Magnetic resonance imaging最新文献

{"title":"Complex-valued image reconstruction for compressed sensing MRI using hierarchical constraint","authors":"Xue Bi ,&nbsp;Xinwen Liu ,&nbsp;Zhifeng Chen ,&nbsp;Hongli Chen ,&nbsp;Yajun Du ,&nbsp;Huizu Chen ,&nbsp;Xiaoli Huang ,&nbsp;Feng Liu","doi":"10.1016/j.mri.2024.110267","DOIUrl":"10.1016/j.mri.2024.110267","url":null,"abstract":"<div><div>In Magnetic Resonance Imaging (MRI), the sequential acquisition of raw complex-valued image data in Fourier space, also known as k-space, results in extended examination times. To speed up the MRI scans, k-space data are usually undersampled and processed using numerical techniques such as compressed sensing (CS). While the majority of CS-MRI algorithms primarily focus on magnitude images due to their significant diagnostic value, the phase components of complex-valued MRI images also hold substantial importance for clinical diagnosis, including neurodegenerative diseases. In this work, complex-valued MRI reconstruction is studied with a focus on the simultaneous reconstruction of both magnitude and phase images. The proposed algorithm is based on the nonsubsampled contourlet transform (NSCT) technique, which offers shift invariance in images. Instead of directly transforming the complex-valued image into the NSCT domain, we introduce a wavelet transform within the NSCT domain, reducing the size of the sparsity of coefficients. This two-level hierarchical constraint (HC) enforces sparse representation of complex-valued images for CS-MRI implementation. The proposed HC is seamlessly integrated into a proximal algorithm simultaneously. Additionally, to effectively minimize the artifacts caused by sub-sampling, thresholds related to different sub-bands in the HC are applied through an alternating optimization process. Experimental results show that the novel method outperforms existing CS-MRI techniques in phase-regularized complex-valued image reconstructions.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110267"},"PeriodicalIF":2.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142503196","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":"Dual-tuned floating solenoid balun for multi-nuclear MRI and MRS","authors":"Yijin Yang ,&nbsp;Boqiao Zhang ,&nbsp;Ming Lu ,&nbsp;Xinqiang Yan","doi":"10.1016/j.mri.2024.110268","DOIUrl":"10.1016/j.mri.2024.110268","url":null,"abstract":"<div><div>Common-mode currents can degrade the RF coil performance and introduce potential safety hazards in MRI. Baluns are the standard method to suppress these undesired common-mode currents. Specifically, floating baluns are preferred in many applications because they are removable, allow post-installation adjustment and avoid direct soldering on the cable. However, floating baluns are typically bulky to achieve excellent common-mode suppression, taking up valuable space in the MRI bore. This is particularly severe for multi-nuclear MRI/MRS applications, as two RF systems exist. In this work, we present a novel dual-tuned floating balun that is fully removable, does not require any physical connection to the coaxial cable, and has a significantly reduced footprint. The floating design employs an inductive coupling between the cable solenoid and a floating solenoid resonator rather than a direct physical connection. Unlike the previous floating solenoid balun, this balun employs a two-layer design further to improve the mutual coupling between the two solenoids. A pole-insertion method is used to suppress common-mode currents at two user-selectable frequencies simultaneously. Bench testing of the fabricated device at 7 T demonstrated high common-mode rejection ratios at Larmor frequencies of both ¹H and ²³Na, even with a compact dimension (diameter 18 mm and length 12 mm). This balun's removable, compact, and multi-resonant nature enables light-weighting, allows more coil elements, and improves cable management for advanced multi-nuclear MRI/MRS systems.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110268"},"PeriodicalIF":2.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142503200","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":"Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy","authors":"Hsiang-Yu Yu ,&nbsp;Cheng Jui Tsai ,&nbsp;Tse-Hao Lee ,&nbsp;Hsin Tung ,&nbsp;Yen-Cheng Shih ,&nbsp;Chien-Chen Chou ,&nbsp;Cheng-Chia Lee ,&nbsp;Po-Tso Lin ,&nbsp;Syu-Jyun Peng","doi":"10.1016/j.mri.2024.110256","DOIUrl":"10.1016/j.mri.2024.110256","url":null,"abstract":"<div><h3>Background</h3><div>Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults.