Journal of X-Ray Science and Technology - Volume 29, issue 4

Authors: Cui, Liyuan | Han, Shanhua | Qi, Shouliang | Duan, Yang | Kang, Yan | Luo, Yu

Article Type: Research Article

Abstract: BACKGROUND: Acute ischemic stroke (AIS) results in high morbidity, disability, and mortality. Early and automatic diagnosis of AIS can help clinicians administer the appropriate interventions. OBJECTIVE: To develop a deep symmetric 3D convolutional neural network (DeepSym-3D-CNN) for automated AIS diagnosis via diffusion-weighted imaging (DWI) images. METHODS: This study includes 190 study subjects (97 AIS and 93 Non-AIS) by collecting both DWI and Apparent Diffusion Coefficient (ADC) images. 3D DWI brain images are split into left and right hemispheres and input into two paths. A map with 125×253×14×12 features is extracted by each path of Inception Modules. …After the features computed from two paths are subtracted through L-2 normalization, four multi-scale convolution layers produce the final predation. Three comparative models using DWI images including MedicalNet with transfer learning, Simple DeepSym-3D-CNN (each 3D Inception Module is replaced by a simple 3D-CNN layer), and L-1 DeepSym-3D-CNN (L-2 normalization is replaced by L-1 normalization) are constructed. Moreover, using ADC images and the combination of DWI and ADC images as inputs, the performance of DeepSym-3D-CNN is also investigated. Performance levels of all three models are evaluated by 5-fold cross-validation and the values of area under ROC curve (AUC) are compared by DeLong’s test. RESULTS: DeepSym-3D-CNN achieves an accuracy of 0.850 and an AUC of 0.864. DeLong’s test of AUC values demonstrates that DeepSym-3D-CNN significantly outperforms other comparative models (p  < 0.05). The highlighted regions in the feature maps of DeepSym-3D-CNN spatially match with AIS lesions. Meanwhile, DeepSym-3D-CNN using DWI images presents the significant higher AUC than that either using ADC images or using DWI-ADC images based on DeLong’s test (p  < 0.05). CONCLUSIONS: DeepSym-3D-CNN is a potential method for automatically identifying AIS via DWI images and can be extended to other diseases with asymmetric lesions. Show more

Keywords: Acute ischemic stroke, diffusion-weighted imaging, deep learning, symmetric convolutional neural networks, automatic diagnosis

DOI: 10.3233/XST-210861

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 551-566, 2021

Authors: Komarskiy, Alexander Alexandrovich | Korzhenevskiy, Sergey Romanovich | Ponomarev, Andrey Viktorovich | Komarov, Nikita Alexandrovich

Article Type: Research Article

Abstract: BACKGROUND: Traditionally, X-ray systems for capturing moving objects consist of a continuous X-ray source and a detector that operates at a predetermined frame rate. OBJECTIVE: This study investigates the possibility of using pulsed X-ray source with an inductive energy storage device and a semiconductor opening switch for shooting moving objects. METHODS: The study uses a high-voltage pulse generator that has the following parameters namely, the pulse voltage amplitude up to 320 kV, the pulse current up to 240 A, the current pulse duration of about 50 ns, and the pulse repetition rate up to 2 kHz. The duration and …intensity of glow for standard CsI:Tl and Gd2 O2 S:Tb X-ray phosphors after their irradiation with X-ray flashes of about 50 ns duration are investigated. After X-ray radiation is converted into light, the signal is recorded using semiconductor detectors. We acquired several images of an object moving at a speed of about 20 m/s. A semiconductor detector with phosphor, which operates in the mode of continuous signal accumulation, is used. RESULTS: When using the pulsed X-ray source and phosphors with a short afterglow, the individual frames can be obtained at the pulse repetition rate of several kilohertz, and the detector does not contain the residual luminescence from the previous frame by the arrival of the next frame. CONCLUSIONS: The X-ray source shows good pulse-to-pulse reproducibility of X-rays, and can be used to capture objects in motion at a frame rate of several kHz. Show more

Keywords: Pulsed X-ray source, speed shooting, X-ray flash, semiconductor opening switch, inspection control, CT scan

DOI: 10.3233/XST-210873

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 567-576, 2021

Authors: Deng, Fuquan | Tie, Changjun | Zeng, Yingting | Shi, Yanbin | Wu, Huiying | Wu, Yu | Liang, Dong | Liu, Xin | Zheng, Hairong | Zhang, Xiaochun | Hu, Zhanli

