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Price: EUR 160.00Authors: 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: 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: 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: 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: Lu, Tianyu | Ma, Jianbing | Zou, Jiajun | Jiang, Chenxu | Li, Yangyang | Han, Jun
Article Type: Research Article
Abstract: BACKGROUND: The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration. OBJECTIVE: This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models. METHODS: We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n … = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated. RESULTS: Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776–0.907) and 0.955 (95% CI: 0.926–0.983) , and the AUCs of the validation cohort were 0.812 (95% CI: 0.677–0.948) and 0.893 (95% CI: 0.795–0.991) , respectively. The DTL signature was superior to the handcrafted radiomics signature. CONCLUSIONS: Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis. Show more
DOI: 10.3233/XST-230326
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 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: Gude, Zachary | Kapadia, Anuj J. | Greenberg, Joel A.
Article Type: Research Article
Abstract: BACKGROUND: A coded aperture X-ray diffraction (XRD) imaging system can measure the X-ray diffraction form factor from an object in three dimensions –X, Y and Z (depth), broadening the potential application of this technology. However, to optimize XRD systems for specific applications, it is critical to understand how to predict and quantify system performance for each use case. OBJECTIVE: The purpose of this work is to present and validate 3D spatial resolution models for XRD imaging systems with a detector-side coded aperture. METHODS: A fan beam coded aperture XRD system was used to scan 3D printed …resolution phantoms placed at various locations throughout the system’s field of view. The multiplexed scatter data were reconstructed using a model-based iterative reconstruction algorithm, and the resulting volumetric images were evaluated using multiple resolution criteria to compare against the known phantom resolution. We considered the full width at half max and Sparrow criterion as measures of the resolution and compared our results against analytical resolution models from the literature as well as a new theory for predicting the system resolution based on geometric arguments. RESULTS: We show that our experimental measurements are bounded by the multitude of theoretical resolution predictions, which accurately predict the observed trends and order of magnitude of the spatial and form factor resolutions. However, we find that the expected and observed resolution can vary by approximately a factor of two depending on the choice of metric and model considered. We observe depth resolutions of 7–16 mm and transverse resolutions of 0.6–2 mm for objects throughout the field of view. Furthermore, we observe tradeoffs between the spatial resolution and XRD form factor resolution as a function of sample location. CONCLUSION: The theories evaluated in this study provide a useful framework for estimating the 3D spatial resolution of a detector side coded aperture XRD imaging system. The assumptions and simplifications required by these theories can impact the overall accuracy of describing a particular system, but they also can add to the generalizability of their predictions. Furthermore, understanding the implications of the assumptions behind each theory can help predict performance, as shown by our data’s placement between the conservative and idealized theories, and better guide future systems for optimized designs. Show more
Keywords: X-ray diffraction, X-ray imaging, X-ray diffraction imaging, resolution
DOI: 10.3233/XST-230244
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-14, 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: Tai, Duong Thanh | Nhu, Nguyen Tan | Tuan, Pham Anh | Suleiman, Abdelmoneim | Omer, Hiba | Alirezaei, Zahra | Bradley, David | Chow, James C.L.
