Journal of X-Ray Science and Technology - Volume 32, issue 4

Authors: Andriiashen, Vladyslav | van Liere, Robert | van Leeuwen, Tristan | Batenburg, Kees Joost

Article Type: Research Article

Abstract: BACKGROUND: X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust, and fast algorithms. Deep Convolutional Neural Networks (DCNNs) satisfy these requirements when a large amount of labeled data is available. To overcome the challenge of collecting these data, different methods of X-ray image generation are considered. OBJECTIVE: Depending on the desired degree of similarity to real data, different physical effects should either be simulated or can be ignored. X-ray scattering is known to be computationally expensive to simulate, and this effect …can greatly affect the accuracy of a generated X-ray image. We aim to quantitatively evaluate the effect of scattering on defect detection. METHODS: Monte-Carlo simulation is used to generate X-ray scattering distribution. DCNNs are trained on the data with and without scattering and applied to the same test datasets. Probability of Detection (POD) curves are computed to compare their performance, characterized by the size of the smallest detectable defect. RESULTS: We apply the methodology to a model problem of defect detection in cylinders. When trained on data without scattering, DCNNs reliably detect defects larger than 1.3 mm, and using data with scattering improves performance by less than 5%. If the analysis is performed on the cases with large scattering-to-primary ratio (1 < SPR  < 5), the difference in performance could reach 15% (approx. 0.4 mm). CONCLUSION: Excluding the scattering signal from the training data has the largest effect on the smallest detectable defects, and the difference decreases for larger defects. The scattering-to-primary ratio has a significant effect on detection performance and the required accuracy of data generation. Show more

Keywords: X-ray imaging, X-ray data generation, X-ray scattering, deep learning, in-line inspection

DOI: 10.3233/XST-230389

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1099-1119, 2024

Authors: Ali, Sajid | Ho, Chien-Yi | Yang, Chen-Chia | Chou, Szu-Hsien | Chen, Zhen-Ye | Huang, Wei-Chien | Shih, Tzu-Ching

Article Type: Research Article

Abstract: Cardiovascular disease (CVD), a global health concern, particularly coronary artery disease (CAD), poses a significant threat to well-being. Seeking safer and cost-effective diagnostic alternatives to invasive coronary angiography, noninvasive coronary computed tomography angiography (CCTA) gains prominence. This study employed OpenFOAM, an open-source Computational Fluid Dynamics (CFD) software, to analyze hemodynamic parameters in coronary arteries with serial stenoses. Patient-specific three-dimensional (3D) models from CCTA images offer insights into hemodynamic changes. OpenFOAM breaks away from traditional commercial software, validated against the FDA benchmark nozzle model for reliability. Applying this refined methodology to seventeen coronary arteries across nine patients, the study evaluates parameters …like fractional flow reserve computed tomography simulation (FFRCTS ), fluid velocity, and wall shear stress (WSS) over time. Findings include FFRCTS values exceeding 0.8 for grade 0 stenosis and falling below 0.5 for grade 5 stenosis. Central velocity remains nearly constant for grade 1 stenosis but increases 3.4-fold for grade 5 stenosis. This research innovates by utilizing OpenFOAM, departing from previous reliance on commercial software. Combining qualitative stenosis grading with quantitative FFRCTS and velocity measurements offers a more comprehensive assessment of coronary artery conditions. The study introduces 3D renderings of wall shear stress distribution across stenosis grades, providing an intuitive visualization of hemodynamic changes for valuable insights into coronary stenosis diagnosis. Show more

Keywords: Computational fluid dynamics (CFD), coronary computed tomography angiography, food and drug administration (FDA) benchmark nozzle model, fractional flow reserve (FFR)

DOI: 10.3233/XST-230239

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1121-1136, 2024

Authors: Chen, Gang | Zhang, Zehuan | Xu, Shuo | Jiang, Shibo | Liu, Ximing | Tang, Peng | Li, Songyuan | Xiang, Xincheng

Article Type: Research Article

Abstract: BACKGROUND: The polychromatic X-rays generated by a linear accelerator (Linac) often result in noticeable hardening artifacts in images, posing a significant challenge to accurate defect identification. To address this issue, a simple yet effective approach is to introduce filters at the radiation source outlet. However, current methods are often empirical, lacking scientifically sound metrics. OBJECTIVE: This study introduces an innovative filter design method that optimizes filter performance by balancing the impact of ray intensity and energy on image quality. MATERIALS AND METHODS: Firstly, different spectra under various materials and thicknesses of filters were obtained using GEometry …ANd Tracking (Geant4) simulation. Subsequently, these spectra and their corresponding incident photon counts were used as input sources to generate different reconstructed images. By comprehensively comparing the intensity differences and noise in images of defective and non-defective regions, along with considering hardening indicators, the optimal filter was determined. RESULTS: The optimized filter was applied to a Linac-based X-ray computed tomography (CT) detection system designed for identifying defects in graphite materials within high-temperature gas-cooled reactor (HTR), with defect dimensions of 2 mm. After adding the filter, the hardening effect reduced by 22%, and the Defect Contrast Index (DCI) reached 3.226. CONCLUSION: The filter designed based on the parameters of Average Difference (AD) and Defect Contrast Index (DCI) can effectively improve the quality of defect images. Show more