</div><div>Most atrophic hippocampi can be identified using MRI based on standard epilepsy protocols; however, difficulties can arise in cases where sclerotic changes in the hippocampus are subtle or non-epilepsy-specific protocols have been implemented. In such cases, quantitative methods, such as T1-weighted axial series MRIs, are valuable additional tools to complement epilepsy-specific protocols. In the current study, we applied machine learning (ML) techniques to the analysis of brain regions of interest (ROIs), including the hippocampus, thalamus, and cortical areas, to enhance the accuracy of lesion lateralization in MRI.</div></div><div><h3>Methods</h3><div>This study included 104 patients diagnosed with mTLE, including 55 with lesions on the right side and 49 with lesions on the left side. FreeSurfer software was used to extract features from high-resolution T1-weighted axial brain MRI scans for use in computing lateralization indices (LI) for various brain regions. After using feature selection to pinpoint critical ROIs, the corresponding LI values were used as parameters in training the ML model.</div></div><div><h3>Results</h3><div>The proposed ML model demonstrated exceptional performance in the lateralization of mTLE, achieving test accuracy of 92.38 % with an AUROC of 0.97.</div></div><div><h3>Conclusion</h3><div>This study demonstrated the efficacy of ML in detecting instances of MTS from thin-slice T1 images. The proposed method provides valuable insights for surgical planning and treatment. Nonetheless, additional research will be required to enhance the robustness of the model and rigorously validate its effectiveness and applicability in clinical settings.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110256"},"PeriodicalIF":2.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469127","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":"Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power","authors":"Chase J. Sakitis,&nbsp;Daniel B. Rowe","doi":"10.1016/j.mri.2024.110252","DOIUrl":"10.1016/j.mri.2024.110252","url":null,"abstract":"<div><div>In fMRI, capturing brain activity during a task is dependent on how quickly the <em>k</em>-space arrays for each volume image are obtained. Acquiring the full <em>k</em>-space arrays can take a considerable amount of time. Under-sampling <em>k</em>-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of <em>k</em>-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled <em>k</em>-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from <em>a priori k</em>-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110252"},"PeriodicalIF":2.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469125","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":"Deep plug-and-play MRI reconstruction based on multiple complementary priors","authors":"Jianmin Wang ,&nbsp;Chunyan Liu ,&nbsp;Yuxiang Zhong ,&nbsp;Xinling Liu ,&nbsp;Jianjun Wang","doi":"10.1016/j.mri.2024.110244","DOIUrl":"10.1016/j.mri.2024.110244","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method has become an important technical means to accelerate MRI by reducing the data sampling rate while maintaining high-quality imaging. However, traditional undersampling reconstruction techniques such as compressed sensing mainly rely on relatively single sparse or low-rank prior information to reconstruct the image, which has limitations in capturing the comprehensive features of images, resulting in the insufficient performance of the reconstructed image in terms of details and key information. In this paper, we propose a deep plug-and-play multiple complementary priors MRI reconstruction model, which combines traditional low-rank matrix recovery model methods and deep learning methods, and integrates global, local and nonlocal priors to improve reconstruction quality. Specifically, we capture the global features of the image through the matrix nuclear norm, and use the deep convolutional neural network denoiser Swin-Conv-UNet (SCUNet) and block-matching and 3-D filtering (BM3D) algorithm to preserve the local details and structural texture of the image, respectively. In addition, we utilize an efficient half-quadratic splitting (HQS) algorithm to solve the proposed model. The experimental results show that our proposed method has better reconstruction ability than the existing popular methods in terms of visual effects and numerical results.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110244"},"PeriodicalIF":2.