Article Type: Research Article

Abstract: BACKGROUND: Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality to detect and diagnose coronary artery disease. Due to the limitations of equipment and the patient’s physiological condition, some CCTA images collected by 64-slice spiral computed tomography (CT) have motion artifacts in the right coronary artery, left circumflex coronary artery and other positions. OBJECTIVE: To perform coronary artery motion artifact correction on clinical CCTA images collected by Siemens 64-slice spiral CT and evaluate the artifact correction method. METHODS: We propose a novel method based on the generative adversarial network (GAN) to correct artifacts of CCTA …clinical images. We use CCTA clinical images collected by 64-slice spiral CT as the original dataset. Pairs of regions of interest (ROIs) cropped from original dataset or images with and without motion artifacts are used to train the dual-zone GAN. When predicting the CCTA images, the network inputs only the clinical images with motion artifacts. RESULTS: Experiments show that this network effectively corrects CCTA motion artifacts. Regardless of ROIs or images, the peak signal to noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE) and mean absolute error (MAE) of the generated images are greatly improved compared to those of the input data. In addition, based on scores from physicians, the average score for the coronary artery artifact correction of the output images is higher. CONCLUSIONS: This study demonstrates that the dual-zone GAN has the excellent ability to correct motion artifacts in the coronary arteries and maintain the overall characteristics of CCTA clinical images. Show more

Keywords: Coronary computed tomography angiography (CCTA), correction of motion artifact, cycle generative adversarial network (GAN)

DOI: 10.3233/XST-210841

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 577-595, 2021

Authors: Yuhara, Toshiyuki | Numano, Tomokazu

Article Type: Research Article

Abstract: BACKGROUND: Digital radiography (DR) is grayscale adjustable and it can be unclear whether an acquired DR image is captured with the minimum radiation dose required. It is necessary to make an image of the amount of noise when taken at a lower dose than the acquired image, without increased exposure. OBJECTIVE: To examine whether an image of unacquired dose can be created from two types of dose DR images acquired using a phantom. METHODS: To create an additive image from two images of different doses, the pixel value of one image is multiplied by a coefficient …and added to the other. The normalized noise power spectra (NNPS) of the normal image and the additive image with the same signal-to-noise ratio (SNR) are compared. The image noise of the unacquired doses is estimated from the graph changes of the pixel values and standard deviations of two images. The error between the SNR of the image obtained by changing the dose and the estimated SNR is measured. We propose a multiplication coefficient calculation formula that theoretically adjusts the additive image to the target SNR. The SNR error of the image created based on this formula is measured. RESULTS: The NNPS curves of the additive and normal images show a difference on the high frequency side. According to the statistics considering the preset of mAs value, there is no significant difference at 85%. The SNR estimation error is approximately 1%. The SNR error of the additive image created based on the formula is approximately 5%. CONCLUSION: The noise of the image of unacquired dose can be estimated, and the additive image adjusted to this value can be considered equivalent to the image taken at the actual dose. Show more

Keywords: Signal to noise ratio, arbitrary dose, normalized noise power spectrum, additive processing image, normal image, multiplication coefficient

DOI: 10.3233/XST-200807

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 597-615, 2021

Authors: Yu, Chang-Ching | Ting, Chien-Yi | Yang, Ming-Hui | Chan, Hung-Pin

Article Type: Research Article

Abstract: The Tc-99m methylene diphosphonate (MDP) whole body bone scan (WBBS) has been widely accepted as a method of choice for the initial diagnosis of bone and joint changes in patients with oncologic diseases. The WBBS has shown high sensitivity but relatively low specificity due to bone variation. This study aims to use the self-developing irregular flux viewer (IFV) system to predict possible bone lesions in planar WBBS. The study uses gradient vector flow (GVF) and self-organizing map (SOM) methods to analyze the blood fluid-dynamics and evaluate hot points. The evaluation includes a selection of 368 patients with bone metastasis from …prostate cancer, lung cancer and breast cancer. Finally, we compare IFV values with BONENAVI version data. BONENAVI is a computer-assisted diagnosis system that analyzes bone scintigraphy automatically. The analysis shows that the IFV system achieves sensitivities of 93% for prostate cancer, 91% for breast cancer, and 83% for lung cancer, respectively. On the other hand, our proposed approach achieves a higher sensitivity than the results of BONEVAVI version 2.0.5 for prostate cancer (88%), breast cancer (86%) and lung cancer (82%), respectively. The study results demonstrate that the high sensitivity and specificity of the IFV system can provide assistance for image interpretation and generate prediction values for WBBS. Show more

