Journal of X-Ray Science and Technology - Volume 31, issue 2

Authors: Chen, Yang | He, Yiwen | Jiang, Zhuoyun | Xie, Yuanzhong | Nie, Shengdong

Article Type: Research Article

Abstract: BACKGROUND: Cardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes. OBJECTIVE: In order to assist doctors in the precise diagnosis and treatment of patients, this study proposed an IS subtyping method combining convolutional neural networks (CNN) and radiomics. METHODS: Firstly, brain embolism regions were segmented from the computed tomography angiography (CTA) images, and radiomics features were extracted; Secondly, the extracted radiomics features were optimized with the L2 norm, and the feature selection was performed by combining random forest; then, the CNN Cap-UNet was built to extract the deep …learning features of the last layer of the network; Finally, combining the selected radiomics features and deep learning features, 9 small-sample classifiers were trained respectively to build and select the optimal IS subtyping classification model. RESULTS: The experimental data include CTA images of 82 IS patients diagnosed and treated in Shanghai Sixth People’s Hospital. The AUC value and accuracy of the optimal subtyping model based on the Adaboost classifier are 0.9018 and 0.8929, respectively. CONCLUSION: The experimental results show that the proposed method can effectively predict the subtype of IS and has potential to assist doctors in making timely and accurate diagnoses of IS patients. Show more

Keywords: Ischemic stroke, computed tomography angiography, radiomics, convolutional neural networks, subtyping model

DOI: 10.3233/XST-221284

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 223-235, 2023

Authors: Arai, Tomohiro | Murata, Syo | Watanabe, Yuichi | Ishihara, Toshihiro | Fukamizu, Yoshiya | Takeda, Satoshi | Ebata, Kiyokadzu | Watanabe, Yuki | Takashima, Yoshio | Kaneko, Junichi

Article Type: Research Article

Abstract: BACKGROUND: Radiological technologists serve as risk communicators who aim to lessen patients’ anxiety about radiation exposure, in addition to performing radiological examinations. OBJECTIVE: We conducted a fact-finding survey on knowledge and awareness of radiation disasters among the radiological technologists to reveal their literacy and competencies regarding radiation disasters. METHODS: A paper questionnaire was distributed to 1,835 radiological technologists at 166 National Hospital Organization facilities in Japan. The 28-item questionnaire covered knowledge and awareness of radiation protection and radiation disasters. Radiological technologists were divided into 2 groups by regionality: areas where a nuclear power station was present/nearby …(NPS areas) and non-NPS areas. RESULTS: Completed questionnaires were returned from 148 facilities with a facility response rate of 89.2% and from 1,391 radiological technologists with a response rate of 75.8%. There were 1,290 valid responses with a valid response rate of 70.3%. The correct answer rate for knowledge of radiation protection and radiation disasters was high in the 24 NPS areas. There were no differences in awareness of radiation disasters between NPS and non-NPS areas. CONCLUSIONS: Establishing a nationwide, region-independent training system can be expected to improve literacy regarding radiation disasters among radiological technologists. Willingness to assist during disasters was high among radiological technologists irrespective of area, indicating that the competencies of radiological technologists represent a competency model for radiation disaster assistance. Show more

Keywords: Radiological technologist, risk communicator, radiation disaster

DOI: 10.3233/XST-221341

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 237-245, 2023

Authors: Zhang, Jiwen | Zhang, Zhongsheng | Mao, Ning | Zhang, Haicheng | Gao, Jing | Wang, Bin | Ren, Jianlin | Liu, Xin | Zhang, Binyue | Dou, Tingyao | Li, Wenjuan | Wang, Yanhong | Jia, Hongyan

Article Type: Research Article

Abstract: OBJECTIVES: This study aims to develop and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer. METHODS: This retrospective study included 263 patients with histologically proven invasive breast cancer and who underwent DCE-MRI examination before surgery in two hospitals. All patients had a defined ALN status based on pathological examination results. Regions of interest (ROIs) of the primary tumor and ipsilateral ALN were manually drawn. A total of 1,409 radiomics features were initially computed from each ROI. Next, the low variance threshold, SelectKBest, and …least absolute shrinkage and selection operator (LASSO) algorithms were used to extract the radiomics features. The selected radiomics features were used to establish the radiomics signature of the primary tumor and ALN. A radiomics nomogram model, including the radiomics signature and the independent clinical risk factors, was then constructed. The predictive performance was evaluated by the receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA) by using the training and testing sets. RESULTS: ALNM rates of the training, internal testing, and external testing sets were 43.6%, 44.3% and 32.3%, respectively. The nomogram, including clinical risk factors (tumor diameter) and radiomics signature of the primary tumor and ALN, showed good calibration and discrimination with areas under the ROC curves of 0.884, 0.822, and 0.813 in the training, internal and external testing sets, respectively. DCA also showed that radiomics nomogram displayed better clinical predictive usefulness than the clinical or radiomics signature alone. CONCLUSIONS: The radiomics nomogram combined with clinical risk factors and DCE-MRI-based radiomics signature may be used to predict ALN metastasis in a noninvasive manner. Show more

Keywords: Breast cancer, Lymph node, Radiomics, Nomogram, Magnetic resonance imaging.