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469126","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":"Advance in the application of 4-dimensional flow MRI in atrial fibrillation","authors":"Junxian Liao ,&nbsp;Hongbiao Sun ,&nbsp;Xin Chen,&nbsp;Qinling Jiang,&nbsp;Yuxin Cheng,&nbsp;Yi Xiao","doi":"10.1016/j.mri.2024.110254","DOIUrl":"10.1016/j.mri.2024.110254","url":null,"abstract":"<div><div>Atrial fibrillation (AF) is the most prevalent arrhythmia in world-wild places and is associated with the development of severe secondary complications such as heart failure and stroke. Emerging evidence shows that the modified hemodynamic environment associated with AF can cause altered flow patterns in left atrial and even systemic blood associated with left atrial appendage thrombosis. Recent advances in magnetic resonance imaging (MRI) allow for the comprehensive visualization and quantification of in vivo aortic flow pattern dynamics. In particular, the technique of 4- dimensional flow MRI (4D flow MRI) offers the opportunity to derive advanced hemodynamic measures such as velocity, vortex, endothelial cell activation potential, and kinetic energy. This review introduces 4D flow MRI for blood flow visualization and quantification of hemodynamic metrics in the setting of AF, with a focus on AF and associated secondary complications.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110254"},"PeriodicalIF":2.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441941","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":"A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers","authors":"Yue Jiang ,&nbsp;Karan Punjabi ,&nbsp;Iain Pierce ,&nbsp;Daniel Knight ,&nbsp;Tina Yao ,&nbsp;Jennifer Steeden ,&nbsp;Alun D. Hughes ,&nbsp;Vivek Muthurangu ,&nbsp;Rhodri Davies","doi":"10.1016/j.mri.2024.110253","DOIUrl":"10.1016/j.mri.2024.110253","url":null,"abstract":"<div><h3>Background</h3><div>The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening routine cardiac scans and in population studies.</div></div><div><h3>Methods</h3><div>A 3D U-Net, U-Net_LR, was used to create 3D isotropic segmentations of the aorta from standard anisotropic 2D trans-axial localizers with low through-plane resolution. Training data was generated from high-resolution 3D isotropic whole heart images by simulating anisotropic images that resemble the low-resolution 2D localizers (the inputs). These inputs were paired with 3D isotropic ‘ground truth’ segmentation masks (the targets) created by a clinician from the high-resolution isotropic images. Segmentation quality was evaluated using an external dataset from UK Biobank. Segmentation accuracy was measured against ground-truth segmentations from concurrently acquired cardiac-triggered, respiratory-gated, high-resolution 3D isotropic whole heart images. Finally, the proposed method was compared to U-Net_HR, a 3D U-Net variant trained directly on high-resolution 3D isotropic images. A second observer was recruited to investigate the interobserver variability.</div></div><div><h3>Results</h3><div>Qualitative validation on an external dataset (UK Biobank) of 180 subjects showed that 93 % of 3D segmentations with the proposed model (U-Net_LR) were considered suitable for clinical use. In quantitative analysis, the proposed method (U-Net_LR) showed good agreement with ground-truth segmentations from isotropic 3D images with a mean DICE score of 0.9, which is no difference from automated segmentations made directly on the high-resolution 3D isotropic aorta images (U-Net_HR). When comparing measurements, there is no significant difference between U-Net_LR, U-Net_HR and two clinical observers in the diameter measurements at the mid ascending aorta, mid aortic arch, and descending aorta.</div></div><div><h3>Conclusions</h3><div>A new method of producing isotropic 3D aortic segmentations from routine CMR 2D anisotropic localizers shows good agreement with segmentation made directly from 3D isotropic images. The method has the potential to be used as a simple screening method for aortic aneurysms without the need for additional sequences.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110253"},"PeriodicalIF":2.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469124","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":"Comparisons of MR and EM inferred tissue microstructure properties using a human autopsy corpus callosum sample","authors":"Emma Friesen ,&nbsp;Rubeena Gosal ,&nbsp;Sheryl Herrera ,&nbsp;Morgan Mercredi ,&nbsp;Richard Buist ,&nbsp;Kant Matsuda ,&nbsp;Melanie Martin","doi":"10.1016/j.mri.2024.110255","DOIUrl":"10.1016/j.mri.2024.110255","url":null,"abstract":"<div><div>Degeneration of white matter (WM) microstructure in the central nervous system is characteristic of many neurodegenerative conditions. Previous research indicates that axonal degeneration visible in <em>ex vivo</em> electron microscopy (EM) photomicrographs precede the onset of clinical symptoms. Measuring WM microstructural features, such as axon diameter and packing fraction, currently require these highly invasive methods of analysis and it is therefore of great importance to develop methods for <em>in vivo</em> measurements. Diffusion weighted Magnetic Resonance Imaging (MRI) is a non-invasive method which can be used in conjunction with temporal diffusion spectroscopy (TDS) and an oscillating gradient spin echo (OGSE) pulse sequence to probe micron-scale