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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
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