Journal of X-Ray Science and Technology - Volume Pre-press, issue Pre-press

Authors: Li, Dongjie | Yuan, Shanliang | Yao, Gang

Article Type: Research Article

Abstract: BACKGROUND: Developing deep learning networks to classify between benign and malignant lung nodules usually requires many samples. Due to the precious nature of medical samples, it is difficult to obtain many samples. OBJECTIVE: To investigate and test a DCA-Xception network combined with a new data enhancement method to improve performance of lung nodule classification. METHODS: First, the Wasserstein Generative Adversarial Network (WGAN) with conditions and five data enhancement methods such as flipping, rotating, and adding Gaussian noise are used to extend the samples to solve the problems of unbalanced sample classification and the insufficient samples. Then, …a DCA-Xception network is designed to classify lung nodules. Using this network, information around the target is obtained by introducing an adaptive dual-channel feature extraction module, and the network learns features more accurately by introducing a convolutional attention module. The network is trained and validated using 274 lung nodules (154 benign and 120 malignant) and tested using 52 lung nodules (23 benign and 29 malignant). RESULTS: The experiments show that the network has an accuracy of 83.46% and an AUC of 0.929. The features extracted using this network achieve an accuracy of 85.24% on the K-nearest neighbor and random forest classifiers. CONCLUSION: This study demonstrates that the DCA-Xception network yields higher performance in classification of lung nodules than the performance using the classical classification networks as well as pre-trained networks. Show more

Keywords: Lung nodule classification, Wasserstein Generative Adversarial Networks (WGAN), Xception, convolutional attention module, classifier

DOI: 10.3233/XST-221219

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-16, 2022

Authors: Tao, Wei | Zhang, Zhouzhou | Zhang, Yuanyuan | Xu, Ming | Sun, Chuanyang

Article Type: Research Article

Abstract: OBJECTIVE: Life-threatening renal hemorrhage after flexible ureterorenoscopy and laser lithotripsy (FURSL) is a rare complication. We aim to review our unit’s experience with super-selective renal artery embolization as therapeutic options for such patients. METHODS: From January 2015 to November 2021, total 1125 patients underwent the FURSL procedures in our unit. Patients with life-threatening renal hemorrhage were reviewed and the information of peri-operative, operative and post-operative were recorded. RESULTS: Of the 1125 patients who underwent FURSL procedure, two patients with life-threatening renal hemorrhage were diagnosis; the age is 67 and 42 years old, respectively. Preoperative imaging examination …showed that two patients had upper ureteral stone and renal stone ranging in size from 1.2 to 3.0 cm. Female patient placed the D-J stent for two weeks before FURSL. After the operation, both patients had the massive gross hematuria, significant drop of hemoglobin (Hgb), blood pressure lowering and needed to transfusion. CT scan showed that the male patient had an intrarenal hematoma. All these two were treated by super-selective renal artery embolization and had a successful outcome. CONCLUSION: Life-threatening renal hemorrhage after FURSL is a rare and severe complication. Super-selective renal artery embolization is a safe and effective method for the treatment of patients with severe renal hemorrhage, preserving healthy renal parenchyma and renal function. Show more

Keywords: Renal hemorrhage, renal artery embolization, flexible ureterorenoscopy and laser lithotripsy (FURSL)

DOI: 10.3233/XST-221214

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-10, 2022

Authors: Gu, Yanan | Liu, Yi | Liu, Wenting | Yan, Rongbiao | Liu, Yuhang | Gui, Zhiguo

Article Type: Research Article

Abstract: OBJECTIVE: In order to solve the problem of image quality degradation of CT reconstruction under sparse angle projection, we propose to develop and test a new sparse angle CT reconstruction method based on group sparse. METHODS: In this method, the group-based sparse representation is introduced into the statistical iterative reconstruction framework as a regularization term to construct the objective function. The group-based sparse representation no longer takes a single patch as the minimum unit of sparse representation, while it uses Euclidean distance as a similarity measure, thus it divides similar patch into groups as basic units for sparse …representation. This method fully considers the local sparsity and non-local self-similarity of image. The proposed method is compared with several commonly used CT image reconstruction methods including FBP, SART, SART-TV and GSR-SART with experiments carried out on Sheep_Logan phantom and abdominal and pelvic images. RESULTS: In three experiments, the visual effect of the proposed method is the best. Under 64 projection angles, the lowest RMSE is 0.004776 and the highest VIF is 0.948724. FSIM and SSIM are all higher than 0.98. Under 50 projection angles, the index of the proposed method remains achieving the best image quality. CONCLUSION: Qualitative and quantitative results of this study demonstrate that this new proposed method can not only remove strip artifacts, but also effectively protect image details. Show more

