Journal of X-Ray Science and Technology最新文献

{"title":"Adaptive orthogonal directional total variation with kernel regression for CT image denoising.","authors":"Xiying Xue, Dongjiang Ji, Chunyu Xu, Yuqing Zhao, Yimin Li, Chunhong Hu","doi":"10.3233/XST-230416","DOIUrl":"10.3233/XST-230416","url":null,"abstract":"<p><strong>Background: </strong>Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images.</p><p><strong>Objective: </strong>In order to address this issue and preserve image edge details, this study proposes an adaptive orthogonal directional total variation method with kernel regression.</p><p><strong>Methods: </strong>The CT reconstructed images are initially processed through kernel regression to obtain the N-term Taylor series, which serves as a local representation of the regression function. By expanding the series to the second order, we obtain the desired estimate of the regression function and localized information on the first and second derivatives. To mitigate the noise impact on these derivatives, kernel regression is performed again to update the first and second derivatives. Subsequently, the original reconstructed image, its local approximation, and the updated derivatives are summed using a weighting scheme to derive the image used for calculating orientation information. For further removal of stripe artifacts, the study introduces the adaptive orthogonal directional total variation (AODTV) method, which denoises along both the edge direction and the normal direction, guided by the previously obtained orientation.</p><p><strong>Results: </strong>Both simulation and real experiments have obtained good results. The results of two real experiments show that the proposed method has obtained PSNR values of 34.5408 dB and 29.4634 dB, which are 1.2392-5.9333 dB and 2.828-6.7995 dB higher than the contrast denoising algorithm, respectively, indicating that the proposed method has good denoising performance.</p><p><strong>Conclusions: </strong>The study demonstrates the effectiveness of the method in eliminating strip artifacts and preserving the fine details of the images.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1253-1271"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602049","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}

{"title":"Design of a multi-carrier X-ray source for communication with energy modulation information.","authors":"Youtao Gao, Yixiang Wu, Shijia Li, Daqian Hei, Yajun Tang","doi":"10.3233/XST-240094","DOIUrl":"10.3233/XST-240094","url":null,"abstract":"<p><p>X-ray communication is a kind of space communication technology which uses X-ray as information carrier. In order to improve the information transmission capacity, communication rate and anti-interference ability of X-ray communication, we proposes to design a novel multi-target X-ray source. The source is composed of a fast switching module of light channels based on FPGA technology and four photoelectric X-ray tubes with different target materials: Cr, Fe, Ni, and Cu. Using Geant4 software, we determined the optimal target thickness for each material, which enabled us to fully leverage the characteristic X-rays for multi-channel signal modulation transmission. Moreover, using CST software for particle trajectory simulation and optimization of the electron beam revealed that at a tube voltage of 20 kV, the focus area measures approximately 1.2 mm×1.2 mm. The simulations show that four kinds of spectra with high distinctiveness can be generated from the Cr, Fe, Ni, and Cu targets. Within a single modulation period, these spectra can be combined in various ways to create 16 different X-ray spectra signals, thereby increasing the number of communication elements and enhancing the information transmission rate.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1315-1329"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731579","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}

{"title":"Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks","authors":"Erapaneni Gayatri, S.L. Aarthy","doi":"10.3233/xst-230204","DOIUrl":"https://doi.org/10.3233/xst-230204","url":null,"abstract":"BACKGROUND:With the rapid growth of Deep Neural Networks (DNN) and Computer-Aided Diagnosis (CAD), more significant works have been analysed for cancer related diseases. Skin cancer is the most hazardous type of cancer that cannot be diagnosed in the early stages. OBJECTIVE:The diagnosis of skin cancer is becoming a challenge to dermatologists as an abnormal lesion looks like an ordinary nevus at the initial stages. Therefore, early identification of lesions (origin of skin cancer) is essential and helpful for treating skin cancer patients effectively. The enormous development of automated skin cancer diagnosis systems significantly supports dermatologists. METHODS:This paper performs a classification of skin cancer by utilising various deep-learning frameworks after resolving the class Imbalance problem in the ISIC-2019 dataset. A fine-tuned ResNet-50 model is used to evaluate the performance of original data, augmented data, and after by adding the focal loss. Focal loss is the best technique to solve overfitting problems by assigning weights to hard misclassified images. RESULTS:Finally, augmented data with focal loss is given a good classification performance with 98.85% accuracy, 95.52% precision, and 95.93% recall. Matthews Correlation coefficient (MCC) is the best metric to evaluate the quality of multi-class images. It has given outstanding performance by using augmented data and focal loss.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"6 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083499","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}