Article Type: Research Article
Abstract: BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis. METHODS: This study applies radiomics and deep learning in the diagnosis …of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit. RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful. CONCLUSIONS: The model provided means for storing and updating patients’ data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses. Show more
Keywords: Lung cancer, deep learning-based diagnosis, radiomics, computer-aided diagnosis
DOI: 10.3233/XST-230255
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Lu, Chang | Han, Zhenye | Zou, Jing
Article Type: Research Article
Abstract: BACKGROUND: Projection Domain Decomposition (PDD) is a dual energy reconstruction method which implements the decomposition process before image reconstruction. The advantage of PDD is that it can alleviate beam hardening artifacts and metal artifacts effectively as energy spectra estimation is considered in PDD. However, noise amplification occurs during the decomposition process, which significantly impacts the accuracy of effective atomic number and electron density. Therefore, effective noise reduction techniques are required in PDD. OBJECTIVE: This study aims to develop a new algorithm capable of minimizing noise while simultaneously preserving edges and fine details. METHODS: …In this study, a denoising algorithm based on low rank and similarity-based regularization (LRSBR) is presented. This algorithm incorporates the low-rank characteristic of tensors into similarity-based regularization (SBR) framework. This method effectively addresses the issue of instability in edge pixels within the SBR algorithm and enhances the structural consistency of dual-energy images. RESULTS: A series of simulation and practical experiments were conducted on a dual-layer dual-energy CT system. Experiments demonstrate that the proposed method outperforms exiting noise removal methods in Peak Signal-to-noise Ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity (SSIM). Meanwhile, there has been a notable enhancement in the visual quality of CT images. CONCLUSIONS: The proposed algorithm has a significantly improved noise reduction compared to other competing approach in dual-energy CT. Meanwhile, the LRSBR method exhibits outstanding performance in preserving edges and fine structures, making it practical for PDD applications. Show more
Keywords: Dual-energy computed tomography, projection domain decomposition, Low rank, similarity-based regularization
DOI: 10.3233/XST-230248
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Shi, Liu | Wei, Cunfeng | Jia, Tong | Zhao, Yunsong | Liu, Baodong
Article Type: Research Article
Abstract: BACKGROUND: The rapid development of industrialization in printed circuit board (PCB) warrants more complexity and integrity, which entails an essential procedure of PCB inspection. X-ray computed laminography (CL) enables inspection of arbitrary regions for large-sized flat objects with high resolution. PCB inspection based on CL imaging is worthy of exploration. OBJECTIVE: This work aims to extract PCB circuit layer information based on CL imaging through image segmentation technique. METHODS: In this work, an effective and applicable segmentation model for PCB CL images is established for the first time. The model comprises two components, …with one integrating edge diffusion and l 0 smoothing to filter CL images with aliasing artifacts, and the other being the fuzzy energy-based active contour model driven by local pre-fitting energy to segment the filtered images. RESULT: The proposed model is able to suppress aliasing artifacts in the PCB CL images and has good performance on images of different circuit layers. CONCLUSIONS: Results of the simulation experiment reveal that the method is capable of accurate segmentation under ideal scanning condition. Testing of different PCBs and comparison of different segmentation methods authenticate the applicability and superiority of the model. Show more
Keywords: PCB, CL, image segmentation, active contour model
DOI: 10.3233/XST-240006
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Zhao, Wei | Liu, Yi | Linghu, Xinyao | Zhang, Pengcheng | Yan, Hongxu | Ding, Xiaxu | Wang, Xiang | Gui, Zhiguo | Chen, Yan
Article Type: Research Article
Abstract: BACKGROUND: Recently, X-rays have been widely used to detect complex structural workpieces. Due to the uneven thickness of the workpiece and the high dynamic range of the X-ray image itself, the detailed internal structure of the workpiece cannot be clearly displayed. OBJECTIVE: To solve this problem, we propose an image enhancement algorithm based on a multi-scale local edge-preserving filter. METHODS: Firstly, the global brightness of the image is enhanced through logarithmic transformation. Then, to enhance the local contrast, we propose utilizing the gradient decay function based on fuzzy entropy to process the gradient and then incorporate …the gradient into the energy function of the local edge-preserving filter (LEP) as a constraint term. Finally, multiple base layers and detail layers are obtained through filtering multi-scale decomposition. All detail layers are enhanced and fused using S-curve mapping to improve contrast further. RESULTS: This method is competitive in both quantitative indices and visual perception quality. CONCLUSIONS: The experimental results demonstrate that the proposed method significantly enhances various complex workpieces and is highly efficient. Show more
Keywords: X-ray images, local edge-preserving filter, local fuzzy entropy, gradient domain compression, S-curve mapping
DOI: 10.3233/XST-240045
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Cui, Wei | Lv, Haipeng | Wang, Jiping | Zheng, Yanyan | Wu, Zhongyi | Zhao, Hui | Zheng, Jian | Li, Ming
Article Type: Research Article
Abstract: BACKGROUND: Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. OBJECTIVE: To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. METHODS: Specifically, we employ a feature-sharing encoder …to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. RESULTS: We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. CONCLUSIONS: In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts. Show more
Keywords: Photon counting CT, ring artifact suppression, feature shared multi-decoder network, complementary learning
DOI: 10.3233/XST-230396
Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
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