Journal of X-Ray Science and Technology - Volume 32, issue 3

Authors: Yang, Yunfeng | Wang, Jiaqi

Article Type: Research Article

Abstract:  Breast cancer is one of the cancers with high morbidity and mortality in the world, which is a serious threat to the health of women. With the development of deep learning, the recognition about computer-aided diagnosis technology is getting higher and higher. And the traditional data feature extraction technology has been gradually replaced by the feature extraction technology based on convolutional neural network which helps to realize the automatic recognition and classification of pathological images. In this paper, a novel method based on deep learning and wavelet transform is proposed to classify the pathological images of breast cancer. Firstly, the …image flip technique is used to expand the data set, then the two-level wavelet decomposition and reconfiguration technology is used to sharpen and enhance the pathological images. Secondly, the processed data set is divided into the training set and the test set according to 8:2 and 7:3, and the YOLOv8 network model is selected to perform the eight classification tasks of breast cancer pathological images. Finally, the classification accuracy of the proposed method is compared with the classification accuracy obtained by YOLOv8 for the original BreaKHis dataset, and it is found that the algorithm can improve the classification accuracy of images with different magnifications, which proves the effectiveness of combining two-level wavelet decomposition and reconfiguration with YOLOv8 network model. Show more

Keywords: Breast cancer pathological image, image classification, deep learning, YOLOv8, wavelet transform

DOI: 10.3233/XST-230296

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 3, pp. 677-687, 2024

Authors: Zhang, Xin | Yang, Ping | Tian, Ji | Wen, Fan | Chen, Xi | Muhammad, Tayyab

Article Type: Research Article

Abstract: BACKGROUND: The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules. OBJECTIVE: In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules. METHODS: First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple …channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features. RESULTS: Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%. CONCLUSION: The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability. Show more

Keywords: Pulmonary nodule classification, Chest CT, Hybrid network, Attention mechanism

DOI: 10.3233/XST-230291

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 3, pp. 689-706, 2024

Authors: Yang, Sijing | Liang, Yongbo | Wu, Shang | Sun, Peng | Chen, Zhencheng

Article Type: Research Article

Abstract: Highlights • Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm’s feature learning ability for complex and diverse tumor morphology CT images. • Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion. • The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results. • The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets. …BACKGROUND: Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming. OBJECTIVE: This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors. METHOD: Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness. RESULTS: The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively. CONCLUSION: The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice. Show more

Keywords: Automatic segmentation, spatial attention mechanism, deep supervision, liver, liver tumors

DOI: 10.3233/XST-230312

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 3, pp. 707-723, 2024

Authors: Rawashdeh, Mohammad | Bani Yaseen, Abdel-Baset | McEntee, Mark | England, Andrew | Kumar, Praveen | Saade, Charbel

Article Type: Research Article

Abstract: BACKGROUND: To reduce radiation dose and subsequent risks, several legislative documents in different countries describe the need for Diagnostic Reference Levels (DRLs). Spinal radiography is a common and high-dose examination. Therefore, the aim of this work was to establish the DRL for Computed Tomography (CT) examinations of the spine in healthcare institutions across Jordan. METHODS: Data was retrieved from the picture archiving and communications system (PACS), which included the CT Dose Index (CTDI (vol)  ) and Dose Length Product (DLP). The median radiation dose values of the dosimetric indices were calculated for each site. DRL values were defined as …the 75th percentile distribution of the median CTDI (vol)  and DLP values. RESULTS: Data was collected from 659 CT examinations (316 cervical spine and 343 lumbar-sacral spine). Of the participants, 68% were males, and the patients’ mean weight was 69.7 kg (minimum = 60; maximum = 80, SD = 8.9). The 75th percentile for the DLP of cervical and LS-spine CT scans in Jordan were 565.2 and 967.7 mGy.cm, respectively. CONCLUSIONS: This research demonstrates a wide range of variability in CTDI (vol)  and DLP values for spinal CT examinations; these variations were associated with the acquisition protocol and highlight the need to optimize radiation dose in spinal CT examinations. Show more

Keywords: Diagnostic reference level, DRL, computed tomography, radiation dose, dose optimization

DOI: 10.3233/XST-230276

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 3, pp. 725-734, 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. 32, no. 3, pp. 735-749, 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. 32, no. 3, pp. 751-764, 2024