Keywords: Tc-99m MDP, irregular flux viewer (IFV), computer-aided diagnosis (CAD), whole body bone scan (WBBS), machine learning (ML)

DOI: 10.3233/XST-200834

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 617-633, 2021

Authors: Shao, Wencheng | Chen, Ziyin | Bai, Yanchun | Xu, Bingchen | Xu, Lili | Zhao, Qiushuang | Wang, Yang | Tang, Xiaobin

Article Type: Research Article

Abstract: PURPOSE: This study aims to evaluate the planned dose of stereotactic body radiation therapy (SBRT) for treating early peripheral non-small cell lung cancer (NSCLC) using the non-coplanar radiation from Cyberknife and Varian linac. Moreover, this study investigates whether Cyberknife and Varian linac are qualified for non-coplanar radiation SBRT for treating early peripheral NSCLC, and which one is better for protecting organs at risk (OARs). METHODS: Retrospective analysis was performed based on the Cyberknife radiation treatment plans (RTPs) and Varian Eclipse RTPs of 10 patients diagnosed with early peripheral NSCLC. The dose distributions in the target and OARs were …compared between the RTPs of Cyberknife and Varian Eclipse using Mim medical imaging software. RESULTS: For PTV, no significant difference in D98 and D95 between the Cyberknife and Eclipse was observed (t  = –0.35, –1.67, P  > 0.05). The homogeneity indexes (HIs) of Cyberknife plans are higher (t  = 71.86, P  < 0.05) than those of Eclipse plans. The V10 , V15 , V20 , V25 , V30 and Dmean of the lung with NSCLC and the V20 of the whole lung for Cyberknife were less than those for Eclipse (t  = –4.73, –5.62, –7.75, –6.38, –6.89, –3.14, –7.09, respectively, P  < 0.05). Cyberknife plans have smaller spinal cord Dmax , trachea Dmax , heart Dmax , chest wall Dmax (t  = –2.49, –2.57, –3.71, –3.56, respectively, P  < 0.05) and esophagus Dmax (t  = –1.95, P  > 0.05) than Varian Eclipse plans. CONCLUSION: To fulfill SBRT by non-coplanar radiation, Cyberknife is recommended for the institutions equipped with Cyberknife, while Varian linac can be applied for the institutions that have not adopted Cyberknife in clinical radiotherapy yet. Show more

Keywords: NSCLC, SBRT, Cyberknife, Varian linac, non-coplanar radiation

DOI: 10.3233/XST-210867

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 635-643, 2021

Authors: Zhang, Lingli

Article Type: Research Article

Abstract: BACKGROUND AND OBJECTIVE: Since the stair artifacts may affect non-destructive testing (NDT) and diagnosis in the later stage, an applicable model is desperately needed, which can deal with the stair artifacts and preserve the edges. However, the classical total variation (TV) algorithm only considers the sparsity of the gradient transformed image. The objective of this study is to introduce and test a new method based on group sparsity to address the low signal-to-noise ratio (SNR) problem. METHODS: This study proposes a weighted total variation with overlapping group sparsity model. This model combines the Gaussian kernel and overlapping group …sparsity into TV model denoted as GOGS-TV, which considers the structure sparsity of the image to be reconstructed to deal with the stair artifacts. On one hand, TV is the accepted commercial algorithm, and it can work well in many situations. On the other hand, the Gaussian kernel can associate the points around each pixel. Quantitative assessments are implemented to verify this merit. RESULTS: Numerical simulations are performed to validate the presented method, compared with the classical simultaneous algebraic reconstruction technique (SART) and the state-of-the-art TV algorithm. It confirms the significantly improved SNR of the reconstruction images both in suppressing the noise and preserving the edges using new GOGS-TV model. CONCLUSIONS: The proposed GOGS-TV model demonstrates its advantages to reduce stair artifacts especially in low SNR reconstruction because this new model considers both the sparsity of the gradient image and the structured sparsity. Meanwhile, the Gaussian kernel is utilized as a weighted factor that can be adapted to the global distribution. Show more

Keywords: Computed tomography (CT), image reconstruction, total variation (TV), group sparsity, signal-to-noise ratio (SNR).

