Journal of X-Ray Science and Technology最新文献_第4页

DKCNN: Improving deep kernel convolutional neural network-based COVID-19 identification from CT images of the chest. DKCNN：从胸部 CT 图像中改进基于深度核卷积神经网络的 covid-19 识别。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI: 10.3233/XST-230424

T Vaikunta Pai, K Maithili, Ravula Arun Kumar, D Nagaraju, D Anuradha, Shailendra Kumar, Ananda Ravuri, T Sunilkumar Reddy, M Sivaram, R G Vidhya

{"title":"DKCNN: Improving deep kernel convolutional neural network-based COVID-19 identification from CT images of the chest.","authors":"T Vaikunta Pai, K Maithili, Ravula Arun Kumar, D Nagaraju, D Anuradha, Shailendra Kumar, Ananda Ravuri, T Sunilkumar Reddy, M Sivaram, R G Vidhya","doi":"10.3233/XST-230424","DOIUrl":"10.3233/XST-230424","url":null,"abstract":"Background: An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease.Objective: A novel structure known as the Pointwise-Temporal-pointwise convolution unit is developed incorporated with the varying kernel-based depth wise temporal convolution before and after the pointwise convolution operations.Methods: The outcome is optimized by the Slap Swarm algorithm (SSA). The proposed Deep CNN is composed of depth wise temporal convolution and end-to-end automatic detection of disease. First, the datasets SARS-COV-2 Ct-Scan Dataset and CT scan COVID Prediction dataset are preprocessed using the min-max approach and the features are extracted for further processing.Results: The experimental analysis is conducted between the proposed and some state-of-art works and stated that the proposed work effectively classifies the disease than the other approaches.Conclusion: The proposed structural unit is used to design the deep CNN with the increasing kernel sizes. The classification process is improved by the inclusion of depth wise temporal convolutions along with the kernel variation. The computational complexity is reduced by the introduction of stride convolutions are used in the residual linkage among the adjacent structural units.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"913-930"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141184758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Special Section: Medical Applications of X-ray Imaging Techniques. 专栏：X 射线成像技术的医学应用。

IF 3 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01

引用次数: 0

An optimized filter design approach for enhancing imaging quality in industrial linear accelerator. 提高工业直线加速器成像质量的优化滤波器设计方法。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI: 10.3233/XST-240032

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

{"title":"An optimized filter design approach for enhancing imaging quality in industrial linear accelerator.","authors":"Gang Chen, Zehuan Zhang, Shuo Xu, Shibo Jiang, Ximing Liu, Peng Tang, Songyuan Li, Xincheng Xiang","doi":"10.3233/XST-240032","DOIUrl":"10.3233/XST-240032","url":null,"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1137-1150"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141321939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron. 利用鲸鱼优化算法、支持向量机和多层感知器从 CT 切片诊断 Covid-19。

IF 3 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI: 10.3233/XST-230196

R Betshrine Rachel, H Khanna Nehemiah, Vaibhav Kumar Singh, Rebecca Mercy Victoria Manoharan

{"title":"Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.","authors":"R Betshrine Rachel, H Khanna Nehemiah, Vaibhav Kumar Singh, Rebecca Mercy Victoria Manoharan","doi":"10.3233/XST-230196","DOIUrl":"10.3233/XST-230196","url":null,"abstract":"Background: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Objective: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented.Methods: The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features.Results: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered.Conclusion: The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"253-269"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dense and U-shaped transformer with dual-domain multi-loss function for sparse-view CT reconstruction. 用于稀疏视图 CT 重建的具有双域多损耗函数的密集 U 型变压器

IF 3 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI: 10.3233/XST-230184

Peng Liu, Chenyun Fang, Zhiwei Qiao

{"title":"A dense and U-shaped transformer with dual-domain multi-loss function for sparse-view CT reconstruction.","authors":"Peng Liu, Chenyun Fang, Zhiwei Qiao","doi":"10.3233/XST-230184","DOIUrl":"10.3233/XST-230184","url":null,"abstract":"Objective: CT image reconstruction from sparse-view projections is an important imaging configuration for low-dose CT, as it can reduce radiation dose. However, the CT images reconstructed from sparse-view projections by traditional analytic algorithms suffer from severe sparse artifacts. Therefore, it is of great value to develop advanced methods to suppress these artifacts. In this work, we aim to use a deep learning (DL)-based method to suppress sparse artifacts.Methods: Inspired by the good performance of DenseNet and Transformer architecture in computer vision tasks, we propose a Dense U-shaped Transformer (D-U-Transformer) to suppress sparse artifacts. This architecture exploits the advantages of densely connected convolutions in capturing local context and Transformer in modelling long-range dependencies, and applies channel attention to fusion features. Moreover, we design a dual-domain multi-loss function with learned weights for the optimization of the model to further improve image quality.Results: Experimental results of our proposed D-U-Transformer yield performance improvements on the well-known Mayo Clinic LDCT dataset over several representative DL-based models in terms of artifact suppression and image feature preservation. Extensive internal ablation experiments demonstrate the effectiveness of the components in the proposed model for sparse-view computed tomography (SVCT) reconstruction.Significance: The proposed method can effectively suppress sparse artifacts and achieve high-precision SVCT reconstruction, thus promoting clinical CT scanning towards low-dose radiation and high-quality imaging. The findings of this work can be applied to denoising and artifact removal tasks in CT and other medical images.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"207-228"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139673531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images. 融合深度神经网络和博弈论，利用光学视网膜断层扫描图像诊断视网膜疾病。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI: 10.3233/XST-240027