Authors: Tawfik, Zyad A. | Farid, Mohamed El-Azab | El Shahat, Khaled M. | Hussein, Ahmed A. | Al Etreby, Mostafa

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. 32, no. 3, pp. 765-781, 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 evaluate their impact on dose distribution in patients with nasopharyngeal carcinoma (NPC) undergoing intensity modulated radiation therapy (IMRT). Additionally, the study sought to determine the optimal time for replanning during the course of treatment. METHODS: Twenty patients diagnosed with NPC underwent IMRT, with weekly pretreatment kV fan beam computed tomography (FBCT) scans in the treatment room. Metastasized lymph nodes in the neck region and organs at risk (OARs) were redelineation using the images from the FBCT scans. Subsequently, the original treatment plan (PLAN0) was replicated …to each FBCT scan to generate new plans labeled as PLAN 1–6. The dose-volume histograms (DVH) of the new plans and the original plan were compared. One-way repeated measure ANOVA was utilized to establish threshold(s) at various time points. The presence of such threshold(s) would signify significant change(s), suggesting the need for replanning. RESULTS: Progressive volume reductions were observed over time in the neck region, the gross target volume for metastatic lymph nodes (GTVnd), as well as the submandibular glands and parotids. Compared to PLAN0, the mean dose (Dmean) of GTVnd-L significantly increased in PLAN5, while the minimum dose covering 95% of the volume (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 during the same period. Furthermore, the dose of bilateral parotid glands, bilateral submandibular glands, brainstem and spinal cord was gradually increased in the middle and late period of treatment. CONCLUSION: Significant anatomical and dosimetric changes were noted in both the target volumes and OARs. Considering the thresholds identified, it is imperative to undertake replanning at approximately 20 fractions. This measure ensures the delivery of adequate doses to target volumes while mitigating the risk of overdosing on OARs. 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. 32, no. 3, pp. 783-795, 2024

Authors: Huang, Ying | Pi, Yifei | Ma, Kui | Miao, Xiaojuan | Fu, Sichao | Feng, Aihui | Duan, Yanhua | 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. 32, no. 3, pp. 797-807, 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. 32, no. 3, pp. 809-822, 2024

Article Type: Other

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 3, pp. 823-823, 2024

Authors: Yu, Penghui | Li, Yanbing | Zhao, Qidong | Chen, Xia | Wu, Liqin | Jiang, Shuai | Rao, Libing | Rao, Yihua

Article Type: Research Article

Abstract: OBJECTIVE: In this study, the three-dimensional relationship between the optimal puncture needle path and the lumbar spinous process was discussed using digital technology. Additionally, the positioning guide plate was designed and 3D printed in order to simulate the surgical puncture of specimens. This plate served as an important reference for the preoperative simulation and clinical application of percutaneous laser decompression (PLD). METHOD: The CT data were imported into the Mimics program, the 3D model was rebuilt, the ideal puncture line N and the associated central axis M were developed, and the required data were measured. All of these …steps were completed. A total of five adult specimens were chosen for CT scanning; the data were imported into the Mimics program; positioning guide plates were generated and 3D printed; a simulated surgical puncture of the specimens was carried out; an X-ray inspection was carried out; and an analysis of the puncture accuracy was carried out. RESULTS: (1) The angle between line N and line M was 42°~55°, and the angles between the line M and 3D plane were 1°~2°, 5°~12°, and 78°~84°, respectively; (2) As the level of the lumbar intervertebral disc decreases, the distance from point to line and point to surface changes regularly; (3) The positioning guide was designed with the end of the lumbar spinous process and the posterior superior iliac spine on both sides as supporting points. (4) Five specimens were punctured 40 times by using the guide to simulate surgical puncture, and the success rate was 97.5%. CONCLUSION: By analyzing the three-dimensional relationship between the optimal puncture needle path and the lumbar spinous process, the guide plate was designed to simulate surgical puncture, and the individualized safety positioning of percutaneous puncture was obtained. Show more

Keywords: Discectomy, safety triangle, puncture channel, 3D printing, puncture guide

DOI: 10.3233/XST-230267

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 3, pp. 825-837, 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. 32, no. 3, pp. 839-855, 2024