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Price: EUR 160.00Authors: Rawashdeh, Mohammad | Bani Yaseen, Abdel-Baset | McEntee, Mark | England, Andrew | Kumar, Praveen | Saade, Charbel
Article Type: Research Article
Abstract: BACKGROUND: To reduce radiation dose and subsequent risks, several legislative documents in different countries describe the need for Diagnostic Reference Levels (DRLs). Spinal radiography is a common and high-dose examination. Therefore, the aim of this work was to establish the DRL for Computed Tomography (CT) examinations of the spine in healthcare institutions across Jordan. METHODS: Data was retrieved from the picture archiving and communications system (PACS), which included the CT Dose Index (CTDI (vol) ) and Dose Length Product (DLP). The median radiation dose values of the dosimetric indices were calculated for each site. DRL values were …defined as the 75th percentile distribution of the median CTDI (vol) and DLP values. RESULTS: Data was collected from 659 CT examinations (316 cervical spine and 343 lumbar-sacral spine). Of the participants, 68% were males, and the patients’ mean weight was 69.7 kg (minimum = 60; maximum = 80, SD = 8.9). The 75th percentile for the DLP of cervical and LS-spine CT scans in Jordan were 565.2 and 967.7 mGy.cm, respectively. CONCLUSIONS: This research demonstrates a wide range of variability in CTDI (vol) and DLP values for spinal CT examinations; these variations were associated with the acquisition protocol and highlight the need to optimize radiation dose in spinal CT examinations. Show more
Keywords: Diagnostic reference level, DRL, computed tomography, radiation dose, dose optimization
DOI: 10.3233/XST-230276
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Li, Zhiyuan | Liu, Yi | Zhang, Pengcheng | Lu, Jing | Gui, Zhiguo
Article Type: Research Article
Abstract: In the medical field, computed tomography (CT) is a commonly used examination method, but the radiation generated increases the risk of illness in patients. Therefore, low-dose scanning schemes have attracted attention, in which noise reduction is essential. We propose a purposeful and interpretable decomposition iterative network (DISN) for low-dose CT denoising. This method aims to make the network design interpretable and improve the fidelity of details, rather than blindly designing or using deep CNN architecture. The experiment is trained and tested on multiple data sets. The results show that the DISN method can restore the low-dose CT image structure and …improve the diagnostic performance when the image details are limited. Compared with other algorithms, DISN has better quantitative and visual performance, and has potential clinical application prospects. Show more
Keywords: LDCT, decomposition, image denoising, iteration, CNN
DOI: 10.3233/XST-230272
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Yang, Yunfeng | Wang, Jiaqi
Article Type: Research Article
Abstract: Breast cancer is one of the cancers with high morbidity and mortality in the world, which is a serious threat to the health of women. With the development of deep learning, the recognition about computer-aided diagnosis technology is getting higher and higher. And the traditional data feature extraction technology has been gradually replaced by the feature extraction technology based on convolutional neural network which helps to realize the automatic recognition and classification of pathological images. In this paper, a novel method based on deep learning and wavelet transform is proposed to classify the pathological images of breast cancer. Firstly, the …image flip technique is used to expand the data set, then the two-level wavelet decomposition and reconfiguration technology is used to sharpen and enhance the pathological images. Secondly, the processed data set is divided into the training set and the test set according to 8:2 and 7:3, and the YOLOv8 network model is selected to perform the eight classification tasks of breast cancer pathological images. Finally, the classification accuracy of the proposed method is compared with the classification accuracy obtained by YOLOv8 for the original BreaKHis dataset, and it is found that the algorithm can improve the classification accuracy of images with different magnifications, which proves the effectiveness of combining two-level wavelet decomposition and reconfiguration with YOLOv8 network model. Show more
Keywords: Breast cancer pathological image, image classification, deep learning, YOLOv8, wavelet transform
DOI: 10.3233/XST-230296
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Zhang, Xin | Yang, Ping | Tian, Ji | Wen, Fan | Chen, Xi | Muhammad, Tayyab
Article Type: Research Article
Abstract: BACKGROUND: The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules. OBJECTIVE: In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules. METHODS: First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple …channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features. RESULTS: Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26% . CONCLUSION: The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability. Show more
Keywords: Pulmonary nodule classification, Chest CT, Hybrid network, Attention mechanism
DOI: 10.3233/XST-230291
