Journal of X-Ray Science and Technology最新文献

{"title":"Deep-silicon photon-counting x-ray projection denoising through reinforcement learning","authors":"Md Sayed Tanveer, Christopher Wiedeman, Mengzhou Li, Yongyi Shi, Bruno De Man, Jonathan S. Maltz, Ge Wang","doi":"10.3233/xst-230278","DOIUrl":"https://doi.org/10.3233/xst-230278","url":null,"abstract":"BACKGROUND: In recent years, deep reinforcement learning (RL) has been applied to various medical tasks and produced encouraging results. OBJECTIVE: In this paper, we demonstrate the feasibility of deep RL for denoising simulated deep-silicon photon-counting CT (PCCT) data in both full and interior scan modes. PCCT offers higher spatial and spectral resolution than conventional CT, requiring advanced denoising methods to suppress noise increase. METHODS: In this work, we apply a dueling double deep Q network (DDDQN) to denoise PCCT data for maximum contrast-to-noise ratio (CNR) and a multi-agent approach to handle data non-stationarity. RESULTS: Using our method, we obtained significant image quality improvement for single-channel scans and consistent improvement for all three channels of multichannel scans. For the single-channel interior scans, the PSNR (dB) and SSIM increased from 33.4078 and 0.9165 to 37.4167 and 0.9790 respectively. For the multichannel interior scans, the channel-wise PSNR (dB) increased from 31.2348, 30.7114, and 30.4667 to 31.6182, 30.9783, and 30.8427 respectively. Similarly, the SSIM improved from 0.9415, 0.9445, and 0.9336 to 0.9504, 0.9493, and 0.0326 respectively. CONCLUSIONS: Our results show that the RL approach improves image quality effectively, efficiently, and consistently across multiple spectral channels and has great potential in clinical applications.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"61 48","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449258","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":"Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature","authors":"Dehua Wang, Hayder Jasim Taher, Murtadha Al-Fatlawi, Badr Ahmed Abdullah, Munojat Khayatovna Ismailova, R. Abedi-Firouzjah","doi":"10.3233/xst-230307","DOIUrl":"https://doi.org/10.3233/xst-230307","url":null,"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"56 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449526","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":"Multiple energy X-ray imaging of metal oxide particles inside gingival tissues.","authors":"Jarrod Cortez, Ignacio Romero, Jason Ngo, Md Sayed Tanveer Azam, Chuang Niu, Cássio Luiz Coutinho Almeida-da-Silva, Leticia Ferreira Cabido, David M Ojcius, Wei-Chun Chin, Ge Wang, Changqing Li","doi":"10.3233/XST-230175","DOIUrl":"10.3233/XST-230175","url":null,"abstract":"<p><strong>Background: </strong>Periodontal disease affects over 50% of the global population and is characterized by gingivitis as the initial sign. One dental health issue that may contribute to the development of periodontal disease is foreign body gingivitis (FBG), which can result from exposure to some kinds of foreign metal particles from dental products or food.</p><p><strong>Objective: </strong>We design a novel, portable, affordable, multispectral X-ray and fluorescence optical microscopic imaging system dedicated to detecting and differentiating metal oxide particles in dental pathological tissues. A novel denoising algorithm is applied. We verify the feasibility and optimize the performance of the imaging system with numerical simulations.</p><p><strong>Methods: </strong>The designed imaging system has a focused X-ray tube with tunable energy spectra and thin scintillator coupled with an optical microscope as detector. A simulated soft tissue phantom is embedded with 2-micron thick metal oxide discs as the imaged object. GATE software is used to optimize the systematic parameters such as energy bandwidth and X-ray photon number. We have also applied a novel denoising method, Noise2Sim with a two-layer UNet structure, to improve the simulated image quality.</p><p><strong>Results: </strong>The use of an X-ray source operating with an energy bandwidth of 5 keV, X-ray photon number of 108, and an X-ray detector with a 0.5 micrometer pixel size in a 100 by 100-pixel array allowed for the detection of particles as small as 0.5 micrometer. With the Noise2Sim algorithm, the CNR has improved substantially. A typical example is that the Aluminum (Al) target's CNR is improved from 6.78 to 9.72 for the case of 108 X-ray photons with the Chromium (Cr) source of 5 keV bandwidth.</p><p><strong>Conclusions: </strong>Different metal oxide particles were differentiated using Contrast-to-Noise ratio (CNR) by utilizing four different X-ray spectra.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"87-103"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048318","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 hybrid thyroid tumor type classification system using feature fusion, multilayer perceptron and bonobo optimization.","authors":"B Shankarlal, S Dhivya, K Rajesh, S Ashok","doi":"10.3233/XST-230430","DOIUrl":"10.3233/XST-230430","url":null,"abstract":"<p><strong>Background: </strong>Thyroid tumor is considered to be a very rare form of cancer. But recent researches and surveys highlight the fact that it is becoming prevalent these days because of various factors.