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Price: EUR 160.00Authors: Hsieh, Shang-Ting | Cheng, Ya-Ai
Article Type: Research Article
Abstract: BACKGROUND: Dental health issues are on the rise, necessitating prompt and precise diagnosis. Automated dental condition classification can support this need. OBJECTIVE: The study aims to evaluate the effectiveness of deep learning methods and multimodal feature fusion techniques in advancing the field of automated dental condition classification. METHODS AND MATERIALS: A dataset of 11,653 clinically sourced images representing six prevalent dental conditions—caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia—was utilized. Features were extracted using five Convolutional Neural Network (CNN) models, then fused into a matrix. Classification models were constructed using Support Vector Machines …(SVM) and Naive Bayes classifiers. Evaluation metrics included accuracy, recall rate, precision, and Kappa index. RESULTS: The SVM classifier integrated with feature fusion demonstrated superior performance with a Kappa index of 0.909 and accuracy of 0.925. This significantly surpassed individual CNN models such as EfficientNetB0, which achieved a Kappa of 0.814 and accuracy of 0.847. CONCLUSIONS: The amalgamation of feature fusion with advanced machine learning algorithms can significantly bolster the precision and robustness of dental condition classification systems. Such a method presents a valuable tool for dental professionals, facilitating enhanced diagnostic accuracy and subsequently improved patient outcomes. Show more
Keywords: Dental conditions, deep learning, convolutional neural network, multimodal feature fusion, SVM
DOI: 10.3233/XST-230271
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Gao, Kai | Ma, Ze-Peng | Zhang, Tian-Le | Liu, Yi-Wen | Zhao, Yong-Xia
Article Type: Research Article
Abstract: PURPOSE: To compare image quality, iodine intake, and radiation dose in overweight and obese patients undergoing abdominal computed tomography (CT) enhancement using different scanning modes and contrast medium. METHODS: Ninety overweight and obese patients (25 kg/m2 ≤body mass index (BMI)< 30 kg/m2 and BMI≥30 kg/m2 ) who underwent abdominal CT-enhanced examinations were randomized into three groups (A, B, and C) of 30 each and scanned using gemstone spectral imaging (GSI) +320 mgI/ml, 100 kVp + 370 mgI/ml, and 120 kVp + 370 mgI/ml, respectively. Reconstruct monochromatic energy images of group A at 50–70 keV (5 keV interval). The iodine intake and radiation dose of each …group were recorded and calculated. The CT values, contrast-to-noise ratios (CNRs), and subjective scores of each subgroup image in group A versus images in groups B and C were by using one-way analysis of variance or Kruskal–Wallis H test, and the optimal keV of group A was selected. RESULTS: The dual-phase CT values and CNRs of each part in group A were higher than or similar to those in groups B and C at 50–60 keV, and similar to or lower than those in groups B and C at 65 keV and 70 keV. The subjective scores of the dual-phase images in group A were lower than those of groups B and C at 50 keV and 55 keV, whereas no significant difference was seen at 60–70 keV. Compared to groups B and C, the iodine intake in group A decreased by 12.5% and 13.3%, respectively. The effective doses in groups A and B were 24.7% and 25.8% lower than those in group C, respectively. CONCLUSION: GSI +320 mgI/ml for abdominal CT-enhanced in overweight patients satisfies image quality while reducing iodine intake and radiation dose, and the optimal keV was 60 keV. Show more
Keywords: Abdominal CT enhancement, gemstone spectral imaging, radiation dose, low tube voltage, low-concentration contrast medium
DOI: 10.3233/XST-230327
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Duan, Yanhua | Feng, Aihui | Wang, Hao | Chen, Hua | Gu, Hengle | Shao, Yan | Huang, Ying | Shen, Zhenjiong | Kong, Qing | Xu, Zhiyong
Article Type: Research Article
