Ultrasonic Imaging最新文献

{"title":"Abstracts for the 2022 Symposium on Ultrasonic Imaging and Tissue Characterization","authors":"Bret McCandless, Marie Muller","doi":"10.1177/01617346221107728","DOIUrl":"https://doi.org/10.1177/01617346221107728","url":null,"abstract":"The Field II ultrasound simulation developed by recently reached its 25-yearanniversary. In that time, its impact on the development of novel methods and systems for medical imaging is hard to overstate. This software has been made freely available to the ultrasound community as citation ware( > 2700 as of 2022) and is frequently updated to support modern versions of Matlab. I will provide a brief retrospec-tive on Field II including describing its simulation methods, capabilities, and limitations to put its use into context among a growing number of other simulation approaches for modern ultrasound research. This talk will highlight our group’s use of Field II in several areas of research to demonstrate how we leverage its linear simulation approach for fundamental acoustic studies. I will discuss best practices for simulation including generation of additive noise. I will demonstrate the combination of pre-computed targets for use in training machine learning applications. I will explore the use of the multistatic data set in the efficient creation and evaluation of various imaging sequences, especially for synthetic aperture imaging. Work from others that has been used to complement the capabilities of Field II will also be briefly introduced(e.g. introducing additive acoustic clutter models, generating imaging targets from natural images for machine learning, the use of simulated acoustic fields as input for mechanical simulations). sound speed Ultrasonic backscatter is associated to cardiac collagen deposition, while anisotropy in ultrasonic backscatter is associated with myo fiber alignment. Preliminary data from our lab suggested anisotropy in backscatter may be primarily associated with collagen that aligns parallel to myofibers, not the myofibers themselves. The purpose of the present study was to determine a relationship between myocardial collagen and anisotropy of ultrasonic backscatter in left ventricular short axis images. Hearts were excised from Sprague Dawley rats, aligned in the short axis with the anterior wall closest to the transducer, and perfused with a colla-genase-containing solution for either 10 (n=7) or 30 minutes (n=7)or control solution for 30 minutes(control n=8). Serial ultrasound images were acquired throughout collagenase digestion and ultrasonic backscatter was assessed where the collagen is primarily aligned perpendicular to the angle of insonification(anterior and posterior walls), and where collagen is primarily aligned parallel to the angle of insonification (lateral and septal walls). Our data suggested that collagenase digestion reduced backscatter anisotropy within the myocardium (p < 0.001)with the lateral and septal walls (collagen parallel to ultrasound) showing the greatest change in backscatter intensity. Histology (Trichrome staining) and biochemistry (hydroxyproline assay) suggests that collagen remains present but is crosslinking is altered within 10 minutes(p < 0.047). These data suggest the anis","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"2 1","pages":"162 - 186"},"PeriodicalIF":2.3,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77412606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Parameter Estimator of the Homodyned K Distribution Based on the Maximum Likelihood Method for Ultrasound Tissue Characterization","authors":"Yang Liu, Yufeng Zhang, Bingbing He, Zhiyao Li, Xun Lang, Hong Liang, Jianhua Chen","doi":"10.1177/01617346221097867","DOIUrl":"https://doi.org/10.1177/01617346221097867","url":null,"abstract":"The homodyned K distribution (HK) can generally describe the ultrasound backscatter envelope statistics distribution with parameters that have specific physical meaning. However, creating robust and reliable HK parameter estimates remains a crucial concern. The maximum likelihood estimator (MLE) usually yields a small variance and bias in parameter estimation. Thus, two recent studies have attempted to use MLE for parameter estimation of HK distribution. However, some of the statements in these studies are not fully justified and they may hinder the application of parameter estimation of HK distribution based on MLE. In this study, we propose a new parameter estimator for the HK distribution based on the MLE (i.e., MLE1), which overcomes the disadvantages of conventional MLE of HK distribution. The MLE1 was compared with other estimators, such as XU estimator (an estimation method based on the first moment of the intensity and tow log-moments) and ANN estimator (an estimation method based on artificial neural networks). We showed that the estimations of parameters α and k are the best overall (in terms of the relative bias, normalized standard deviation, and relative root mean squared errors) using the proposed MLE1 compared with the others based on the simulated data when the sample size was N = 1000. Moreover, we assessed the usefulness of the proposed MLE1 when the number of scatterers per resolution cell was high (i.e., α up to 80) and when the sample size was small (i.e., N = 100), and we found a satisfactory result. Tests on simulated ultrasound images based on Field II were performed and the results confirmed that the proposed MLE1 is feasible and reliable for the parameter estimation from the ultrasonic envelope signal. Therefore, the proposed MLE1 can accurately estimate the HK parameters with lower uncertainty, which presents a potential practical value for further ultrasonic applications.