</p><p><strong>Objectives: </strong>This paper proposes a novel hybrid classification system that is able to identify and classify the above said four different types of thyroid tumors using high end artificial intelligence techniques. The input data set is obtained from Digital Database of Thyroid Ultrasound Images through Kaggle repository and augmented for achieving a better classification performance using data warping mechanisms like flipping, rotation, cropping, scaling, and shifting.</p><p><strong>Methods: </strong>The input data after augmentation goes through preprocessing with the help of bilateral filter and is contrast enhanced using dynamic histogram equalization. The ultrasound images are then segmented using SegNet algorithm of convolutional neural network. The features needed for thyroid tumor classification are obtained from two different algorithms called CapsuleNet and EfficientNetB2 and both the features are fused together. This process of feature fusion is carried out to heighten the accuracy of classification.</p><p><strong>Results: </strong>A Multilayer Perceptron Classifier is used for classification and Bonobo optimizer is employed for optimizing the results produced. The classification performance of the proposed model is weighted using metrics like accuracy, sensitivity, specificity, F1-score, and Matthew's correlation coefficient.</p><p><strong>Conclusion: </strong>It can be observed from the results that the proposed multilayer perceptron based thyroid tumor type classification system works in an efficient manner than the existing classifiers like CANFES, Spatial Fuzzy C means, Deep Belief Networks, Thynet and Generative adversarial network and Long Short-Term memory.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"651-675"},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941063","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":"Three-dimensional analysis of puncture needle path through safety triangle approach PLD and design of puncture positioning guide plate.","authors":"Penghui Yu, Yanbing Li, Qidong Zhao, Xia Chen, Liqin Wu, Shuai Jiang, Libing Rao, Yihua Rao","doi":"10.3233/XST-230267","DOIUrl":"10.3233/XST-230267","url":null,"abstract":"<p><strong>Objective: </strong>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).</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>(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%.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"825-837"},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140190348","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":"Coronary artery segmentation in CCTA images based on multi-scale feature learning.","authors":"Bu Xu, Jinzhong Yang, Peng Hong, Xiaoxue Fan, Yu Sun, Libo Zhang, Benqiang Yang, Lisheng Xu, Alberto Avolio","doi":"10.3233/XST-240093","DOIUrl":"10.3233/XST-240093","url":null,"abstract":"<p><strong>Background: </strong>Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy.</p><p><strong>Objective: </strong>A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries.</p><p><strong>Methods: </strong>The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance.</p><p><strong>Results: </strong>The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets.</p><p><strong>Conclusion: </strong>Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"973-991"},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472046","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":"Reference-free calibration method for asynchronous rotation in robotic CT.","authors":"Xuan Zhou, Yuedong Liu, Cunfeng Wei, Qiong Xu","doi":"10.3233/XST-240023","DOIUrl":"10.3233/XST-240023","url":null,"abstract":"<p><strong>Background: </strong>Geometry calibration for robotic CT system is necessary for obtaining acceptable images under the asynchrony of two manipulators.</p><p><strong>Objective: </strong>We aim to evaluate the impact of different types of asynchrony on images and propose a reference-free calibration method based on a simplified geometry model.</p><p><strong>Methods: </strong>We evaluate the impact of different types of asynchrony on images and propose a novel calibration method focused on asynchronous rotation of robotic CT. The proposed method is initialized with reconstructions under default uncalibrated geometry and uses grid sampling of estimated geometry to determine the direction of optimization. Difference between the re-projections of sampling points and the original projection is used to guide the optimization direction. Images and estimated geometry are optimized alternatively in an iteration, and it stops when the difference of residual projections is close enough, or when the maximum iteration number is reached.</p><p><strong>Results: </strong>In our simulation experiments, proposed method shows better performance, with the PSNR increasing by 2%, and the SSIM increasing by 13.6% after calibration. The experiments reveal fewer artifacts and higher image quality.</p><p><strong>Conclusion: </strong>We find that asynchronous rotation has a more significant impact on reconstruction, and the proposed method offers a feasible solution for correcting asynchronous rotation.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1239-1252"},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141601992","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":"Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN.","authors":"Lijia Zhi, Shaoyong Duan, Shaomin Zhang","doi":"10.3233/XST-240069","DOIUrl":"10.3233/XST-240069","url":null,"abstract":"<p><strong>Objective: </strong>Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy of image retrieval. Highly expressive feature vectors play a crucial role in the search process. In this paper, we propose an effective deep convolutional neural network (CNN) model to extract concise feature vectors for multiple semantic X-ray medical image retrieval.