Keywords: Computed tomography imaging, sparse angle, group sparse representation, dictionary learning

DOI: 10.3233/XST-221199

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2022

Authors: Wei, Shu-Hua | Zhang, Jin-Mei | Shi, Bin | Gao, Fei | Zhang, Zhao-Xuan | Qian, Li-Ting

Article Type: Research Article

Abstract: OBJECTIVE: To investigate predictive value of CT-based radiomics features on visceral pleural invasion (VPI) in ≤3.0 cm peripheral type early non-small cell lung cancer (NSCLC). METHODS: A total of 221 NSCLC cases were collected. Among them, 115 are VPI-positive and 106 are VPI-negative. Using a stratified random sampling method, 70% cases were assigned to training dataset (n  = 155) and 30% cases (n  = 66) were assigned to validation dataset. First, CT findings, imaging features, clinical data and pathological findings were retrospectively analyzed, the size, location and density characteristics of nodules and lymph node status, the relationship between lesions and pleura …(RAP) were assessed, and their mean CT value and the shortest distance between lesions and pleura (DLP) were measured. Next, the minimum redundancy-maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) features were extracted from the imaging features. Then, CT imaging prediction model, texture feature prediction model and joint prediction model were built using multifactorial logistic regression analysis method, and the area under the ROC curve (AUC) was applied to evaluate model performance in predicting VPI. RESULTS: Mean diameter, density, fractal relationship with pleura, and presence of lymph node metastasis were all independent predictors of VPI. When applying to the validation dataset, the CT imaging model, texture feature model, and joint prediction model yielded AUC = 0.882, 0.824 and 0.894, respectively, indicating that AUC of the joint prediction model was the highest (p  <  0.05). CONCLUSION: The study demonstrates that the joint prediction model containing CT morphological features and texture features enables to predict the presence of VPI in early NSCLC preoperatively at the highest level. Show more

Keywords: Non-small cell lung cancer (NSCLC), Visceral pleural invasion (VPI), Computed tomography (CT), radiomics, predictive models

DOI: 10.3233/XST-221220

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-12, 2022

Authors: Helwan, Abdulkader | Azar, Danielle | Abdellatef, Hamdan

Article Type: Research Article

Abstract: BACKGROUND: Knee Osteoarthritis (KOA) is the most common type of Osteoarthritis (OA) and it is diagnosed by physicians using a standard 0 –4 Kellgren Lawrence (KL) grading system which sets the KOA on a spectrum of 5 grades; starting from normal (0) to Severe OA (4). OBJECTIVES: In this paper, we propose a transfer learning approach of a very deep wide residual learning-based network (WRN-50-2) which is fine-tuned using X-ray plain radiographs from the Osteoarthritis Initiative (OAI) dataset to learn the KL severity grading of KOA. METHODS: We propose a data augmentation approach of OAI data …to avoid data imbalance and reduce overfitting by applying it only to certain KL grades depending on their number of plain radiographs. Then we conduct experiments to test the model based on an independent testing data of original plain radiographs acquired from the OAI dataset. RESULTS: Experimental results showed good generalization power in predicting the KL grade of knee X-rays with an accuracy of 72% and Precision 74% . Moreover, using Grad-Cam, we also observed that network selected some distinctive features that describe the prediction of a KL grade of a knee radiograph. CONCLUSION: This study demonstrates that our proposed new model outperforms several other related works, and it can be further improved to be used to help radiologists make more accurate and precise diagnosis of KOA in future clinical practice. Show more

Keywords: Knee osteoarthritis, Kellgren Lawrence, Wide ResNet-50-2, Grad-Cam, Residual learning