Abstract: Purpose: This study aims to assess the dosimetry and treatment efficiency of TaiChiB-based Stereotactic Body Radiotherapy (SBRT) plans applying to treat two-lung lesions with one overlapping organs at risk. Methods: For four retrospective patients diagnosed with two-lung lesions each patient, four treatment plans were designed including Plan Edge , TaiChiB linac-based, RGS-based, and a linac-RGS hybrid (Plan TCLinac , Plan TCRGS , and Plan TCHybrid ). Dosimetric metrics and beam-on time were employed to evaluate and compare the TaiChiB-based plans against Plan Edge . Results: For Conformity Index (CI), Plan TCRGS outperformed all other …plans with an average CI of 1.06, as opposed to Plan Edge′ s 1.33. Similarly, for R50 % , Plan TCRGS was superior with an average R50 % of 3.79, better than Plan Edge′ s 4.28. In terms of D2 cm , Plan TCRGS also led with an average of 48.48%, compared to Plan Edge′ s 56.25% . For organ at risk (OAR) sparing, Plan TCRGS often displayed the lowest dosimetric values, notably for the spinal cord (Dmax 5.92 Gy) and lungs (D1500cc 1.00 Gy, D1000cc 2.61 Gy, V10 Gy 15.14%). However, its high Dmax values for the heart and great vessels sometimes exceeded safety thresholds. Plan TCHybrid presented a balanced approach, showing doses comparable to or better than Plan Edge without crossing safety limits. In terms of beam-on time, Plan TCLinac emerged as the most efficient treatment option in three out of four cases, followed closely by Plan Edge in one case. Plan TCRGS , despite its dosimetric advantages, was the least efficient, recording notably longer beam-on times, with a peak at 33.28 minutes in Case 2. Conclusion: For patients with two-lung lesions treated by SBRT whose one lesion overlaps with OARs, the Plan TCHybrid delivered by TaiChiB digital radiotherapy system can be recommended as a clinical option. Show more
Keywords: SBRT, TaiChiB, two-target lung lesions, rotating gamma system, Edge linac, hybrid plan
DOI: 10.3233/XST-230176
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Alshamrani, Khalaf | Alshamrani, Hassan A.
Article Type: Research Article
Abstract: BACKGROUND: Digital X-ray imaging is essential for diagnosing osteoporosis, but distinguishing affected patients from healthy individuals using these images remains challenging. OBJECTIVE: This study introduces a novel method using deep learning to improve osteoporosis diagnosis from bone X-ray images. METHODS: A dataset of bone X-ray images was analyzed using a newly proposed procedure. This procedure involves segregating the images into regions of interest (ROI) and non-ROI, thereby reducing data redundancy. The images were then processed to enhance both spatial and statistical features. For classification, a Support Vector Machine (SVM) classifier was employed to …distinguish between osteoporotic and non-osteoporotic cases. RESULTS: The proposed method demonstrated a promising Area under the Curve (AUC) of 90.8% in diagnosing osteoporosis, benchmarking favorably against existing techniques. This signifies a high level of accuracy in distinguishing osteoporosis patients from healthy controls. CONCLUSIONS: The proposed method effectively distinguishes between osteoporotic and non-osteoporotic cases using bone X-ray images. By enhancing image features and employing SVM classification, the technique offers a promising tool for efficient and accurate osteoporosis diagnosis. Show more
Keywords: Lossless compression, classification, Bone Xray, ROI, patch size, osteoporosis
DOI: 10.3233/XST-230238
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Chen, Aoqiang | Chen, Xuemei | Jiang, Xiaobo | Wang, Yajuan | Chi, Feng | Xie, Dehuan | Zhou, Meijuan
Article Type: Research Article
Abstract: BACKGROUND: The study aimed to investigate anatomical changes in the neck region and their impact on dose distribution in patients with nasopharyngeal carcinoma (NPC) undergoing intensity modulated radiation therapy (IMRT), as well as to determine the optimal time for replanning during treatment. METHODS: Twenty NPC patients received IMRT with weekly pretreatment in-room kV fan beam computed tomography (FBCT) scans. Metastasized lymph nodes in the neck region and organs at risk (OARs) were recontoured based on the FBCT scans. The original treatment plan (PLAN0) was copied to each FBCT scan to create new plans of PLAN 1–6, correspondingly. The …dose-volume histograms (DVH) of the new plans and the original plan were compared. One-way repeated