</p><p><strong>Methods: </strong>We build a feature pyramid based CNN model with ResNet50V2 backbone to extract multi-level semantic information. And we use the well-known public multiple semantic annotated X-ray medical image data set IRMA to train and test the proposed model.</p><p><strong>Results: </strong>Our method achieves an IRMA error of 32.2, which is the best score compared to the existing literature on this dataset.</p><p><strong>Conclusions: </strong>The proposed CNN model can effectively extract multi-level semantic information from X-ray medical images. The concise feature vectors can improve the retrieval accuracy of multi-semantic and unevenly distributed X-ray medical images.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1297-1313"},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731580","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":"Can AI generate diagnostic reports for radiologist approval on CXR images? A multi-reader and multi-case observer performance study.","authors":"Lin Guo, Li Xia, Qiuting Zheng, Bin Zheng, Stefan Jaeger, Maryellen L Giger, Jordan Fuhrman, Hui Li, Fleming Y M Lure, Hongjun Li, Li Li","doi":"10.3233/XST-240051","DOIUrl":"10.3233/XST-240051","url":null,"abstract":"<p><strong>Background: </strong>Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming.</p><p><strong>Objective: </strong>To access the utility of a novel artificial intelligence (AI) system (MOM-ClaSeg) in enhancing the accuracy and efficiency of radiologists in detecting heterogenous lung abnormalities through a multi-reader and multi-case (MRMC) observer performance study.</p><p><strong>Methods: </strong>Over 36,000 CXR images were retrospectively collected from 12 hospitals over 4 months and used as the experiment group and the control group. In the control group, a double reading method is used in which two radiologists interpret CXR to generate a final report, while in the experiment group, one radiologist generates the final reports based on AI-generated reports.</p><p><strong>Results: </strong>Compared with double reading, the diagnostic accuracy and sensitivity of single reading with AI increases significantly by 1.49% and 10.95%, respectively (P < 0.001), while the difference in specificity is small (0.22%) and without statistical significance (P = 0.255). Additionally, the average image reading and diagnostic time in the experimental group is reduced by 54.70% (P < 0.001).</p><p><strong>Conclusion: </strong>This MRMC study demonstrates that MOM-ClaSeg can potentially serve as the first reader to generate the initial diagnostic reports, with a radiologist only reviewing and making minor modifications (if needed) to arrive at the final decision. It also shows that single reading with AI can achieve a higher diagnostic accuracy and efficiency than double reading.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1465-1480"},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the effectiveness of multi-view slice correction strategy based on deep learning in high pitch helical CT reconstruction.","authors":"Zihan Deng, Zhisheng Wang, Legeng Lin, Demin Jiang, Junning Cui, Shunli Wang","doi":"10.3233/XST-240128","DOIUrl":"10.3233/XST-240128","url":null,"abstract":"<p><strong>Background: </strong>Recent studies have explored layered correction strategies, employing a slice-by-slice approach to mitigate the prominent limited-view artifacts present in reconstructed images from high-pitch helical CT scans. However, challenges persist in determining the angles, quantity, and sequencing of slices.</p><p><strong>Objective: </strong>This study aims to explore the optimal slicing method for high pitch helical scanning 3D reconstruction. We investigate the impact of slicing angle, quantity, order, and model on correction effectiveness, aiming to offer valuable insights for the clinical application of deep learning methods.</p><p><strong>Methods: </strong>In this study, we constructed and developed a series of data-driven slice correction strategies for 3D high pitch helical CT images using slice theory, and conducted extensive experiments by adjusting the order, increasing the number, and replacing the model.</p><p><strong>Results: </strong>The experimental results indicate that indiscriminately augmenting the number of correction directions does not significantly enhance the quality of 3D reconstruction. Instead, optimal reconstruction outcomes are attained by aligning the final corrected slice direction with the observation direction.</p><p><strong>Conclusions: </strong>The data-driven slicing correction strategy can effectively solve the problem of artifacts in high pitch helical scanning. Increasing the number of slices does not significantly improve the quality of the reconstruction results, ensuring that the final correction angle is consistent with the observation angle to achieve the best reconstruction quality.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1535-1552"},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866165","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}