Keywords: Linear accelerator, hardening artifacts, defect identification, filter design

DOI: 10.3233/XST-240032

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1137-1150, 2024

Authors: Watanabe, Haruyuki | Ezawa, Yuina | Matsuyama, Eri | Kondo, Yohan | Hayashi, Norio | Maruyama, Sho | Ogura, Toshihiro | Shimosegawa, Masayuki

Article Type: Research Article

Abstract: BACKGROUND: Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic value are rejected, and retakes are required. However, determining the suitability of retaking radiographs is a qualitative evaluation. OBJECTIVE: To evaluate skull radiograph accuracy automatically using an unsupervised learning-based autoencoder (AE) and a variational autoencoder (VAE). In this study, we eliminated visual qualitative evaluation and used unsupervised learning to identify skull radiography retakes from the quantitative evaluation. METHODS: Five skull phantoms were imaged on radiographs, and 1,680 images were acquired. These images correspond …to two categories: normal images captured at appropriate positions and images captured at inappropriate positions. This study verified the discriminatory ability of skull radiographs using anomaly detection methods. RESULTS: The areas under the curves for AE and VAE were 0.7060 and 0.6707, respectively, in receiver operating characteristic analysis. Our proposed method showed a higher discrimination ability than those of previous studies which had an accuracy of 52%. CONCLUSIONS: Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations. Show more

Keywords: Anomaly detection, unsupervised learning, autoencoder, variational autoencoder, skull radiograph

DOI: 10.3233/XST-230431

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1151-1162, 2024

Authors: Gobo, M.S.S. | Balbin, D.R. | Hönnicke, M.G. | Poletti, M.E.

Article Type: Research Article

Abstract: BACKGROUND: Typical propagation-based X-ray phase contrast imaging (PB-PCI) experiments using polyenergetic sources are tested in very ideal conditions: low-energy spectrum (mainly characteristic X-rays), small thickness and homogeneous materials considered weakly absorbing objects, large object-to-detector distance, long exposure times and non-clinical detector. OBJECTIVE: Explore PB-PCI features using boundary conditions imposed by a low power polychromatic X-ray source (X-ray spectrum without characteristic X-rays), thick and heterogenous materials and a small area imaging detector with high low-detection radiation threshold, elements commonly found in a clinical scenario. METHODS: A PB-PCI setup implemented using a microfocus X-ray source and a dental …imaging detector was characterized in terms of different spectra and geometric parameters on the acquired images. Test phantoms containing fibers and homogeneous materials with close attenuation characteristics and animal bone and mixed soft tissues (bio-sample models) were analyzed. Contrast to Noise Ratio (CNR), system spatial resolution and Kerma values were obtained for all images. RESULTS: Phase contrast images showed CNR up to 15% higher than conventional contact images. Moreover, it is better seen when large magnifications (>3) and object-to-detector distances (>13 cm) were used. The influence of the spectrum was not appreciable due to the low efficiency of the detector (thin scintillator screen) at high energies. CONCLUSIONS: Despite the clinical boundary condition used in this work, regarding the X-ray spectrum, thick samples, and detection system, it was possible to acquire phase contrast images of biological samples. Show more

Keywords: Phase contrast imaging, propagation-based, edge-enhancement, optimization, digital dental detector, clinical application

DOI: 10.3233/XST-230425

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1163-1175, 2024

Authors: Lin, Yu-Fang | Hsieh, Chen-Hsi | Tien, Hui-Ju | Lee, Yi-Huan | Chen, Yi-Chun | Lai, Lu-Han | Hsu, Shih-Ming | Shueng, Pei-Wei