measure ANOVA was employed to define threshold(s) at any timepoint. The presence of a threshold(s) would indicate significant anatomical change(s) such that replanning should be suggested. RESULTS: Progressive volume reductions in the neck region, gross target volume for metastatic lymph nodes (GTVnd), submandibular glands, and parotids were observed over time. Compared to PLAN0, Dmean of GTVnd-L significantly increased in PLAN5, while the D95% of PGTVnd-L showed a significant decrease from PLAN3 to PLAN6. Similarly, the Dmean of GTVnd-R significantly increased from PLAN4 to PLAN6, whereas the D95% of PGTVnd-R exhibited a significant decrease from PLAN3 to PLAN6. Furthermore, a gradual increase in the dose delivered to the bilateral parotid glands, bilateral submandibular glands, brainstem, and spinal cord from PLAN0 to PLAN6. CONCLUSION: Significant anatomic and dosimetric changes were observed in the target volumes and OARs. Based on the identified thresholds, replanning at approximately 20 fractions is crucial to ensure adequate target volumes dose and avoid overdosing to the OARs. This approach is clinically feasible and strongly recommended, particularly for centers without access to an adaptive planning system. Show more
Keywords: Nasopharyngeal carcinoma, intensity-modulated radiation therapy, anatomical changes, replanning
DOI: 10.3233/XST-230280
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Li, Haoyan | Li, Zhentao | Gao, Shuaiyi | Hu, Jiaqi | Yang, Zhihao | Peng, Yun | Sun, Jihang
Article Type: Research Article
Abstract: OBJECTIVES: To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. METHODS: An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability …index (d’) were computed and compared among reconstructions. RESULTS: NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d’ than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d’ increased for acrylic insert, and d’ of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy. CONCLUSIONS: DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d’. Show more
Keywords: Multidetector computed tomography, image enhancement, image reconstruction, deep learning
DOI: 10.3233/XST-230333
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Musleh, Abdullah
Article Type: Research Article
Abstract: In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient’s condition is crucial in modern medicine since it determines whether or not the patient will receive the care they need. Data from a sinus CT scan is uploaded to a computer and displayed on a high-definition monitor to give the surgeon a clear anatomical orientation before endoscopic sinus surgery. In this study, a unique method is presented for detecting and diagnosing paranasal sinus disorders using machine learning. The researchers behind the current …study designed their own approach. To speed up diagnosis, one of the primary goals of our study is to create an algorithm that can accurately evaluate the paranasal sinuses in CT scans. The proposed technology makes it feasible to automatically cut down on the number of CT scan images that require investigators to manually search through them all. In addition, the approach offers an automatic segmentation that may be used to locate the paranasal sinus region and crop it accordingly. As a result, the suggested method dramatically reduces the amount of data that is necessary during the training phase. As a result, this results in an increase in the efficiency of the computer while retaining a high degree of performance accuracy. The suggested method not only successfully identifies sinus irregularities but also automatically executes the necessary segmentation without requiring any manual cropping. This eliminates the need for time-consuming and error-prone human labor. When tested with actual CT scans, the method in question was discovered to have an accuracy of 95.16 percent while retaining a sensitivity of 99.14 percent throughout. Show more
Keywords: Paranasal sinuses, endoscopic sinus surgery, accuracy, rmse, sensitivity, sinus irregularities, segmentation, and machine learning
DOI: 10.3233/XST-230284
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Shankarlal, B. | Dhivya, S. | Rajesh, K. | Ashok, S.