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Yang, Sijing | Liang, Yongbo | Wu, Shang | Sun, Peng | Chen, Zhencheng
Article Type: Research Article
Abstract: Highlights • Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm’s feature learning ability for complex and diverse tumor morphology CT images. • Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion. • The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results. • The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets. …BACKGROUND: Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming. OBJECTIVE: This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors. METHOD: Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness. RESULTS: The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively. CONCLUSION: The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice. Show more
Keywords: Automatic segmentation, spatial attention mechanism, deep supervision, liver, liver tumors
DOI: 10.3233/XST-230312
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Yu, Penghui | Li, Yanbing | Zhao, Qidong | Chen, Xia | Wu, Liqin | Jiang, Shuai | Rao, Libing | Rao, Yihua
Article Type: Research Article
Abstract: OBJECTIVE: In this study, the three-dimensional relationship between the optimal puncture needle path and the lumbar spinous process was discussed using digital technology. Additionally, the positioning guide plate was designed and 3D printed in order to simulate the surgical puncture of specimens. This plate served as an important reference for the preoperative simulation and clinical application of percutaneous laser decompression (PLD). METHOD: The CT data were imported into the Mimics program, the 3D model was rebuilt, the ideal puncture line N and the associated central axis M were developed, and the required data were measured. All of these …steps were completed. A total of five adult specimens were chosen for CT scanning; the data were imported into the Mimics program; positioning guide plates were generated and 3D printed; a simulated surgical puncture of the specimens was carried out; an X-ray inspection was carried out; and an analysis of the puncture accuracy was carried out. RESULTS: (1) The angle between line N and line M was 42°~55°, and the angles between the line M and 3D plane were 1°~2°, 5°~12°, and 78°~84°, respectively; (2) As the level of the lumbar intervertebral disc decreases, the distance from point to line and point to surface changes regularly; (3) The positioning guide was designed with the end of the lumbar spinous process and the posterior superior iliac spine on both sides as supporting points. (4) Five specimens were punctured 40 times by using the guide to simulate surgical puncture, and the success rate was 97.5% . CONCLUSION: By analyzing the three-dimensional relationship between the optimal puncture needle path and the lumbar spinous process, the guide plate was designed to simulate surgical puncture, and the individualized safety positioning of percutaneous puncture was obtained. Show more
Keywords: Discectomy, safety triangle, puncture channel, 3D printing, puncture guide
DOI: 10.3233/XST-230267
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Gopatoti, Anandbabu | Jayakumar, Ramya | Billa, Poornaiah | Patteeswaran, Vijayalakshmi
Article Type: Research Article
Abstract: BACKGROUND: COVID-19 needs to be diagnosed and staged to be treated accurately. However, prior studies’ diagnostic and staging abilities for COVID-19 infection needed to be improved. Therefore, new deep learning-based approaches are required to aid radiologists in detecting and quantifying COVID-19-related lung infections. OBJECTIVE: To develop deep learning-based models to classify and quantify COVID-19-related lung infections. METHODS: Initially, Dual Decoder Attention-based Semantic Segmentation Networks (DDA-SSNets) such as Dual Decoder Attention-UNet (DDA-UNet) and Dual Decoder Attention-SegNet (DDA-SegNet) are proposed to facilitate the dual segmentation tasks such as lung lobes and infection segmentation in chest …X-ray (CXR) images. The lung lobe and infection segmentations are mapped to grade the severity of COVID-19 infection in both the lungs of CXRs. Later, a Genetic algorithm-based Deep Convolutional Neural Network classifier with the optimum number of layers, namely GADCNet, is proposed to classify the extracted regions of interest (ROI) from the CXR lung lobes into COVID-19 and non-COVID-19. RESULTS: The DDA-SegNet shows better segmentation with an average BCSSDC of 99.53% and 99.97% for lung lobes and infection segmentations, respectively, compared with DDA-UNet with an average BCSSDC of 99.14% and 99.92%. The proposed DDA-SegNet with GADCNet classifier offered excellent classification results with an average BCCAC of 99.98%, followed by the GADCNet with DDA-UNet with an average BCCAC of 99.92% after extensive testing and analysis. CONCLUSIONS: The results show that the proposed DDA-SegNet has superior performance in the segmentation of lung lobes and COVID-19-infected regions in CXRs, along with improved severity grading compared to the DDA-UNet and improved accuracy of the GADCNet classifier in classifying the CXRs into COVID-19, and non-COVID-19. Show more
Keywords: X-ray images, semantic segmentation, lung lobe segmentation, infection segmentation, genetic algorithm