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"17 1","pages":"142 - 160"},"PeriodicalIF":2.3,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87784238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Speckle Reduction in Ultrasound Images of the Common Carotid Artery Based on Integer and Fractional-Order Total Variation","authors":"Kun Wang, Zhiyao Li, Yufeng Zhang","doi":"10.1177/01617346221096840","DOIUrl":"https://doi.org/10.1177/01617346221096840","url":null,"abstract":"Designing a technique with higher speckle noise suppressing capability, better edge preserving performance, and lower time complexity is a research objective for the common carotid artery (CCA) ultrasound despeckling. Total variation based techniques have been widely used in the image denoising and have good performance in preserving the edges in the images. However, the total variation based filters can produce the staircase artifacts. To address this issue, second-order total variation based techniques have been proposed for the image denoising. However, the previous study has been proved that the fractional differential model has better performance in reducing the speckles in ultrasound despeckling compared with the second-order model. Thus, to improve the performance of ultrasound despeckling and edge preserving, a novel despeckling model based on integer and fractional-order total variation (IFOTV) is proposed for CCA ultrasound images. Moreover, the minimization problems in our despeckling model are solved by the alternating direction method of multiplier (ADMM). In results with synthetic images, the edge preservation index (EPI) values of proposed method are 0.9524, 0.8797, and 0.7351 as well as 0.9137, 0.8253, and 0.6847 under three different levels of noise, which are the highest among four advanced methods. In results with simulated CCA ultrasound images, the speckle suppression and mean preservation indices of proposed method are 0.5596, 0.6571, and 0.8106 under three different levels of noise, which are the best among four advanced methods. In results with clinical images, the average absolute error of intima-media thickness measurements of proposed method is 0.0660 ± 0.0679 (mean ± std in mm), which is the lowest among four advanced methods. In conclusion, the IFOTV method has improved performance in suppressing the speckle noise and preserving the edge, and is thus a potential alternative to the current filters for the CCA ultrasound despeckling.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"1 1","pages":"123 - 141"},"PeriodicalIF":2.3,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83166230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac Segmentation Method Based on Domain Knowledge","authors":"Yingni Wang, Wenbin Chen, Tianhong Tang, Wenquan Xie, Yong Jiang, Huabin Zhang, Xiaobo Zhou, Kehong Yuan","doi":"10.1177/01617346221099435","DOIUrl":"https://doi.org/10.1177/01617346221099435","url":null,"abstract":"Echocardiography plays an important role in the clinical diagnosis of cardiovascular diseases. Cardiac function assessment by echocardiography is a crucial process in daily cardiology. However, cardiac segmentation in echocardiography is a challenging task due to shadows and speckle noise. The traditional manual segmentation method is a time-consuming process and limited by inter-observer variability. In this paper, we present a fast and accurate echocardiographic automatic segmentation framework based on Convolutional neural networks (CNN). We propose FAUet, a segmentation method serially integrated U-Net with coordinate attention mechanism and domain feature loss from VGG19 pre-trained on the ImageNet dataset. The coordinate attention mechanism can capture long-range dependencies along one spatial direction and meanwhile preserve precise positional information along the other spatial direction. And the domain feature loss is more concerned with the topology of cardiac structures by exploiting their higher-level features. In this research, we use a two-dimensional echocardiogram (2DE) of 88 patients from two devices, Philips Epiq 7C and Mindray Resona 7T, to segment the left ventricle (LV), interventricular septal (IVS), and posterior left ventricular wall (PLVW). We also draw the gradient weighted class activation mapping (Grad-CAM) to improve the interpretability of the segmentation results. Compared with the traditional U-Net, the proposed segmentation method shows better performance. The mean Dice Score Coefficient (Dice) of LV, IVS, and PLVW of FAUet can achieve 0.932, 0.848, and 0.868, and the average Dice of the three objects can achieve 0.883. Statistical analysis showed that there is no significant difference between the segmentation results of the two devices. The proposed method can realize fast and accurate segmentation of 2DE with a low time cost. Combining coordinate attention module and feature loss with the original U-Net framework can significantly increase the performance of the algorithm.