Journal of X-Ray Science and Technology - Volume 31, issue 5

Authors: Bilgin, Ezel Yaltırık | Ünal, Özkan | Göç, Muhammed Fatih | Bahsi, Taha

Article Type: Research Article

Abstract: BACKGROUND: The etiology, clinicopathological features, and prognosis of cancer in cases with EGFR mutations are different from those without mutations. OBJECTİVE: This study aims to evaluate the differences in ADC histogram analysis in brain metastases with EGFR mutation status in lung adenocarcinoma cases and the relationship between ADC histogram analysis differences and overall survival. METHODS: In this retrospective case-control study, 30 patients (8 EGFR+/22 EGFR-) and 51 brain metastases (15 EGFR+/36 EGFR-) were included. ROI markings are first performed from each section, including metastasis in ADC mapping using FIREVOXEL software. Next, ADC histogram parameters are calculated. …Overall survival analysis after brain metastasis (OSBM) is defined as the time from initial brain metastasis diagnosis to the time of death or last follow-up. Patient-based (by evaluating the largest lesion) and lesion-based (by evaluating all measurable lesions) statistical analyses are then performed. RESULTS: In the lesion-based analysis, skewness values are lower in EGFR+ patients, which is statistically significant (p  = 0.012). The two groups have no significant difference regarding other ADC histogram analysis parameters, mortality, and overall survival (p  > 0.05). In the ROC analysis, the most appropriate skewness cut-off value is determined as 0.321 to distinguish the EGFR mutation difference, and this value is statistically significant (sensitivity: 66.7%, specificity: 80.6%, AUC: 0.730) (p  = 0.006). CONCLUSİON: The findings of this study provide valuable insights into the differences in ADC histogram analysis according to EGFR mutation status in brain metastases due to lung adenocarcinoma. The identified parameters, especially skewness, are potentially non-invasive biomarkers for predicting mutation status. Incorporating these biomarkers into routine clinical practice may aid treatment decision-making and prognostic assessment for patients. Further validation studies and prospective investigations are warranted to confirm the clinical utility of these findings and establish their potential for personalized therapeutic strategies and patient outcomes. Show more

Keywords: ADC, histogram analysis, brain metastasis, Non-small cell lung cancer

DOI: 10.3233/XST-230084

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1035-1045, 2023

Authors: Shi, Yu | Li, Juntao | Li, Ke | Zhang, Xuexue | Chang, Peng | Huang, Zujian | Liu, Yanyun | Wang, Yihan | Zhan, Yonghua | Cao, Xu | Zhu, Shouping

Article Type: Research Article

Abstract: BACKGROUND: Micro-computed tomography is important in cardiac imaging for preclinical small animal models, but motion artifacts may appear due to the rapid heart rates. To avoid influence of motion artifacts, the prospective ECG gating schemes based on an X-ray source trigger have been investigated. However, due to the lack of pulsed X-ray exposure modes, high-resolution micro-focus X-ray sources do not support source triggering in most cases. OBJECTIVE: To develop a fast-cardiac multiphase acquisition strategy using prospective ECG gating for micro-focus X-ray tubes with a continuous emission mode. METHODS: The proposed detector-trigger-based prospective ECG gating acquisition scheme …(DTB-PG) triggers the X-ray detector at the R peak of ECG, and then collects multiple phase projections of the heart in one ECG cycle by sequence acquisition. Cardiac multiphase images are reconstructed after performing the same acquisition in all views. The feasibility of this strategy was verified in multiphase imaging experiments of a phantom with 150 ms motion period and a mouse heart on a micro-focus micro-CT system with continuous emission mode. RESULTS: Using a high frame-rate CMOS detector, DTB-PG discriminates the positions of the motion phantom well in 10 different phases and enables to distinguish the changes in the cardiac volume of the mouse in different phases. The acquisition rate of DTB-PG is much faster than other prospective gating schemes as demonstrated by theoretical analysis. CONCLUSIONS: DTB-PG combines the advantages of prospective ECG gating strategies and X-ray detector-trigger mode to suppress motion artifacts, achieve ultra-fast acquisition rates, and relax hardware limitations. Show more

Keywords: 4D micro-CT, multiple cardiac phases, ECG gating, detector trigger, sequence acquisition

DOI: 10.3233/XST-230034

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1047-1066, 2023

Authors: Xu, Junjie | Wang, Tong | Luo, Yu | Shang, Lintao | Mai, Xiuying | Ruan, Junjie | Pan, Xiaofen | Chi, Feng