DOI: 10.3233/XST-221336

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 247-263, 2023

Authors: Xiong, Shan | Hu, Hai | Liu, Sibin | Huang, Yuanyi | Cheng, Jianmin | Wan, Bing

Article Type: Research Article

Abstract: OBJECTIVE: To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department. METHODS: Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during …the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3. RESULTS: The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p  <  0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p  >  0.017). Radiologist I’s FPS increased significantly in P2 compared to P1 (p  <  0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p  >  0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p  >  0.05). CONCLUSIONS: DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures. Show more

Keywords: Rib fractures, deep learning, convolutional neural network, tomography, X-ray computed

DOI: 10.3233/XST-221343

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 265-276, 2023

Authors: Shi, Jun | Li, Miao | Zhao, Yuxin | Xiao, Shali

Article Type: Research Article

Abstract: X-ray optics with good focusing ability and high spectral resolution are required in X-ray spectroscopy for the diagnosis of high temperature and density plasmas. In our study, a novel X-ray spectrometer is developed to provide the ability to record spectra with excellent focusing performance and high energy resolution. It is accomplished by using a continuously conical crystal (CCC) that is formed by circles with different curvatures. In this paper, we present the foundational work of the design and development of continuously conical crystal spectrometer (CCCS) along with initial results obtained with a titanium (Ti) target as the object source. First, …the spectrometer based on such a continuously conical crystal is used to measure X-ray spectra on Ti target X-ray Tube device. The spectral resolution (λ/Δλ) is around 615 with the source size of 1 mm. Then, we test the capability of the spectrometer on Xingguang-III Laser Facility with Ti target. He-like and Li-like Ti lines are recorded based on which the spectrometer performance is evaluated. The experiment result shows that the spectrometer provides a high spectral resolving power up to 1000, while acquiring a one-dimensional image of the source. Show more

Keywords: Plasmas, X-ray, crystal spectrometer, continuous cone

DOI: 10.3233/XST-221259

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 277-284, 2023

Authors: García-Esparza, A.M. | Garnica-Garza, H.M.

Article Type: Research Article

Abstract: BACKGROUND: Molecular breast imaging uses Tc-99 m sestamibi to obtain functional images of the breast. Determining the Tc-99 m sestamibi uptake in volumes of interest in the breast may be useful in assessing the response to neoadjuvant chemotherapy or for the purposes of breast cancer risk assessment. PURPOSE: To determine, using Monte Carlo simulation, if emission tomography can be used to quantify the uptake of Tc-99 m sestamibi in molecular breast imaging and if so, to determine the accuracy as a function of the number of projections used in the reconstruction process. METHODS: In this study, two voxelized breast …models are implemented with different ratios of fibroglandular to fatty tissue and tumoral masses of varying dimensions. Monte Carlo simulation is used to calculate sets of projections, which assumes that each tumoral mass contains a given Tc-99 m activity. Projections are also calculated for a calibration phantom in order to correlate the known activity with the image pixel value. For each case, the total number of calculated projections is 36 and the reconstruction is carried out for 36, 18, 9, 7 and 5 projections, respectively, using an open source image reconstruction toolbox. RESULTS: Study data show that determination of Tc-99 m sestamibi uptake with and average error of 7% can be carried out with as little as 7 projections. CONCLUSIONS: Molecular breast emission tomography enables to accurately determine the Tc-99 m sestamibi tumoral mass uptake with the number of projections very close to the number of images currently acquired in clinical practice. Show more

Keywords: Molecular breast tomography, Monte Carlo simulation, breast cancer

DOI: 10.3233/XST-221303

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 285-299, 2023

Authors: Han, Liying | Li, Fugai | Yu, Hengyong | Xia, Kewen | Xin, Qiyuan | Zou, Xiaoyu

Article Type: Research Article

Abstract: BACKGROUND: Lung cancer has the second highest cancer mortality rate in the world today. Although lung cancer screening using CT images is a common way for early lung cancer detection, accurately detecting lung nodules remains a challenged issue in clinical practice. OBJECTIVE: This study aims to develop a new weighted bidirectional recursive pyramid algorithm to address the problems of small size of lung nodules, large proportion of background region, and complex lung structures in lung nodule detection of CT images. METHODS: First, the weighted bidirectional recursive feature pyramid network (BiPRN) is proposed, which can increase the …ability of network model to extract feature information and achieve multi-scale fusion information. Second, a CBAM_CSPDarknet53 structure is developed to incorporate an attention mechanism as a feature extraction module, which can aggregate both spatial information and channel information of the feature map. Third, the weighted BiRPN and CBAM_CSPDarknet53 are applied to the YOLOvX model for lung nodule detection experiments, named BiRPN-YOLOvX, where YOLOvX represents different versions of YOLO. To verify the effectiveness of our weighted BiRPN and CBAM_ CSPDarknet53 algorithm, they are fused with different models of YOLOv3, YOLOv4 and YOLOv5, and extensive experiments are carried out using the publicly available lung nodule datasets LUNA16 and LIDC-IDRI. The training set of LUNA16 contains 949 images, and the validation and testing sets each contain 118 images. There are 1987, 248 and 248 images in LIDC-IDRI’s training, validation and testing sets, respectively. RESULTS: The sensitivity of lung nodule detection using BiRPN-YOLOv5 reaches 98.7% on LUNA16 and 96.2% on LIDC-IDRI, respectively. CONCLUSION: This study demonstrates that the proposed new method has potential to help improve the sensitivity of lung nodule detection in future clinical practice. Show more

