By establishing a standardized 3D method for evaluating bone regeneration, healing can be better monitored and evaluated. Show more

Keywords: Autologous bone, cystectomy, defect filling, odontogenic cysts, three-dimensional methodology

DOI: 10.3233/XST-200690

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-15, 2020

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-190469

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-21, 2019