Article Type: Research Article

Abstract: Background: There is no standardized registration frame of cone beam CT (CBCT) in intensity modulated radiotherapy (IMRT) for nasopharyngeal carcinoma (NPC). The overall registration frame that covers the whole head and neck is the most commonly used CBCT registration frame for NPC patients in IMRT. Objective: To compare the set-up errors using different registration frames of CBCT for NPC to assess the set-up errors for different region of the commonly used clinical overall registration frame. Methods: 294 CBCT images of 59 NPC patients were collected. Four registration frames were used for matching. The set-up errors were …obtained using an automatic matching algorithm and then compared. The expansion margin from the clinical target volume (CTV) to the planned target volume (PTV) in the four groups was also calculated. Results: The average range of the isocenter translation and rotation errors of four registration frames are 0.89∼2.41 mm and 0.49∼1.53°, respectively, which results in a significant difference in the set-up errors (p  < 0.05). The set-up errors obtained from the overall frame are smaller than those obtained from the head, upper neck, and lower neck frames. The margin ranges of the overall, head, upper neck, and lower neck frames in three translation directions are 1.49∼2.39 mm, 1.92∼2.45 mm, 1.86∼3.54 mm and 3.02∼4.78 mm, respectively. The expansion margins calculated from the overall frame are not enough, especially for the lower neck. Conclusion: Set-up errors of the neck are underestimated by the overall registration frame. Thus, it is important to improve the position immobilization of the neck, especially the lower neck. The margin of the target volume of the head and neck region should be expanded separately if circumstances permit. Show more

Keywords: Nasopharyngeal carcinoma, set-up error, registration frame, IMRT, CBCT

DOI: 10.3233/XST-230024

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1067-1077, 2023

Authors: Kondo, Tatsuya | Umezu, Manami | Kondo, Yohan | Sato, Mitsuru | Kanazawa, Tsutomu | Noto, Yoshiyuki

Article Type: Research Article

Abstract: BACKGROUND: Imaging examinations are crucial for diagnosing acute ischemic stroke, and knowledge of a patient’s body weight is necessary for safe examination. To perform examinations safely and rapidly, estimating body weight using head computed tomography (CT) scout images can be useful. OBJECTIVE: This study aims to develop a new method for estimating body weight using head CT scout images for contrast-enhanced CT examinations in patients with acute ischemic stroke. METHODS: This study investigates three weight estimation techniques. The first utilizes total pixel values from head CT scout images. The second one employs the Xception model, which …was trained using 216 images with leave-one-out cross-validation. The third one is an average of the first two estimates. Our primary focus is the weight estimated from this third new method. RESULTS: The third new method, an average of the first two weight estimation methods, demonstrates moderate accuracy with a 95% confidence interval of ±14.7 kg. The first method, using only total pixel values, has a wider interval of ±20.6 kg, while the second method, a deep learning approach, results in a 95% interval of ±16.3 kg. CONCLUSIONS: The presented new method is a potentially valuable support tool for medical staff, such as doctors and nurses, in estimating weight during emergency examinations for patients with acute conditions such as stroke when obtaining accurate weight measurements is not easily feasible. Show more

Keywords: Deep learning, computed tomography, scout images, body weight, and acute cerebral infarction

DOI: 10.3233/XST-230087

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1079-1091, 2023

Authors: Xu, Yuting | Wu, Zhifang | Zhang, Huaxia | Wang, Qiang

Article Type: Research Article

Abstract: BACKGROUND: X-ray imaging plays an important role in security inspection. However, the objects are complex, which makes it difficult to automatically detect prohibited and restricted objects. OBJECTIVE: This study aims to develop and test a detection method based on a new image segmentation scheme to solve the problem of detecting prohibited and restricted objects from pseudo-color X-ray images with complex backgrounds. METHODS: The internal mechanism of the influence of different color spaces on image segmentation effect is explored, and the color space component Hi is studied. Furthermore, the mechanism of the new Hi component …and the influence law of its adjustable coefficient are revealed. Additionally, a detection method based on Hi color space segmentation for pseudo-color X-ray images is proposed. The segmentation and detection methods are then tested on actual X-ray images. RESULTS: The results show that hue has the greatest influence on image segmentation effect of the pseudo-color X-ray images. For different pseudo-color X-ray images with complex backgrounds, applying the proposed new Hi color space segmentation method achieves overall accuracy of 0.974 and 1.0 in detecting the gun and knife, respectively. CONCLUSION: The new X-ray image detection method based on the Hi color space segmentation proposed in this paper enables to better solve the complex background problem including object overlap and adhesion and thus more effectively meet the requirements of actual security inspection. Show more

Keywords: X-ray images, image segmentation, complex background, Hi component, automatic detection

