Journal of X-Ray Science and Technology - Volume 30, issue 5

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. 30, no. 5, pp. 993-1008, 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. 30, no. 5, pp. 1009-1021, 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. 30, no. 5, pp. 1023-1032, 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. 30, no. 5, pp. 1033-1045, 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. 30, no. 5, pp. 1047-1056, 2022