Journal of X-Ray Science and Technology - Volume 28, issue 6

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. 28, no. 6, pp. 1025-1035, 2020

Authors: Komolafe, Temitope E. | Du, Qiang | Zhang, Yin | Wu, Zhongyi | Zhang, Cheng | Li, Ming | Zheng, Jian | Yang, Xiaodong

Article Type: Research Article

Abstract: BACKGROUND: Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. OBJECTIVE: To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. METHODS: The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity …of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. RESULTS: The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. CONCLUSIONS: This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images. Show more

Keywords: Hybrid optimization method, simulated dual-energy, breast computed tomography, material basis

DOI: 10.3233/XST-190639

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 6, pp. 1037-1054, 2020

Authors: Yan, Aimin | Wu, Xizeng | Liu, Hong

Article Type: Research Article

Abstract: Dual phase grating X-ray interferometry is radiation dose-efficient as compared to common Talbot-Lau grating interferometry. The authors developed a general quantitative theory to predict the fringe visibility in dual-phase grating X-ray interferometry with polychromatic X-ray sources. The derived formulas are applicable to setups with phase gratings of any phase modulation and with either monochromatic or polychromatic X-rays. Numerical simulations are presented to validate the derived formulas. The theory provides useful tools for design optimization of dual-phase grating X-ray interferometers.

Keywords: X-ray imaging, X-ray interferometry

DOI: 10.3233/XST-200726

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 6, pp. 1055-1067, 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. 28, no. 6, pp. 1069-1089, 2020

Authors: Chen, Zixiang | Zhang, Qiyang | Zhou, Chao | Zhang, Mengxi | Yang, Yongfeng | Liu, Xin | Zheng, Hairong | Liang, Dong | Hu, Zhanli

Article Type: Research Article

Abstract: BACKGROUND: Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, an advanced reconstruction algorithm is also needed. OBJECTIVE: To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image. METHODS: A low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) involving a transformed model for attenuation coefficients of the object to be reconstructed and prior information …application in the forward-projection process was used to reconstruct CT images from sparse-view projection data. A digital extended cardiac-torso (XCAT) ventral phantom and a diagnostic head phantom were employed to evaluate the performance of the proposed PI-NDI method. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR) and mean percent absolute error (MPAE) of the reconstructed images were measured for quantitative evaluation of the proposed PI-NDI method. RESULTS: The reconstructed images with sparse-view projection data via the proposed PI-NDI method have higher quality by visual inspection than that via the compared methods. In terms of quantitative evaluations, the RMSE measured on the images reconstructed by the PI-NDI method with sparse projection data is comparable to that by MLEM-TV, PWLS-TV and PWLS-PICCS with fully sampled projection data. When the projection data are very sparse, images reconstructed by the PI-NDI method have higher PSNR values and lower MPAE values than those from the compared algorithms. CONCLUSIONS: This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction. The experimental results validate that the new method has superior performance over other state-of-art methods. Show more

Keywords: Computed tomography, sparse-view, prior image, prior matrix, image reconstruction

DOI: 10.3233/XST-200716

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 6, pp. 1091-1111, 2020

Authors: Liu, Peng | Gu, Qianbiao | Hu, Xiaoli | Tan, Xianzheng | Liu, Jianbin | Xie, An | Huang, Feng

Article Type: Research Article

Abstract: PURPOSE: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with …the conventional CT evaluation method based on subjective CT image features. RESULTS: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. −1.514±1.800 (mean±standard deviation) with p  < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment. Show more

Keywords: Computed tomography, radiomics, pancreatic ductal Adenocarcinoma, lymph node metastasis, personalized medicine

DOI: 10.3233/XST-200730

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 6, pp. 1113-1121, 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. 28, no. 6, pp. 1123-1139, 2020

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

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. 28, no. 6, pp. 1141-1155, 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. 28, no. 6, pp. 1157-1169, 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. 28, no. 6, pp. 1171-1186, 2020