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"33 1","pages":"105 - 117"},"PeriodicalIF":2.3,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81140367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing","authors":"Hua Yan, Yuankun Wei, Yinggang Zhou, Yifan Wang","doi":"10.1177/01617346221092695","DOIUrl":"https://doi.org/10.1177/01617346221092695","url":null,"abstract":"Acoustic tomography (AT) is one of a few non-contact measurement techniques that can present information about the temperature distribution. Its successful application greatly depends on the performance of the reconstruction algorithm. In this paper, a temperature distribution reconstruction method based on compressed sensing (CS) is proposed. Firstly, a measurement matrix of an AT system in a CS framework is established. Secondly, a sparse basis is selected based on the mutual coherence between the measurement matrix and sparse basis. Thirdly, an improvement of the orthogonal matching pursuit (OMP) algorithm, called the IMOMP algorithm, is proposed for pursuing efficiency in recovering sparse signals. Reconstruction experiments of Gaussian sparse signals showed that IMOMP was better than OMP in both success ratio and running time, and the selection method of sparse basis was effective. Finally, a temperature distribution reconstruction algorithm based on compressed sensing, that is, the CS-IMOMP algorithm, is proposed. Simulation and experiment results show that, compared with the least square algorithm and the Simultaneous Iterative Reconstruction Technique algorithm, the CS-IMOMP algorithm has smaller reconstruction errors and provides more accurate information about the temperature distribution.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"12 1","pages":"77 - 95"},"PeriodicalIF":2.3,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81951606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Spectrum-Beamformer for Conventional B-Mode Ultrasound Imaging System: Principle, Validation, and Robustness","authors":"Chen Jiang, Chengcheng Liu, Y. Zhan, Dean Ta","doi":"10.1177/01617346221085184","DOIUrl":"https://doi.org/10.1177/01617346221085184","url":null,"abstract":"Fast and efficient imaging techniques are important for real-time ultrasound imaging. The delay and sum (DAS) beamformer is the most widely-used strategy in focused ultrasound imaging (FUI) modality. However, calculating the time delays and coherently summing the amplitude response in DAS is computationally expensive and generally require a high-performance processor to realize real-time processing. In this study, an efficient spectrum beamformer, namely full-matrix capture (FMC)-stolt, is proposed in FUI system with a linear phased array. The imaging performance of FMC-stolt was validated with the point-scatter simulation and in vitro point and cyst phantoms, and then compared with that of five beamformers, that is, Multiline acquisition (MLA), retrospective transmit beamforming (RTB) in the FUI modality, as well as DAS, Garcia’s frequency-wavenumber (f-k), Lu’s f-k in the coherent plane wave compounding imaging (CPWCI) modality, under specific conditions. We show that the imaging performance of FMC-stolt is better than MLA-DAS in non-transmit-focal regions, and comparable with RTB-DAS at all imaging depths. FMC-stolt also shows better discontinuity alleviation than MLA and RTB. In addition, FMC-stolt has similar imaging characteristics (e.g., off-axis resolution, computational cost) as the f-k beamformers. The computational complexity and actual computational time indicate that FMC-stolt is comparable to Garcia’s f-k, Lu’s f-k, and faster than RTB and CPWCI-DAS if the transmitting numbers are close for FUI and CPWCI. The study demonstrates that the proposed FMC-stolt could achieve good reconstruction speed while preserving high-quality images and thus provide a choice for software beamforming for conventional B-mode ultrasound imaging, especially for hand-held devices with limited performance processors.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"1 1","pages":"59 - 76"},"PeriodicalIF":2.3,"publicationDate":"2022-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91128204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation.","authors":"Vahid Ashkani Chenarlogh,&nbsp;Mostafa Ghelich Oghli,&nbsp;Ali Shabanzadeh,&nbsp;Nasim Sirjani,&nbsp;Ardavan Akhavan,&nbsp;Isaac Shiri,&nbsp;Hossein Arabi,&nbsp;Morteza Sanei Taheri,&nbsp;Mohammad Kazem Tarzamni","doi":"10.1177/01617346211069882","DOIUrl":"https://doi.org/10.1177/01617346211069882","url":null,"abstract":"<p><p>U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normalization layers as its main layers. There are four blocks in the encoder-decoder path. The results of our proposed architecture were evaluated using a prepared dataset for head circumference and abdominal circumference segmentation tasks, and a public dataset (HC18-Grand challenge dataset) for fetal head circumference measurement. The proposed fast network significantly improved the processing time in comparison with U-Net, dilated U-Net, R2U-Net, attention U-Net, and MFP U-Net. It took 0.47 seconds for segmenting a fetal abdominal image. In addition, over the prepared dataset using the proposed accurate model, Dice and Jaccard coefficients were 97.62% and 95.43% for fetal head segmentation, 95.07%, and 91.99% for fetal abdominal segmentation. Moreover, we have obtained the Dice and Jaccard coefficients of 97.45% and 95.00% using the public HC18-Grand challenge dataset. Based on the obtained results, we have concluded that a fine-tuned and a simple well-structured model used in clinical devices can outperform complex models.