DOI: 10.3233/XST-230421
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
Authors: Shao, Wencheng | Lin, Xin | Huang, Ying | Qu, Liangyong | Weihai , Zhuo | Liu, Haikuan
Article Type: Research Article
Abstract: PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources. MATERIALS AND METHODS: We randomly selected the image data of 723 patients who underwent thoracic CT examinations. We performed auto-segmentation based on the selected data to generate the regions of interest (ROIs) of thoracic organs using the DeepViewer software. For each patient, radiomics features of the thoracic ROIs were extracted via the Pyradiomics package. The support vector regression (SVR) model was trained based on the radiomics features and reference organ …dose obtained by Monte Carlo (MC) simulation. The root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were evaluated. The robustness was verified by randomly assigning patients to the train and test sets of data and comparing regression metrics of different patient assignments. RESULTS: For the right lung, left lung, lungs, esophagus, heart, and trachea, results showed that the trained SVR model achieved the RMSEs of 2 mGy to 2.8 mGy on the test sets, 1.5 mGy to 2.5 mGy on the train sets. The calculated MAPE ranged from 0.1 to 0.18 on the test sets, and 0.08 to 0.15 on the train sets. The calculated R-squared was 0.75 to 0.89 on test sets. CONCLUSIONS: By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core. Show more
Keywords: Thoracic CT scanning, patient-specific modeling, radiation dosage, radiomics, support vector regression
DOI: 10.3233/XST-240015
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wang, Dehua | Jasim Taher, Hayder | Al-Fatlawi, Murtadha | Abdullah, Badr Ahmed | Khayatovna Ismailova, Munojat | Abedi-Firouzjah, Razzagh
Article Type: Research Article
Abstract: AIM: This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of LGE (LGE-4CH) in the axial plane. METHODS: After considering the inclusion and exclusion criteria, 115 patients (83 with MI diagnosis and 32 as healthy control patients), were included in the present study. Radiomic features were extracted from the whole left ventricular …myocardium (LVM). Feature selection methods were Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-Square (Chi2), Analysis of Variance (Anova), Recursive Feature Elimination (RFE), and SelectPersentile. The classification methods were Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). Different metrics, including receiver operating characteristic curve (AUC), accuracy, F1- score, precision, sensitivity, and specificity were calculated for radiomic features extracted from CMR images using stratified five-fold cross-validation. RESULTS: For the MI detection, Lasso (as the feature selection) and RF/LR (as the classifiers) in sBTFE sequences had the best performance (AUC: 0.97). All features and classifiers of T1 + sBTFE sequences with the weighted method (as the fused image), had a good performance (AUC: 0.97). In addition, the results of the evaluated metrics, especially mean AUC and accuracy for all models, determined that the T1 + sBTFE-weighted fused method had strong predictive performance (AUC: 0.93±0.05; accuracy: 0.93±0.04), followed by T1 + sBTFE-PCA fused method (AUC: 0.85±0.06; accuracy: 0.84±0.06). CONCLUSION: Our selected CMRI sequences demonstrated that radiomics analysis enables to detection of MI accurately. Among the investigated sequences, the T1 + sBTFE-weighted fused method with the highest AUC and accuracy values was chosen as the best technique for MI detection. Show more
Keywords: Myocardial infarction, cardiac magnetic resonance images, multi-parametric, tensor-based, radiomics feature, machine learning
DOI: 10.3233/XST-230307
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Liu, Bo | Haithem Zaki, Shaima | García, Eduardo | Bonilla, Amanda | Thabit, Daha | Hussein Adab, Aya
Article Type: Research Article
Abstract: OBJECTIVE: It seems that dose rate (DR) and photon beam energy (PBE) may influence the sensitivity and response of polymer gel dosimeters. In the current project, the sensitivity and response dependence of optimized PASSAG gel dosimeter (OPGD) on DR and PBE were assessed. MATERIALS AND METHODS: We fabricated the OPGD and the gel samples were irradiated with various DRs and PBEs. Then, the sensitivity and response (R 2 ) of OPGD were obtained by MRI at various doses and post-irradiation times. RESULTS: Our analysis showed that the sensitivity and response of OPGD are not affected …by the evaluated DRs and PBEs. It was also found that the dose resolution values of OPGD ranged from 9 to 33 cGy and 12 to 34 cGy for the evaluated DRs and PBEs, respectively. Additionally, the data demonstrated that the sensitivity and response dependence of OPGD on DR and PBE do not vary over various times after the irradiation. CONCLUSIONS: The findings of this research project revealed that the sensitivity and response dependence of OPGD are independent of DR and PBE. Show more
Keywords: Dose rate, optimized PASSAG, magnetic resonance imaging, photon beam energy, polymer gel dosimeter
DOI: 10.3233/XST-230282
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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