{"title":"Performance evaluation of quantitative material decomposition in slow kVp switching dual-energy CT","authors":"Chenchen Ma, Ting Su, Jiongtao Zhu, Xin Zhang, Hairong Zheng, Dong Liang, Na Wang, Yunxin Zhang, Yongshuai Ge","doi":"10.3233/xst-230201","DOIUrl":"https://doi.org/10.3233/xst-230201","url":null,"abstract":"BACKGROUND:Slow kVp switching technique is an important approach to realize dual-energy CT (DECT) imaging, but its performance has not been thoroughly investigated yet. OBJECTIVE:This study aims at comparing and evaluating the DECT imaging performance of different slow kVp switching protocols, andthus helps determining the optimal system settings. METHODS:To investigate the impact of energy separation, two different beam filtration schemes are compared: the stationary beam filtration and dynamic beam filtration. Moreover, uniform tube voltage modulation and weighted tube voltage modulation are compared along with various modulation frequencies. A model-based direct decomposition algorithm is employed to generate the water and iodine material bases. Both numerical and physical experiments are conducted to verify the slow kVp switching DECT imaging performance. RESULTS: Numerical and experimental results demonstrate that the material decomposition is less sensitive to beam filtration, voltage modulation type and modulation frequency. As a result, robust material-specific quantitative decomposition can be achieved in slow kVp switching DECT imaging. CONCLUSIONS:Quantitative DECT imaging can be implemented with slow kVp switching under a variety of system settings.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"175 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083553","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}

{"title":"Dual spectral limited-angle CT imaging regularized by edge-preserving diffusion and smoothing.","authors":"Yanwei Qin, Ying Zhang, Xin Lu, Peng Zhang, Yunsong Zhao","doi":"10.2139/ssrn.4264039","DOIUrl":"https://doi.org/10.2139/ssrn.4264039","url":null,"abstract":"Limited-angle CT scan is an effective way for nondestructive inspection of planar objects, and various methods have been proposed accordingly. When the scanned object contains high-absorption material, such as metal, existing methods may fail due to the beam hardening of X-rays. In order to overcome this problem, we adopt a dual spectral limited-angle CT scan and propose a corresponding image reconstruction algorithm, which takes the polychromatic property of the X-ray into consideration, makes basis material images free of beam hardening artifacts and metal artifacts, and then helps depress the limited-angle artifacts. Experimental results on both simulated PCB data and real data demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44880270","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}

{"title":"A novel multi-view X-ray digital imaging stitching algorithm.","authors":"Yang Kang,&nbsp;Rui Wu,&nbsp;Sen Wu,&nbsp;Kun Cao,&nbsp;Tingting Tan,&nbsp;Yingrui Li,&nbsp;Gangqiang Zha","doi":"10.3233/XST-221261","DOIUrl":"https://doi.org/10.3233/XST-221261","url":null,"abstract":"<p><strong>Background: </strong>In fan beam X-ray imaging applications, several X-ray images sometimes need to be stitched together into a panoramic image because of the size limitations of the detector.</p><p><strong>Objective: </strong>This study aims to propose a novel multi-view X-ray digital imaging stitching algorithm (MVS) based on the CdZnTe photon counting linear array detectors to solve the problem of fan beam X-ray stitching deformation.</p><p><strong>Methods: </strong>The panoramic image is generated in four steps including (1) multi-view projection data acquisition, (2) overlapping positioning, (3) weighted fusion and (4) projected pixel value calculation. Images of a globe and foot are scanned by fan beam X-rays and a CdZnTe detector. The proposed method is applied to stitch together the scanned images of the globe. Three other methods are also used for comparison. Finally, this MVS algorithm is also used in the stitching of scanned images of the foot.</p><p><strong>Results: </strong>Compared with the 50% stitching accuracy of other methods, the new MVS algorithm reached a stitching accuracy of 94.4%. Image distortion on the globe and feet is also eliminated and thus image quality is significantly improved.</p><p><strong>Conclusions: </strong>This study proposes a new multi-view X-ray digital imaging stitching algorithm. Study results demonstrate the superiority of this new algorithm and its feasibility in practical applications.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"31 1","pages":"153-166"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9295727","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}