DOI: 10.3233/XST-221190

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2022

Authors: Durmus, Ismail Faruk | Okumus, Ayse

Article Type: Research Article

Abstract: OBJECTIVE: To compare dosimetric and radiobiological terms of modified dynamic conformal arc therapy (mDCAT) and volumetric modulated arc therapy (VMAT) techniques using different flattening-filter free (FFF) energies in patients with single adrenal metastasis. METHODS: In this study, plans were prepared for 10 patients drawing on the mDCAT and VMAT techniques with 6MV-FFF and 10MV-FFF energies. Target volume doses, biological effective doses (BED), quality indices, Monitor Unit (MU), number of segments, beam-on time and critical organ doses were compared in the plans. RESULTS: Plans with the significantly lower gradient index (GI) and conformity index (CI) values were …obtained with 6MV-FFF energy VMAT planning (p  <  0.05). The higher values were obtained for dose to 95% of internal target volume (ITVD95 ), ITVD95 -BED10 with 10MV-FFF energy VMAT planning, whereas lower results were obtained for high dose spillage (HDS%) values (p  <  0,05). With 10MV-FFF energy, HDS% values were 21.1% lower in VMAT plans and 5.6% lower in mDCAT plans compared to 6MV-FFF energy. Plans with approximately 50% fewer segments were obtained in mDCAT plans than VMAT plans (p  <  0,05). Beam-on time values with mDCAT was 1.84 times lower when 6MV-FFF energies were analyzed, and 2.11 times lower when 10MV-FFF was analyzed (p  <  0,05). Additionally, when 6MV-FFF and 10MV-FFF energies were examined, MU values with mDCAT were 2.1 and 2.5 times lower (p  <  0,05). In general, the smaller the target volume size, the greater the differences between MU and beam-on time values mDCAT and VMAT. CONCLUSIONS: The study results implied that VMAT enabled to offer significantly more conformal SBRT plans with steeper dose fall-off beyond the target volume for single adrenal metastasis than the mDCAT, which attained at the cost of significantly higher MU and beam-on times. Especially with 10MV-FFF energy mDCAT plans, low-dose-bath zones can be reduced, and shorter-term treatments can be implemented with large segments. In adrenal gland SBRT, higher effective doses can be achieved with the right energy and technique, critical organ doses can be reduced, thus increasing the possibility of local control of the tumor with low toxicity. Show more

Keywords: Adrenal gland SBRT, mDCAT, VMAT, FFF

DOI: 10.3233/XST-221192

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-10, 2022

Authors: Nakazeko, Kazuma | Kojima, Shinya | Watanabe, Hiroyuki | Kudo, Hiroyuki

Article Type: Research Article

Abstract: BACKGROUND: Skull radiography, an assessment method for initial diagnosis and post-operative follow-up, requires substantial retaking of various types of radiographs. During retaking, a radiologic technologist estimates a patient’s rotation angle from the radiograph by comprehending the relationship between the radiograph and the patient’s angle for adequate assessment, which requires extensive experience. OBJECTIVE: To develop and test a new deep learning model or method to automatically estimate patient’s angle from radiographs. METHODS: The patient’s position is assessed using deep learning to estimate their angle from skull radiographs. Skull radiographs are simulated using two-dimensional projections from head computed …tomography images and used as input data to estimate the patient’s angle, using deep learning under supervised training. A residual neural network model is used where the rectified linear unit is changed to a parametric rectified linear unit, and dropout is added. The patient’s angle is estimated in the lateral and superior-inferior directions. RESULTS: Applying this new deep learning model, the estimation errors are 0.56±0.36° and 0.72±0.52° in the lateral and superior-inferior angles, respectively. CONCLUSIONS: These findings suggest that a patient’s angle can be accurately estimated from a radiograph using a deep learning model leading to reduce retaking time, and then used to facilitate skull radiography. Show more

Keywords: Skull radiography, radiographs, patient’s angle, retaking, deep learning, ResNet

DOI: 10.3233/XST-221200

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-13, 2022

Authors: Priyanka, | Kadavigere, Rajagopal | Sukumar, Suresh

Article Type: Research Article

Abstract: BACKGROUND: Pediatric population is more sensitive to the effects of radiation than adults. Establishing diagnostic reference level (DRL) is an efficient dose optimization technique implemented by many countries for reducing radiation dose during Computed Tomography (CT) examinations. OBJECTIVES: To estimate radiation dose and establish a new local diagnostic reference level for CT head examination in the pediatric population. MATERIALS AND METHODS: We prospectively recruited 143 pediatric patients referred for CT head examination with age ranging from 0–5 years old. All patients had undergone CT head examination using the standard pediatric head protocol. Volumetric CT dose index …(CTDIvol) and dose length product (DLP) were recorded. The effective dose was first calculated. Then, 75th percentile of dose indices was calculated to establish DRLs. RESULTS: DRLs in terms of CTDIvol and DLP are 23.84 mGy, 555.99 mGy.cm for patients <1 years old and 28.65 mGy, 794.99 mGy.cm for patients from 1–5 years old, respectively. Mean effective doses for <1 years old patients and 1–5 years old patients are 2.91 mSv and 2.78 mSv respectively. CONCLUSION: The study concludes that DRL in terms of CTDIvol is lower but DRL in terms of DLP and the effective dose is higher compared to a few other studies which necessitate the need for dose optimization. Show more