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"44 1","pages":"25-38"},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39788975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of Quantitative Tissue Characterization Using Novel Parameters Extracted From Photoacoustic Power Spectrum Considering Multiple Absorbers.","authors":"Nikita Rathi,&nbsp;Saugata Sinha,&nbsp;Bhargava Chinni,&nbsp;Vikram Dogra,&nbsp;Navalgund Rao","doi":"10.1177/01617346211055053","DOIUrl":"https://doi.org/10.1177/01617346211055053","url":null,"abstract":"<p><p>Frequency domain analysis of radio frequency signal is performed to differentiate between different tissue categories in terms of spectral parameters. However, due to complex relationship between the absorber size and spectral parameters, they cannot be used for quantitative tissue characterization. In an earlier study, we showed that using linear relationship between absorber size and two new spectral parameters namely number of lobes and average lobe width, absorber size can be successfully recovered from photoacoustic signal generated by single absorber. As actual biological tissue contains multiple absorbers, in this study we extended the application of these two new spectral parameters for computing absorber size from signals generated by multiple PA absorbers. We revisited our analytical model to establish two new linear relationships between the absorber radius and number of lobes as well as average lobe width considering multiple absorbers with bandlimited acquisition. A simulation study was performed to validate these linear relationships. A retrospective ex vivo study, in which the spectral parameters were computed using multiwavelength photoacoustic signals, was performed with freshly exercised thyroid specimens from 38 actual human patients undergoing thyroidectomy after having a diagnosis of suspected thyroid lesions. From statistical analysis it is shown that both the parameters were significantly different between malignant and non-malignant thyroid and malignant and normal thyroid tissue. Performance of the supervised classification with the computed spectral parameters showed that the extracted parameters could be successfully used to differentiate malignant thyroid tissue from normal thyroid tissue with reasonable degree of accuracy.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"44 1","pages":"13-24"},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39823906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images.","authors":"Jignesh Chowdary,&nbsp;Pratheepan Yogarajah,&nbsp;Priyanka Chaurasia,&nbsp;Velmathi Guruviah","doi":"10.1177/01617346221075769","DOIUrl":"https://doi.org/10.1177/01617346221075769","url":null,"abstract":"<p><p>Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by <math><mrow><mn>1</mn><mo>.</mo><mn>08</mn><mo>%</mo></mrow></math>, <math><mrow><mn>4</mn><mo>.</mo><mn>13</mn><mo>%</mo></mrow></math>, and classification by <math><mrow><mn>1</mn><mo>.</mo><mn>16</mn><mo>%</mo></mrow></math>, <math><mrow><mn>2</mn><mo>.</mo><mn>34</mn><mo>%</mo></mrow></math>, respectively than the methods available in the literature.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"44 1","pages":"3-12"},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8f/98/10.1177_01617346221075769.PMC8902030.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39593634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography-Performance Study In Silico.","authors":"Aleksandra Wilczewska,&nbsp;Szymon Cygan,&nbsp;Jakub Żmigrodzki","doi":"10.1177/01617346211068812","DOIUrl":"https://doi.org/10.1177/01617346211068812","url":null,"abstract":"<p><p>Although the two dimensional Speckle Tracking Echocardiography has gained a strong position among medical diagnostic techniques in cardiology, it still requires further developments to improve its repeatability and reliability. Few works have attempted to incorporate the left ventricle segmentation results in the process of displacements and strain estimation to improve its performance. We proposed the use of mask information as an additional penalty in the elastic image registration based displacements estimation. This approach was studied using a short axis view synthetic echocardiographic data, segmented using an active contour method. The obtained masks were distorted to a different degree, using different methods to assess the influence of the segmentation quality on the displacements and strain estimation process. The results of displacements and circumferential strain estimations show, that even though the method is dependent on the mask quality, the potential loss in accuracy due to the poor segmentation quality is much lower than the potential accuracy gain in cases where the segmentation performs well.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"44 1","pages":"39-54"},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39827094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}