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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: 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
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