Keywords: Multiscale fusion, weighted bidirectional recursive feature pyramid, CBAM_CSPDarknet53, YOLOvX, lung nodule detection

DOI: 10.3233/XST-221310

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 301-317, 2023

Authors: Zhong, Xinyi | Liang, Ningning | Cai, Ailong | Yu, Xiaohuan | Li, Lei | Yan, Bin

Article Type: Research Article

Abstract: BACKGROUND: Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work. OBJECTIVE: This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior. METHODS: The new method reconstructs CT images through a reconstruction model incorporating image gradient L 0 -norm minimization and deep image priors using a plug-and-play super-resolution framework. The deep learning priors are learned …from a deep residual network and then plugged into the proposed new framework, and alternating direction method of multipliers is utilized to optimize the iterative solution of the model. RESULTS: The simulation data analysis results show that the new method improves the signal-to-noise ratio (PSNR) by 7% and the modulation transfer function (MTF) curves show that the value of MTF50 increases by 0.02 factors compared with the result of deep plug-and-play super-resolution. Additionally, the real CT image data analysis results show that the new method improves the PSNR by 5.1% and MTF50 by 0.11 factors. CONCLUSION: Both simulation and real data experiments prove that the proposed new CT super-resolution method using deep learning priors can reconstruct CT images with lower noise and better detail recovery. This method is flexible, effective and extensive for low-resolution CT image super-resolution. Show more

Keywords: CT, image reconstruction, super-resolution, sparsity regularization, deep learning prior, plug-and-play. alternating direction method of multipliers

DOI: 10.3233/XST-221299

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 319-336, 2023

Authors: Tan, Zhengbo | Lin, Jiangli | Chen, Ke | Zhuang, Yan | Han, Lin

Article Type: Research Article

Abstract: BACKGROUND: Melanoma is a tumor caused by melanocytes with a high degree of malignancy, easy local recurrence, distant metastasis, and poor prognosis. It is also difficult to be detected by inexperienced dermatologist due to their similar appearances, such as color, shape, and contour. OBJECTIVE: To develop and test a new computer-aided diagnosis scheme to detect melanoma skin cancer. METHODS: In this new scheme, the unsupervised clustering based on deep metric learning is first conducted to make images with high similarity together and the corresponding model weights are utilized as teacher-model for the next stage. Second, benefit …from the knowledge distillation, the attention transfer is adopted to make the classification model enable to learn the similarity features and information of categories simultaneously which improve the diagnosis accuracy than the common classification method. RESULTS: In validation sets, 8 categories were included, and 2443 samples were calculated. The highest accuracy of the new scheme is 0.7253, which is 5% points higher than the baseline (0.6794). Specifically, the F1-Score of three malignant lesions BCC (Basal cell carcinoma), SCC (Squamous cell carcinomas), and MEL (Melanoma) increase from 0.65 to 0.73, 0.28 to 0.37, and 0.54 to 0.58, respectively. In two test sets of HAN including 3844 samples and BCN including 6375 samples, the highest accuracies are 0.68 and 0.53 for HAM and BCN datasets, respectively, which are higher than the baseline (0.649 and 0.516). Additionally, F1 scores of BCC, SCC, MEL are 0.49, 0.2, 0.45 in HAM dataset and 0.6, 0.14, 0.55 in BCN dataset, respectively, which are also higher than F1 scores the results of baseline. CONCLUSIONS: This study demonstrates that the similarity clustering method enables to extract the related feature information to gather similar images together. Moreover, based on the attention transfer, the proposed classification framework can improve total accuracy and F1-score of skin lesion diagnosis. Show more

Keywords: Skin cancer, melanoma, similarity clustering, attention transfer, deep metric learning

DOI: 10.3233/XST-221333

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 337-355, 2023

Authors: Su, Xuan | Zhang, Huan | Wang, Yuanjun

Article Type: Research Article

Abstract: BACKGROUND: Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE: The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS: This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic …features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t -test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS: Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS: Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome. Show more

Keywords: Colorectal liver metastases, chemotherapy, radiomics, Apparent diffusion coefficient (ADC) value, longitudinal data

DOI: 10.3233/XST-221317

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 357-372, 2023