Keywords: Diagnostic reference levels, pediatric computed tomography, volumetric computed tomography dose index, dose length product, effective dose estimation

DOI: 10.3233/XST-221172

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-9, 2022

Authors: Chen, Chih-I | Lu, Nan-Han | Huang, Yung-Hui | Liu, Kuo-Ying | Hsu, Shih-Yen | Matsushima, Akari | Wang, Yi-Ming | Chen, Tai-Been

Article Type: Research Article

Abstract: BACKGROUND: Dividing liver organs or lesions depicting on computed tomography (CT) images could be applied to help tumor staging and treatment. However, most existing image segmentation technologies use manual or semi-automatic analysis, making the analysis process costly and time-consuming. OBJECTIVE: This research aims to develop and apply a deep learning network architecture to segment liver tumors automatically after fine tuning parameters. METHODS AND MATERIALS: The medical imaging is obtained from the International Symposium on Biomedical Imaging (ISBI), which includes 3D abdominal CT scans of 131 patients diagnosed with liver tumors. From these CT scans, there are …7,190 2D CT images along with the labeled binary images. The labeled binary images are regarded as gold standard for evaluation of the segmented results by FCN (Fully Convolutional Network). The backbones of FCN are extracted from Xception, InceptionresNetv2, MobileNetv2, ResNet18, ResNet50 in this study. Meanwhile, the parameters including optimizers (SGDM and ADAM), size of epoch, and size of batch are investigated. CT images are randomly divided into training and testing sets using a ratio of 9:1. Several evaluation indices including Global Accuracy, Mean Accuracy, Mean IoU (Intersection over Union), Weighted IoU and Mean BF Score are applied to evaluate tumor segmentation results in the testing images. RESULTS: The Global Accuracy, Mean Accuracy, Mean IoU, Weighted IoU, and Mean BF Scores are 0.999, 0.969, 0.954, 0.998, 0.962 using ResNet50 in FCN with optimizer SGDM, batch size 12, and epoch 9. It is important to fine tuning the parameters in FCN model. Top 20 FNC models enable to achieve higher tumor segmentation accuracy with Mean IoU over 0.900. The occurred frequency of InceptionresNetv2, MobileNetv2, ResNet18, ResNet50, and Xception are 9, 6, 3, 5, and 2 times. Therefore, the InceptionresNetv2 has higher performance than others. CONCLUSIONS: This study develop and test an automated liver tumor segmentation model based on FCN. Study results demonstrate that many deep learning models including InceptionresNetv2, MobileNetv2, ResNet18, ResNet50, and Xception have high potential to segment liver tumors from CT images with accuracy exceeding 90% . However, it is still difficult to accurately segment tiny and small size tumors by FCN models. Show more

Keywords: Abdominal computerized tomography (CT), fully convolutional network, liver tumor segmentation

DOI: 10.3233/XST-221194

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-14, 2022

Authors: Bourkache, Noureddine | Laghrouche, Mourad | Lahdir, Mourad | Sidhom, Sahbi

Article Type: Research Article

Abstract: BACKGROUND: Medical diagnostic support systems are important tools in the field of radiology. However, the precision obtained, during the exploitation of high homogeneity image datasets, needs to be improved. OBJECTIVE: To develop a new learning system dedicated to public health practitioners. This study presents an upgraded version dedicated to radiology experts for better clinical decision-making when diagnosing and treating the patient (CAD approach). METHODS: Our system is a hybrid approach based on a matching of semantic and visual attributes of images. It is a combination of two complementary subsystems to form the intermodal system. The first …one named α based on semantic attributes. Indexing and image retrieval based on specific key words. The second system named β based on low-level attributes. Vectors characterizing the digital content of the image (color, texture and shape) represent images. Our image database consists of 930 X-ray images including 320 mammograms acquired from the mini-MIAS database of mammograms and 610 X-rays acquired from the Public Hospital Establishment (EPH-Rouiba Algeria). The combination of two subsystems gives rise to the intermodal system: α-subsystem offers an overall result (based on visual descriptors), then β-subsystem (low level descriptors) refines the result and increases relevance. RESULTS: Our system can perform a specific image search (in a database of images with very high homogeneity) with an accuracy of around 90% for a recall of 25% . The average (overall) accuracy of the system exceeds 70% . CONCLUSION: The results obtained are very encouraging, and demonstrate efficiency of our approach, particularly for the intermodal system. Show more

Keywords: Image indexing, medical image, data base indexing, information retrieval, content base retrieval, query by image, cancer diagnosis

DOI: 10.3233/XST-221180

Citation: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-21, 2022