Article Type: Research Article
Abstract: BACKGROUND: Thyroid tumor is considered to be a very rare form of cancer. But recent researches and surveys highlight the fact that it is becoming prevalent these days because of various factors. OBJECTIVES: This paper proposes a novel hybrid classification system that is able to identify and classify the above said four different types of thyroid tumors using high end artificial intelligence techniques. The input data set is obtained from Digital Database of Thyroid Ultrasound Images through Kaggle repository and augmented for achieving a better classification performance using data warping mechanisms like flipping, rotation, cropping, scaling, and shifting. …METHODS: The input data after augmentation goes through preprocessing with the help of bilateral filter and is contrast enhanced using dynamic histogram equalization. The ultrasound images are then segmented using SegNet algorithm of convolutional neural network. The features needed for thyroid tumor classification are obtained from two different algorithms called CapsuleNet and EfficientNetB2 and both the features are fused together. This process of feature fusion is carried out to heighten the accuracy of classification. RESULTS: A Multilayer Perceptron Classifier is used for classification and Bonobo optimizer is employed for optimizing the results produced. The classification performance of the proposed model is weighted using metrics like accuracy, sensitivity, specificity, F1-score, and Matthew’s correlation coefficient. CONCLUSION: It can be observed from the results that the proposed multilayer perceptron based thyroid tumor type classification system works in an efficient manner than the existing classifiers like CANFES, Spatial Fuzzy C means, Deep Belief Networks, Thynet and Generative adversarial network and Long Short-Term memory. Show more
Keywords: Thyroid tumor, bilateral filter, dynamic histogram equalization, feature fusion, segnet, multilayer perceptron, capsulenet
DOI: 10.3233/XST-230430
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-25, 2024
Authors: Pérez, MartÍn | Lado, Gerardo M. | Mato, Germán | Franco, Diego G. | Vinciguerra, Ignacio Artola | Berisso, Mariano Gómez | Pomiro, Federico J. | Lipovetzky, José | Marpegan, Luciano
Article Type: Research Article
Abstract: An automated system for acquiring microscopic-resolution radiographic images of biological samples was developed. Mass-produced, low-cost, and easily automated components were used, such as Commercial-Off-The-Self CMOS image sensors (CIS), stepper motors, and control boards based on Arduino and RaspberryPi. System configuration, imaging protocols, and Image processing (filtering and stitching) were defined to obtain high-resolution images and for successful computational image reconstruction. Radiographic images were obtained for animal samples including the widely used animal models zebrafish (Danio rerio ) and the fruit-fly (Drosophila melanogaster ), as well as other small animal samples. The use of phosphotungstic acid (PTA) as a contrast agent …was also studied. Radiographic images with resolutions of up to (7±0.6)μm were obtained, making this system comparable to commercial ones. This work constitutes a starting point for the development of more complex systems such as X-ray attenuation micro-tomography systems based on low-cost off-the-shelf technology. It will also bring the possibility to expand the studies that can be carried out with small animal models at many institutions (mostly those working on tight budgets), particularly those on the effects of ionizing radiation and absorption of heavy metal contaminants in animal tissues. Show more
Keywords: Zebrafish, Drosophila, CMOS, image processing, image sensors, X-ray imaging
DOI: 10.3233/XST-230232
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2023
Authors: Jiang, Shi Bo | Sun, Yue Wen | Xu, Shuo | Zhang, Hua Xia | Wu, Zhi Fang
Article Type: Research Article
Abstract: Accurate segmentation of industrial CT images is of great significance in industrial fields such as quality inspection and defect analysis. However, reconstruction of industrial CT images often suffers from typical metal artifacts caused by factors like beam hardening, scattering, statistical noise, and partial volume effects. Traditional segmentation methods are difficult to achieve precise segmentation of CT images mainly due to the presence of these metal artifacts. Furthermore, acquiring paired CT image data required by fully supervised networks proves to be extremely challenging. To address these issues, this paper introduces an improved CycleGAN approach for achieving semi-supervised segmentation of industrial CT …images. This method not only eliminates the need for removing metal artifacts and noise, but also enables the direct conversion of metal artifact-contaminated images into segmented images without the requirement of paired data. The average values of quantitative assessment of image segmentation performance can reach 0.96645 for Dice Similarity Coefficient(Dice) and 0.93718 for Intersection over Union(IoU). In comparison to traditional segmentation methods, it presents significant improvements in both quantitative metrics and visual quality, provides valuable insights for further research. Show more
Keywords: Industrial CT, image segmentation, metal artifact, CycleGAN, dataset acquisition, semi-supervised