structures within neural tissue. The current experiment aims to compare axon diameter measurements, mean effective axon diameter (<span><math><mover><mi>AxD</mi><mo>¯</mo></mover></math></span><strong>)</strong>, and packing fractions calculated from EM histopathological analysis and inferred values from MR images. Mathematical models of axon diameters used for analysis include the ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD) model and the AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) model using ROI (Region of Interest) based analysis (RBA) and voxel-based analysis (VBA), respectively. Overall, it was observed that MRI inferred WM microstructural parameters overestimate those calculated from EM. This may be attributable to tissue shrinkage during EM dehydration, the sensitivity of MR pulse sequences to larger diameter axons, and/or inaccurate model assumptions. The results of the current study provide a means to characterize the precision and accuracy of RBA-ACD and VBA-AAD OGSE-TDS and highlight the need for further research investigating the relationship between <em>ex vivo</em> MRI and EM, with the goal of reaching <em>in vivo</em> MRI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110255"},"PeriodicalIF":2.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469128","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":"An accelerated alternating direction method of multiplier for MRI with TV regularisation","authors":"ZhiBin Zhu ,&nbsp;YueHong Ding ,&nbsp;Ying Liu ,&nbsp;JiaQi Huang","doi":"10.1016/j.mri.2024.110249","DOIUrl":"10.1016/j.mri.2024.110249","url":null,"abstract":"<div><div>Compressed Sensing (CS) is important in the field of image processing and signal processing, and CS-Magnetic Resonance Imaging (MRI) is used to reconstruct image from undersampled k-space data. Total Variation (TV) regularisation is a common technique to improve the sparsity of image, and the Alternating Direction Multiplier Method (ADMM) plays a key role in the variational image processing problem. This paper aims to improve the quality of MRI and shorten the reconstruction time. We consider MRI to solve a linear inverse problem, we convert it into a constrained optimization problem based on TV regularisation, then an accelerated ADMM is established. Through a series of theoretical derivations, we verify that the algorithm satisfies the convergence rate of <span><math><mi>O</mi><mfenced><mrow><mn>1</mn><mo>/</mo><msup><mi>k</mi><mn>2</mn></msup></mrow></mfenced></math></span> under the condition that one objective function is quadratically convex and the other is strongly convex. We select five undersampled templates for testing in MRI experiment and compare it with other algorithms, experimental results show that our proposed method not only improves the running speed but also gives better reconstruction results.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110249"},"PeriodicalIF":2.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381241","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":"Self-navigated subspace reconstruction for real-time MR imaging of the vocal tract","authors":"Peng Cao ,&nbsp;Wenting Jiang ,&nbsp;Changhe Chen ,&nbsp;Yiang Wang ,&nbsp;Jonathan Havenhill","doi":"10.1016/j.mri.2024.110243","DOIUrl":"10.1016/j.mri.2024.110243","url":null,"abstract":"<div><div>Purpose: Real-time MRI offers a continuous and dynamic view of the object being imaged. Researchers have applied real-time MRI to speech production, which allows for the visualization of the vocal tract during speech.</div><div>Methods: This study proposed applying self-navigated subspace reconstruction for real-time vocal tract imaging. We performed experiments on a clinical 3 T MRI using standard RF coils and rapid acquisition. Additionally, 1000 frames were compressed during reconstruction to a few principal components, and iterative low-rank approximation was performed on compressed k-space, in conjunction with the orthogonal basis estimation for the subspace.</div><div>Results: The simulation study involving a 32-time acceleration showed that the proposed method produced a reasonably small root mean square error (RMSE) of 0.159, compared to 0.278 for sliding window reconstruction, 0.2527 for SToRM and 0.294 for low-rank reconstruction. The study also presented in vivo images of a typical sagittal image with a temporal resolution of 7 ms/frame or 21 ms/frame for the three-slice scan.</div><div>Conclusion: Our study presented a subspace reconstruction technique that does not require a navigator echo, which can be used for real-time MRI, particularly in speech imaging applications.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110243"},"PeriodicalIF":2.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381242","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}