Article Type: Research Article

Abstract: BACKGROUND: The inherent problems in the existence of electron equilibrium and steep dose fall-off pose difficulties for small- and narrow-field dosimetry. OBJECTIVE: To investigate the cutout factors for keloid electron radiotherapy using various dosimetry detectors for small and narrow fields. METHOD: The measurements were performed in a solid water phantom with nine different cutout shapes. Five dosimetry detectors were used in the study: pinpoint 3D ionization chamber, Farmer chamber, semiflex chamber, Classic Markus parallel plate chamber, and EBT3 film. RESULTS: The results demonstrated good agreement between the semiflex and pinpoint chambers. Furthermore, there was …no difference between the Farmer and pinpoint chambers for large cutouts. For the EBT3 film, half of the cases had differences greater than 1%, and the maximum discrepancy compared with the reference chamber was greater than 2% for the narrow field. CONCLUSION: The parallel plate, semiflex chamber and EBT3 film are suitable dosimeters that are comparable with pinpoint 3D chambers in small and narrow electron fields. Notably, a semiflex chamber could be an alternative option to a pinpoint 3D chamber for cutout widths≥3 cm. It is very important to perform patient-specific cutout factor calibration with an appropriate dosimeter for keloid radiotherapy. Show more

Keywords: Cutout factor, electron beam, dosimetry detector, narrow field, keloid

DOI: 10.3233/XST-240059

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1177-1184, 2024

Authors: Shao, Wencheng | Lin, Xin | Huang, Ying | Qu, Liangyong | Zhuo, Weihai | Liu, Haikuan

Article Type: Research Article

Abstract: PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources. MATERIALS AND METHODS: We randomly selected the image data of 723 patients who underwent thoracic CT examinations. We performed auto-segmentation based on the selected data to generate the regions of interest (ROIs) of thoracic organs using the DeepViewer software. For each patient, radiomics features of the thoracic ROIs were extracted via the Pyradiomics package. The support vector regression (SVR) model was trained based on the radiomics features and reference organ …dose obtained by Monte Carlo (MC) simulation. The root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were evaluated. The robustness was verified by randomly assigning patients to the train and test sets of data and comparing regression metrics of different patient assignments. RESULTS: For the right lung, left lung, lungs, esophagus, heart, and trachea, results showed that the trained SVR model achieved the RMSEs of 2 mGy to 2.8 mGy on the test sets, 1.5 mGy to 2.5 mGy on the train sets. The calculated MAPE ranged from 0.1 to 0.18 on the test sets, and 0.08 to 0.15 on the train sets. The calculated R-squared was 0.75 to 0.89 on test sets. CONCLUSIONS: By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core. Show more

Keywords: Thoracic CT scanning, patient-specific modeling, radiation dosage, radiomics, support vector regression

DOI: 10.3233/XST-240015

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1185-1197, 2024

Authors: Huang, Ying | Cai, Ruxin | Pi, Yifei | Ma, Kui | Kong, Qing | Zhuo, Weihai | Kong, Yan

Article Type: Research Article

Abstract: OBJECTIVE: This study aims to explore the feasibility of DenseNet in the establishment of a three-dimensional (3D) gamma prediction model of IMRT based on the actual parameters recorded in the log files during delivery. METHODS: A total of 55 IMRT plans (including 367 fields) were randomly selected. The gamma analysis was performed using gamma criteria of 3% /3 mm (Dose Difference/Distance to Agreement), 3% /2 mm, 2% /3 mm, and 2% /2 mm with a 10% dose threshold. In addition, the log files that recorded the gantry angle, monitor units (MU), multi-leaf collimator (MLC), and jaws position during delivery were …collected. These log files were then converted to MU-weighted fluence maps as the input of DenseNet, gamma passing rates (GPRs) under four different gamma criteria as the output, and mean square errors (MSEs) as the loss function of this model. RESULTS: Under different gamma criteria, the accuracy of a 3D GPR prediction model decreased with the implementation of stricter gamma criteria. In the test set, the mean absolute error (MAE) of the prediction model under the gamma criteria of 3% /3 mm, 2% /3 mm, 3% /2 mm, and 2% /2 mm was 1.41, 1.44, 3.29, and 3.54, respectively; the root mean square error (RMSE) was 1.91, 1.85, 4.27, and 4.40, respectively; the Sr was 0.487, 0.554, 0.573, and 0.506, respectively. There was a correlation between predicted and measured GPRs (P  < 0.01). Additionally, there was no significant difference in the accuracy between the validation set and the test set. The accuracy in the high GPR group was high, and the MAE in the high GPR group was smaller than that in the low GPR group under four different gamma criteria. CONCLUSIONS: In this study, a 3D GPR prediction model of patient-specific QA using DenseNet was established based on log files. As an auxiliary tool for 3D dose verification in IMRT, this model is expected to improve the accuracy and efficiency of dose validation. Show more

Keywords: Three-dimensional gamma prediction, log files, patient-specific quality assurance

DOI: 10.3233/XST-230412

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1199-1208, 2024