DOI: 10.3233/XST-230233
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: Wu, Panpan | Qu, Yue | Zhao, Ziping | Cui, Yue | Xu, Yurou | An, Peng | Yu, Hengyong
Article Type: Research Article
Abstract: Diabetic retinopathy (DR) is one of the leading causes of blindness. However, because the data distribution of classes is not always balanced, it is challenging for automated early DR detection using deep learning techniques. In this paper, we propose an adaptive weighted ensemble learning method for DR detection based on optical coherence tomography (OCT) images. Specifically, we develop an ensemble learning model based on three advanced deep learning models for higher performance. To better utilize the cues implied in these base models, a novel decision fusion scheme is proposed based on the Bayesian theory in terms of the key evaluation …indicators, to dynamically adjust the weighting distribution of base models to alleviate the negative effects potentially caused by the problem of unbalanced data size. Extensive experiments are performed on two public datasets to verify the effectiveness of the proposed method. A quadratic weighted kappa of 0.8487 and an accuracy of 0.9343 on the DRAC2022 dataset, and a quadratic weighted kappa of 0.9007 and an accuracy of 0.8956 on the APTOS2019 dataset are obtained, respectively. The results demonstrate that our method has the ability to enhance the ovearall performance of DR detection on OCT images. Show more
Keywords: Diabetic retinopathy, ensemble learning, decision fusion
DOI: 10.3233/XST-230252
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Tanveer, Md Sayed | Wiedeman, Christopher | Li, Mengzhou | Shi, Yongyi | De Man, Bruno | Maltz, Jonathan S. | Wang, Ge
Article Type: Research Article
Abstract: BACKGROUND: In recent years, deep reinforcement learning (RL) has been applied to various medical tasks and produced encouraging results. OBJECTIVE: In this paper, we demonstrate the feasibility of deep RL for denoising simulated deep-silicon photon-counting CT (PCCT) data in both full and interior scan modes. PCCT offers higher spatial and spectral resolution than conventional CT, requiring advanced denoising methods to suppress noise increase. METHODS: In this work, we apply a dueling double deep Q network (DDDQN) to denoise PCCT data for maximum contrast-to-noise ratio (CNR) and a multi-agent approach to handle data non-stationarity. RESULTS: …Using our method, we obtained significant image quality improvement for single-channel scans and consistent improvement for all three channels of multichannel scans. For the single-channel interior scans, the PSNR (dB) and SSIM increased from 33.4078 and 0.9165 to 37.4167 and 0.9790 respectively. For the multichannel interior scans, the channel-wise PSNR (dB) increased from 31.2348, 30.7114, and 30.4667 to 31.6182, 30.9783, and 30.8427 respectively. Similarly, the SSIM improved from 0.9415, 0.9445, and 0.9336 to 0.9504, 0.9493, and 0.0326 respectively. CONCLUSIONS: Our results show that the RL approach improves image quality effectively, efficiently, and consistently across multiple spectral channels and has great potential in clinical applications. Show more
Keywords: Photon-counting CT, deep-silicon detector, projection denoising, artificial intelligence, neural network, deep reinforcement learning, multi-agent learning
DOI: 10.3233/XST-230278
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-33, 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
Authors: Moock, Verena M. | Arce Chávez, Darien E. | García-Segundo, Crescencio | Ruiz-Huerta, Leopoldo
Article Type: Research Article
Abstract: BACKGROUND: The environmental impact on industrial X-ray tomography systems has gained its attention in terms of image precision and metrology over recent years, yet is still complex due to the variety of applications. OBJECTIVE: The current study explores the photothermal repercussions of the overall radiation exposure time. It shows the emerging dimensional uncertainty when measuring a stainless steel sphere by means of circular tomography scans. METHODS: The authors develop a novel frame difference method for X-ray radiographies to evaluate the spatial changes induced in the projected absorption maps on the X-ray panel. The …object of interest has a simple geometry for the purpose of proof of concept. The dominant source of the observed radial uncertainty is the photothermal effect due to high-energy X-ray scattering at the metal workpiece. Thermal variations are monitored by an infrared camera within the industrial tomography system, which confines that heat in the industrial grade X-ray system. RESULTS: The authors demonstrate that dense industrial computed tomography programs with major X-ray power notably affect the uncertainty of digital dimensional measurements. The registered temperature variations are consistent with dimensional changes in radiographies and hence form a source of error that might result in visible artifacts within the 3D image reconstruction. CONCLUSIONS: This contribution is of fundamental value to reach the balance between the number of projections and radial uncertainty tolerance when performing analysis with X-ray dimensional exploration in precision measurements with industrial tomography. Show more
Keywords: computed tomography, digital manufacturing, digital metrology, infrared imaging, thermal expansion, photothermal effect