DOI: 10.3233/XST-230088

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1093-1114, 2023

Authors: Jo, Ajin | Kim, Eunhye

Article Type: Research Article

Abstract: BACKGROUND: Neonatal patients located in incubators are exposed to as many as 159 radiographs until discharge. To reduce the dose exposed to the patient, factors that may cause unnecessary exposure to the patient were judged. When conducting portable X-rays of neonatal patients located in an incubator, it is not easy to determine the exact field size because collimation light is exposed on the acrylic plate, an incubator canopy, and the resulting shadow is reflected on the patient’s body. OBJECTIVE: This study aims to measure the organ dose exposed to the patient according to the field size when a …portable radiograph is given to a neonatal patient in a neonatal intensive care unit (NICU) incubator. METHODS: To identify the absorbed organ dose depending on the radiation field size during portable X-ray examination of neonatal patient, a Monte Carlo N-Particle (MCNP) simulation, a SpeckCalc program, and a neonatal phantom from the ICRP 89 are applied for the calculation. According to the minimal field size (MinFS) standards of the European Commission (EC), the smaller field size is intended to measure tightly from the top of the lung apices to the bottom of the genitals; a larger field size is also calculated by adding 6 cm in width and length. RESULTS: Compared to the hospital C condition from the previous study, the larger and smaller field sizes are decreased by an average of 45% and 67%, respectively. Study results also show a 42% reduction in smaller field size compared to the larger field size. CONCLUSION: When taking chest and abdomen radiographic images of neonatal patients in incubators, appropriate field sizes are required to prevent inappropriate dose absorption for non-thoracic organs. Show more

Keywords: Incubator, neonatal patient, Monte Carlo simulation, organ-absorbed dose, field size

DOI: 10.3233/XST-230080

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1115-1124, 2023

Authors: ur Rehman, Aziz | Naseer, Asma | Karim, Saira | Tamoor, Maria | Naz, Samina

Article Type: Research Article

Abstract: Background: Computer aided diagnosis has gained momentum in the recent past. The advances in deep learning and availability of huge volumes of data along with increased computational capabilities has reshaped the diagnosis and prognosis procedures. Objective: These methods are proven to be relatively less expensive and safer alternatives of the otherwise traditional approaches. This study is focused on efficient diagnosis of three very common diseases: lung cancer, pneumonia and Covid-19 using X-ray images. Methods: Three different deep learning models are designed and developed to perform 4-way classification. Inception V3, Convolutional Neural Networks (CNN) and Long Short …Term Memory models (LSTM) are used as building blocks. The performance of these models is evaluated using three publicly available datasets, the first dataset contains images for Lung cancer, second contains images for Covid-19 and third dataset contains images for Pneumonia and normal subjects. Combining three datasets creates a class imbalance problem which is resolved using pre-processing and data augmentation techniques. After data augmentation 1386 subjects are randomly chosen for each class. Results: It is observed that CNN when combined with LSTM (CNN-LSTM) produces significantly improved results (accuracy of 94.5 %) which is better than CNN and InceptionV3-LSTM. 3,5, and 10 fold cross validation is performed to verify all results calculated using three different classifiers Conclusions: This research concludes that a single computer-aided diagnosis system can be developed for diagnosing multiple diseases. Show more

Keywords: Convolutional neural network, long short-term memory, lung cancer, Xray

DOI: 10.3233/XST-230113

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1125-1143, 2023

Authors: Arora, Saurabh | Gupta, Ruchir | Srivastava, Rajeev

Article Type: Research Article

Abstract: BACKGROUND: Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is required. OBJECTIVE: This study aims to develop and test a novel multi-fusion deep neural net consisting of encoder-decoder architecture for automatic and accurate teeth region segmentation from panoramic X-ray images. METHODS: The encoder has two different streams based on …CNN which include the conventional CNN stream and the Atrous net stream. Next, the fusion of features from these streams is done at each stage to encode the contextual rich information of teeth. A dual-type skip connection is then added between the encoder and decoder to minimise semantic information gaps. Last, the decoder comprises deconvolutional layers for reconstructing the segmented teeth map. RESULTS: The assessment of the proposed model is performed on two different dental datasets consisting of 1,500 and 1,000 panoramic X-ray images, respectively. The new model yields accuracy of 97.0% and 97.7%, intersection over union (IoU) score of 91.1% and 90.2%, and dice coefficient score (DCS) of 92.4% and 90.7% for datasets 1 and 2, respectively. CONCLUSION: Applying the proposed model to two datasets outperforms the recent state-of-the-art deep models with a relatively smaller number of parameters and higher accuracy, which demonstrates the potential of the new model to help dentists more accurately and efficiently diagnose dental diseases in future clinical practice. Show more

Keywords: Image segmentation, teeth segmentation, feature fusion, X-ray dental images

DOI: 10.3233/XST-230104

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1145-1161, 2023

Article Type: Retraction

DOI: 10.3233/XST-190469

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 5, pp. 1163-1163, 2023