{"title":"Development of a new body weight estimation method using head CT scout images.","authors":"Tatsuya Kondo,&nbsp;Manami Umezu,&nbsp;Yohan Kondo,&nbsp;Mitsuru Sato,&nbsp;Tsutomu Kanazawa,&nbsp;Yoshiyuki Noto","doi":"10.3233/XST-230087","DOIUrl":"https://doi.org/10.3233/XST-230087","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"31 5","pages":"1079-1091"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10649844","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}

{"title":"Projection domain processing for low-dose CT reconstruction based on subspace identification.","authors":"Junru Ren,&nbsp;Ningning Liang,&nbsp;Xiaohuan Yu,&nbsp;Yizhong Wang,&nbsp;Ailong Cai,&nbsp;Lei Li,&nbsp;Bin Yan","doi":"10.3233/XST-221262","DOIUrl":"https://doi.org/10.3233/XST-221262","url":null,"abstract":"<p><strong>Purpose: </strong>Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT.</p><p><strong>Methods: </strong>This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom.</p><p><strong>Results: </strong>In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04.</p><p><strong>Conclusion: </strong>This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"31 1","pages":"63-84"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10785734","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}

{"title":"Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma.","authors":"Yongzhen Ren, Siyuan Lu, Dongmei Zhang, Xian Wang, Enock Adjei Agyekum, Jin Zhang, Qing Zhang, Feiju Xu, Guoliang Zhang, Yu Chen, Xiangjun Shen, Xuelin Zhang, Ting Wu, Hui Hu, Xiuhong Shan, Jun Wang, Xiaoqin Qian","doi":"10.3233/XST-230091","DOIUrl":"10.3233/XST-230091","url":null,"abstract":"<p><strong>Background: </strong>Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making.</p><p><strong>Objective: </strong>This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC.</p><p><strong>Methods: </strong>In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier.</p><p><strong>Results: </strong>Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only.</p><p><strong>Conclusions: </strong>The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1263-1280"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10088989","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}

{"title":"Automated lung tumor segmentation robust to various tumor sizes using a consistency learning-based multi-scale dual-attention network.","authors":"Jumin Lee,&nbsp;Min-Jin Lee,&nbsp;Bong-Seog Kim,&nbsp;Helen Hong","doi":"10.3233/XST-230003","DOIUrl":"https://doi.org/10.3233/XST-230003","url":null,"abstract":"<p><strong>Background: </strong>It is often difficult to automatically segment lung tumors due to the large tumor size variation ranging from less than 1 cm to greater than 7 cm depending on the T-stage.</p><p><strong>Objective: </strong>This study aims to accurately segment lung tumors of various sizes using a consistency learning-based multi-scale dual-attention network (CL-MSDA-Net).</p><p><strong>Methods: </strong>To avoid under- and over-segmentation caused by different ratios of lung tumors and surrounding structures in the input patch according to the size of the lung tumor, a size-invariant patch is generated by normalizing the ratio to the average size of the lung tumors used for the training. Two input patches, a size-invariant patch and size-variant patch are trained on a consistency learning-based network consisting of dual branches that share weights to generate a similar output for each branch with consistency loss. The network of each branch has a multi-scale dual-attention module that learns image features of different scales and uses channel and spatial attention to enhance the scale-attention ability to segment lung tumors of different sizes.</p><p><strong>Results: </strong>In experiments with hospital datasets, CL-MSDA-Net showed an F1-score of 80.49%, recall of 79.06%, and precision of 86.78%. This resulted in 3.91%, 3.38%, and 2.95% higher F1-scores than the results of U-Net, U-Net with a multi-scale module, and U-Net with a multi-scale dual-attention module, respectively. In experiments with the NSCLC-Radiomics datasets, CL-MSDA-Net showed an F1-score of 71.7%, recall of 68.24%, and precision of 79.33%. This resulted in 3.66%, 3.38%, and 3.13% higher F1-scores than the results of U-Net, U-Net with a multi-scale module, and U-Net with a multi-scale dual-attention module, respectively.</p><p><strong>Conclusions: </strong>CL-MSDA-Net improves the segmentation performance on average for tumors of all sizes with significant improvements especially for small sized tumors.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"31 5","pages":"879-892"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10299511","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}