DOI: 10.3233/XST-230260
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Cao, Kun | Gao, Fei | Long, Rong | Zhang, Fan-Dong | Huang, Chen-Cui | Cao, Min | Yu, Yi-Zhou | Sun, Ying-Shi
Article Type: Research Article
Abstract: PURPOSE: The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. METHODS: Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, …which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading. RESULTS: Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists’ reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy. CONCLUSIONS: The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists’ performance in predicting malignancy of suspicious breast calcifications. Show more
Keywords: Breast neoplasms, calcifications, machine learning, mammography, contrast media
DOI: 10.3233/XST-230332
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ren, Junru | Zhang, Wenkun | Wang, YiZhong | Liang, Ningning | Wang, Linyuan | Cai, Ailong | Wang, Shaoyu | Zheng, Zhizhong | Li, Lei | Yan, Bin
Article Type: Research Article
Abstract: Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter …scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses. Show more
Keywords: Dual-energy CT, dual quarter scans, limited-angle problem, characteristic analysis, anchor network
DOI: 10.3233/XST-230245
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
Authors: Lai, Yexin | Liu, Xueyu | Hou, Fan | Han, Zhiyong | E, Linning | Su, Ningling | Du, Dianrong | Wang, Zhichong | Zheng, Wen | Wu, Yongfei
Article Type: Research Article
Abstract: BACKGROUND: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability. OBJECTIVE: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD. METHODS: In detail, we first present a convolutional neural network that can segment and quantify five …types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions. RESULTS: Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation. CONCLUSIONS: In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians. Show more
Keywords: Interstitial lung disease, deep learning, lesion quantification, severity prediction
DOI: 10.3233/XST-230218
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Liu, Peng | Fang, Chenyun | Qiao, Zhiwei
Article Type: Research Article
Abstract: OBJECTIVE: CT image reconstruction from sparse-view projections is an important imaging configuration for low-dose CT, as it can reduce radiation dose. However, the CT images reconstructed from sparse-view projections by traditional analytic algorithms suffer from severe sparse artifacts. Therefore, it is of great value to develop advanced methods to suppress these artifacts. In this work, we aim to use a deep learning (DL)-based method to suppress sparse artifacts. METHODS: Inspired by the good performance of DenseNet and Transformer architecture in computer vision tasks, we propose a Dense U-shaped Transformer (D-U-Transformer) to suppress sparse artifacts. This architecture exploits the …advantages of densely connected convolutions in capturing local context and Transformer in modelling long-range dependencies, and applies channel attention to fusion features. Moreover, we design a dual-domain multi-loss function with learned weights for the optimization of the model to further improve image quality. RESULTS: Experimental results of our proposed D-U-Transformer yield performance improvements on the well-known Mayo Clinic LDCT dataset over several representative DL-based models in terms of artifact suppression and image feature preservation. Extensive internal ablation experiments demonstrate the effectiveness of the components in the proposed model for sparse-view computed tomography (SVCT) reconstruction. SIGNIFICANCE: The proposed method can effectively suppress sparse artifacts and achieve high-precision SVCT reconstruction, thus promoting clinical CT scanning towards low-dose radiation and high-quality imaging. The findings of this work can be applied to denoising and artifact removal tasks in CT and other medical images. Show more
Keywords: Computed tomography, sparse-view reconstruction, deep convolutional network, Transformer, multi-loss function
DOI: 10.3233/XST-230184
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Tawfik, Zyad A. | Farid, Mohamed El-Azab | Shahat, Khaled M. El | Hussein, Ahmed A. | Etreby, Mostafa Al
Article Type: Research Article
Abstract: BACKGROUND: SRS and SRT are precise treatments for brain metastases, delivering high doses while minimizing doses to nearby organs. Modern linear accelerators enable the precise delivery of SRS/SRT using different modalities like three-dimensional conformal radiotherapy (3DCRT), intensity-modulated radiotherapy (IMRT), and Rapid Arc (RA). OBJECTIVE: This study aims to compare dosimetric differences and evaluate the effectiveness of 3DCRT, IMRT, and Rapid Arc techniques in SRS/SRT for brain metastases. METHODS: 10 patients with brain metastases, 3 patients assigned for SRT, and 7 patients for SRS. For each patient, 3 treatment plans were generated using the …Eclipse treatment planning system using different treatment modalities. RESULTS: No statistically significant differences were observed among the three techniques in the homogeneity index (HI), maximum D2%, and minimum D98% doses for the target, with a p > 0.05. The RA demonstrated a better conformity index of 1.14±0.25 than both IMRT 1.21±0.26 and 3DCRT 1.37±0.31. 