S Vishnu Priyan, R Vinod Kumar, C Moorthy, V S Nishok

{"title":"A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images.","authors":"S Vishnu Priyan, R Vinod Kumar, C Moorthy, V S Nishok","doi":"10.3233/XST-240027","DOIUrl":"10.3233/XST-240027","url":null,"abstract":"Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of artificial intelligence. This paper provides a complete approach to improve the accuracy and reliability of retinal disease identification using images from OCT (Retinal Optical Coherence Tomography). The Hybrid Model GIGT, which combines Generative Adversarial Networks (GANs), Inception, and Game Theory, is a novel method for diagnosing retinal diseases using OCT pictures. This technique, which is carried out in Python, includes preprocessing images, feature extraction, GAN classification, and a game-theoretic examination. Resizing, grayscale conversion, noise reduction using Gaussian filters, contrast enhancement using Contrast Limiting Adaptive Histogram Equalization (CLAHE), and edge recognition via the Canny technique are all part of the picture preparation step. These procedures set up the OCT pictures for efficient analysis. The Inception model is used for feature extraction, which enables the extraction of discriminative characteristics from the previously processed pictures. GANs are used for classification, which improves accuracy and resilience by adding a strategic and dynamic aspect to the diagnostic process. Additionally, a game-theoretic analysis is utilized to evaluate the security and dependability of the model in the face of hostile attacks. Strategic analysis and deep learning work together to provide a potent diagnostic tool. This suggested model's remarkable 98.2% accuracy rate shows how this method has the potential to improve the detection of retinal diseases, improve patient outcomes, and address the worldwide issue of visual impairment.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1011-1039"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140960156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FDB-Net: Fusion double branch network combining CNN and transformer for medical image segmentation. FDB-Net：结合 CNN 和变换器的融合双分支网络，用于医学图像分割。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI: 10.3233/XST-230413

Zhongchuan Jiang, Yun Wu, Lei Huang, Maohua Gu

{"title":"FDB-Net: Fusion double branch network combining CNN and transformer for medical image segmentation.","authors":"Zhongchuan Jiang, Yun Wu, Lei Huang, Maohua Gu","doi":"10.3233/XST-230413","DOIUrl":"10.3233/XST-230413","url":null,"abstract":"Background: The rapid development of deep learning techniques has greatly improved the performance of medical image segmentation, and medical image segmentation networks based on convolutional neural networks and Transformer have been widely used in this field. However, due to the limitation of the restricted receptive field of convolutional operation and the lack of local fine information extraction ability of the self-attention mechanism in Transformer, the current neural networks with pure convolutional or Transformer structure as the backbone still perform poorly in medical image segmentation.Methods: In this paper, we propose FDB-Net (Fusion Double Branch Network, FDB-Net), a double branch medical image segmentation network combining CNN and Transformer, by using a CNN containing gnConv blocks and a Transformer containing Varied-Size Window Attention (VWA) blocks as the feature extraction backbone network, the dual-path encoder ensures that the network has a global receptive field as well as access to the target local detail features. We also propose a new feature fusion module (Deep Feature Fusion, DFF), which helps the image to simultaneously fuse features from two different structural encoders during the encoding process, ensuring the effective fusion of global and local information of the image.Conclusion: Our model achieves advanced results in all three typical tasks of medical image segmentation, which fully validates the effectiveness of FDB-Net.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"931-951"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Special Section: Medical Applications of X-ray Imaging Techniques. 专栏：X 射线成像技术的医学应用。

IF 3 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01

引用次数: 0

Evaluation of cutout factors with small and narrow fields using various dosimetry detectors in electron beam keloid radiotherapy. 在电子束瘢痕疙瘩放射治疗中使用各种剂量检测器评估小场和窄场的切出因子。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI: 10.3233/XST-240059

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

{"title":"Evaluation of cutout factors with small and narrow fields using various dosimetry detectors in electron beam keloid radiotherapy.","authors":"Yu-Fang Lin, Chen-Hsi Hsieh, Hui-Ju Tien, Yi-Huan Lee, Yi-Chun Chen, Lu-Han Lai, Shih-Ming Hsu, Pei-Wei Shueng","doi":"10.3233/XST-240059","DOIUrl":"10.3233/XST-240059","url":null,"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1177-1184"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141437769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A feasibility study to predict 3D dose delivery accuracy for IMRT using DenseNet with log files. 使用带有日志文件的 DenseNet 预测 IMRT 3D 剂量输送准确性的可行性研究。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI: 10.3233/XST-230412

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

{"title":"A feasibility study to predict 3D dose delivery accuracy for IMRT using DenseNet with log files.","authors":"Ying Huang, Ruxin Cai, Yifei Pi, Kui Ma, Qing Kong, Weihai Zhuo, Yan Kong","doi":"10.3233/XST-230412","DOIUrl":"10.3233/XST-230412","url":null,"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1199-1208"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140870664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0