DOI: 10.3233/XST-200833

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 645-662, 2021

Authors: Ge, Yu-Xi | Xu, Wen-Bo | Wang, Zi | Zhang, Jun-Qin | Zhou, Xin-Yi | Duan, Shao-Feng | Hu, Shu-Dong | Fei, Bo-Jian

Article Type: Research Article

Abstract: OBJECTIVES: This study aims to evaluate diagnostic performance of radiomic analysis using computed tomography (CT) to identify lymphovascular invasion (LVI) in patients diagnosed with rectal cancer and assess diagnostic performance of different lesion segmentations. METHODS: The study is applied to 169 pre-treatment CT images and the clinical features of patients with rectal cancer. Radiomic features are extracted from two different volumes of interest (VOIs) namely, gross tumor volume and peri-tumor tissue volume. The maximum relevance and the minimum redundancy, and the least absolute shrinkage selection operator based logistic regression analyses are performed to select the optimal feature subset …on the training cohort. Then, Rad and Rad-clinical combined models for LVI prediction are built and compared. Finally, the models are externally validated. RESULTS: Eighty-three patients had positive LVI on pathology, while 86 had negative LVI. An optimal multi-mode radiology nomogram for LVI estimation is established. The area under the receiver operating characteristic curves of the Rad and Rad-clinical combined model in the peri-tumor VOI group are significantly higher than those in the tumor VOI group (Rad: peri-tumor vs. tumor: 0.85 vs. 0.68; Rad-clinical: peri-tumor vs. tumor: 0.90 vs 0.82) in the validation cohort. Decision curve analysis shows that the peri-tumor-based Rad-clinical combined model has the best performance in identifying LVI than other models. CONCLUSIONS: CT radiomics model based on peri-tumor volumes improves prediction performance of LVI in rectal cancer compared with the model based on tumor volumes. Show more

Keywords: Lymphovascular invasion, rectal cancer, computed tomography, radiomics

DOI: 10.3233/XST-210877

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 663-674, 2021

Authors: Sun, Zongqiong | Jin, Linfang | Zhang, Shuai | Duan, Shaofeng | Xing, Wei | Hu, Shudong

Article Type: Research Article

Abstract: PURPOSE: To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS: The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, …filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS: In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p  < 0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p  > 0.05). CONCLUSION: The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer. Show more

Keywords: Gastric cancer, lauren type, radiomics, nomogram, computed tomography

DOI: 10.3233/XST-210888

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 675-686, 2021

Authors: Wang, Ya-Ning | Du, Yu | Shi, Gao-Feng | Wang, Qi | Li, Ru-Xun | Qi, Xiao-Hui | Cai, Xiao-Jia | Zhang, Xuan

Article Type: Research Article

Abstract: OBJECTIVE: To investigate feasibility of applying deep learning image reconstruction (DLIR) algorithm in a low-kilovolt enhanced scan of the upper abdomen. METHODS: A total of 64 patients (BMI<28) are selected for the enhanced upper abdomen scan and divided evenly into two groups. The tube voltages in Group A are 100kV in arterial phase and 80kV in venous phase, while tube voltages are 120kV during two phases in Group B. Image reconstruction algorithms used in Group A include the filtered back projection (FBP) algorithm, the adaptive statistical iterative reconstruction-Veo (ASIR-V 40% and 80%) algorithm, and the DLIR algorithm (DL-L, …DL-M, DL-H). Image reconstruction algorithm used in Group B is ASIR-V40%. The different reconstruction algorithm images are used to measure the common hepatic artery, liver, renal cortex, erector spinae, and subcutaneous adipose in the arterial phase and the average CT value and standard deviation of the portal vein, liver, spleen, erector spinae, and subcutaneous adipose in the portal phase. The signal-to-noise ratio (SNR) is calculated, and the images are also scored subjectively. RESULTS: In Group A, noise in the aorta, liver, portal vein (the portal phase), spleen (the portal phase), renal cortex, retroperitoneal adipose, and muscle is significantly lower in both the DL-H and ASIR-V80% images, and the SNR is significantly higher than those in the remaining groups (P <0.05). The SNR of each tissue and organ in Group B is not significantly different from that in DL-M, DL-L, and ASIR-V40% in Group A (P >0.05). The subjective image quality scores in the DL-H and B groups are higher than those in the other groups, and the FBP group has significantly lower image quality than the remaining groups (P <0.05). CONCLUSION: For upper abdominal low-kilovolt enhanced scan data, the DLIR-H gear yields a more satisfactory image quality than the FBP and ASIR-V. Show more

Keywords: Computed tomography (CT), CT image reconstruction, deep learning image reconstruction, X-ray tube voltage

DOI: 10.3233/XST-210892

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 687-695, 2021