3DCRT and IMRT had lower Gradient Index values compared to RA, suggesting that they achieved a better dose gradient than RA. The mean treatment time decreased by 26.2% and 10.3% for 3DCRT and RA, respectively, compared to IMRT. In organs at risk, 3DCRT had lower maximum doses than IMRT and RA, but some differences were not statistically significant. However, in the brain stem and brain tissues, RA exhibited lower maximum doses compared to IMRT and 3DCRT. Additionally, RA and IMRT had lower V15Gy, V12Gy, and V9Gy values compared to 3DCRT. CONCLUSION: While 3D-CRT delivered lower doses to organs at risk, RA and IMRT provided better conformity and target coverage. RA effectively controlled the maximum dose and irradiated volume of normal brain tissue. Overall, these findings indicate that 3DCRT, RA, and IMRT are suitable for treating brain metastases in SRS/SRT due to their improved dose conformity and target coverage while minimizing dose to healthy tissues. Show more
Keywords: SRS, SRT, linac-based SRS, brain metastases, Rapid Arc, IMRT, 3DCRT
DOI: 10.3233/XST-230275
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 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: Chang, Jiahao | Zhu, Chaoyang | Song, Yuanpeng | Wang, Zhentao
Article Type: Research Article
Abstract: The time response characteristic of the detector is crucial in radiation imaging systems. Unfortunately, existing parallel plate ionization chamber detectors have a slow response time, which leads to blurry radiation images. To enhance imaging quality, the electrode structure of the detector must be modified to reduce the response time. This paper proposes a gas detector with a grid structure that has a fast response time. In this study, the detector electrostatic field was calculated using COMSOL, while Garfield++ was utilized to simulate the detector’s output signal. To validate the accuracy of simulation results, the experimental ionization chamber was tested on …the experimental platform. The results revealed that the average electric field intensity in the induced region of the grid detector was increased by at least 33% . The detector response time was reduced to 27% –38% of that of the parallel plate detector, while the sensitivity of the detector was only reduced by 10% . Therefore, incorporating a grid structure within the parallel plate detector can significantly improve the time response characteristics of the gas detector, providing an insight for future detector enhancements. Show more
Keywords: Time response characteristics, gas ionization chamber, garfield++, grid detector, detector sensitivity
DOI: 10.3233/XST-230219
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: 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: Zong, Fangke | Yang, Jun | Jiang, Jun | Guo, JinChuan
Article Type: Research Article
Abstract: In the X-ray single-grating imaging system, the acquisition of frequency information is the key step of phase-contrast and scattering information recovery. In the process of information extraction, it is easy to lead to the degradation of imaging quality due to the Moire Artifact, thus limiting the development and application of X-ray single-grating imaging system. In order to address the above problems, in this article, based on the theoretical analysis of the generation principle of Moire Artifact in imaging system, the advantages and disadvantages of grating rotation method are analyzed, and a method of suppressing Moire artifacts by adjusting grating projection …frequency is proposed. The experimental results show that the method proposed here can suppress the Moire noise in the background noise, resulting in a reduction of more than 50% in the standard deviation of the background noise. High quality phase-contrast and scattering images are obtained experimentally, which is of great value to the development of X-ray single-grating imaging technology. Show more
Keywords: X-ray optics, phase-contrast, X-ray imaging, moire artifacts, grating
DOI: 10.3233/XST-230202
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 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: Fu, Baoyue | Wei, Longyu | Wang, Chuanbin | Xiong, Baizhu | Bo, Juan | Jiang, Xueyan | Zhang, Yu | Jia, Haodong | Dong, Jiangning
Article Type: Research Article
Abstract: OBJECTIVE: To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). METHODS: Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, …and change rates of these were calculated. RESULTS: Multivariate Cox regression analysis showed Δ BMD, Δ SFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); Δ PMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms’ predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CONCLUSION: CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably. Show more
Keywords: Locally advanced cervical cancer, quantitative computer tomography-based body composition, survival
DOI: 10.3233/XST-230212
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-15, 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: Bai, Han | Song, Hui | Li, Qianyan | Bai, Jie | Wang, Ru | Liu, Xuhong | Chen, Feihu | Pan, Xiang
Article Type: Research Article
Abstract: OBJECTIVE: Try to create a dose gradient function (DGF) and test its effectiveness in reducing radiation induced lung injury in breast cancer radiotherapy. MATERIALS AND METHODS: Radiotherapy plans of 30 patients after breast-conserving surgery were included in the study. The dose gradient function was defined as D G H = V D V p 3 , then the area under the DGF curve of each plan was calculated in rectangular coordinate system, and the minimum area was used as the trigger factor, and other plans were triggered …to optimize for area reduction. The dosimetric parameters of target area and organs at risk in 30 cases before and after re-optimization were compared. RESULTS: On the premise of ensuring that the target dose met the clinical requirements, the trigger factor obtained based on DGF could further reduce the V5 , V10 , V20 , V30 and mean lung dose (MLD) of the ipsilateral lung in breast cancer radiotherapy, P < 0.01. And the D2cc and mean heart dose (MHD) of the heart were also reduced, P < 0.01. Besides, the NTCPs of the ipsilateral lung and the heart were also reduced, P < 0.01. CONCLUSION: The trigger factor obtained based on DGF is efficient in reducing radiation induced lung injury in breast cancer radiotherapy. Show more
Keywords: Radiation induced lung injury, dose gradient function, trigger factor, re-optimization, breast-conserving surgery
DOI: 10.3233/XST-230198
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Betshrine Rachel, R. | Nehemiah, H. Khanna | Singh, Vaibhav Kumar | Manoharan, Rebecca Mercy Victoria
Article Type: Research Article
Abstract: BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. METHODS: The lung tissues are segmented using Otsu’s thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test …sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier’s accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. RESULTS: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier’s results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall’s Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. CONCLUSION: The MLP classifier’s accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94% . Show more
Keywords: Covid-19, WOA, SVM, MLP, kendall’s correlation coefficient graph
DOI: 10.3233/XST-230196
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Li, Wenxiu | Gou, Fangfang | Wu, Jia
Article Type: Research Article
Abstract: BACKGROUND: In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE: This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS: The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer …enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS: The model’s performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS: The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information. Show more
Keywords: Breast cancer, cancer staging, auxiliary diagnosis, attention mechanism, convolutional neural network
DOI: 10.3233/XST-230194
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Kang, Yang | Wu, Rui | Li, Peizheng | Li, Qingpei | Wu, Sen | Tan, Tingting | Li, Yingrui | Zha, Gangqiang
Article Type: Research Article
Abstract: BACKGROUND: The gangue content in coal seriously affects the calorific value produced by its combustion. In practical applications, gangue in coal needs to be completely separated. The pseudo-dual-energy X-ray method does not have high sorting accuracy. OBJECTIVE: This study aims to propose a novel multi-dimensional coal and gangue X-ray sorting algorithm based on CdZnTe photon counting detectors to solve the problem of coal and gangue sorting by X-ray. METHODS: This complete algorithm includes five steps: (1) Preferred energy bins, (2) transmittance sorting, (3) one-dimensional R-value sorting, (4) two-dimensional R-value sorting, and (5) three-dimensional …R-value sorting. The output range of each step is determined by prior information from 65 groups of coal and gangue. An additional 110 groups of coal and gangue are employed experimentally to validate the algorithm’s accuracy. RESULTS: Compared with the 60% sorting accuracy of the Pseudo-dual-energy method, the new algorithm reached a sorting accuracy of 99% . CONCLUSIONS: Study results demonstrate the superiority of this novel algorithm and its feasibility in practical applications. This novel algorithm can guide other two-substance X-ray sorting applications based on photon counting detectors. Show more
Keywords: X-ray, sorting algorithm, photon counting, CdZnTe detector
DOI: 10.3233/XST-230250
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Huang, Ying | Pi, Yifei | Ma, Kui | Miao, Xiaojuan | Fu, Sichao | Feng, Aihui | Yanhua, Duan | Kong, Qing | Zhuo, Weihai | Xu, Zhiyong
Article Type: Research Article
Abstract: BACKGROUND: The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude. OBJECTIVE: The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet. METHODS: A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, …and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude. RESULTS: In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45–1.54, 0.58–0.90, 0.32–0.36, and 0.15–0.24; the mean absolute error (MAE) was 1.06–1.18, 0.32–0.78, 0.25–0.27, and 0.11–0.18, respectively, for COLL, MU, MLCR and MLCS. CONCLUSIONS: In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA. Show more
Keywords: Error magnitude prediction, ResNet, dose distribution, patient-specific QA
DOI: 10.3233/XST-230251
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-11, 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
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