Magnetic resonance imaging最新文献

{"title":"Noninvasive assessment of renal allograft function with multiparametric MRI: DWI, DTI, and arterial spin labeling compared with eGFR.","authors":"Wenxin Li, Feng Gao, Jun Zhang, Xiaolu Liu, Meng Li, Tian Gan","doi":"10.1016/j.mri.2026.110818","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110818","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and arterial spin labeling (ASL) for noninvasive assessment of renal allograft function using estimated glomerular filtration rate (eGFR) as the reference.</p><p><strong>Methods: </strong>Eighty-four kidney transplant recipients and 30 age-matched healthy volunteers underwent mono-exponential DWI, DTI, and pseudocontinuous ASL on a 1.5-T MRI scanner. Cortical and medullary apparent diffusion coefficient (ADC), fractional anisotropy (FA), and cortical renal blood flow (RBF) were measured. Recipients were grouped by eGFR, and intergroup differences, correlations with eGFR, and receiver operating characteristic performance were analyzed.</p><p><strong>Results: </strong>Cortical ADC, medullary ADC, and cortical RBF decreased with worsening allograft function and correlated positively with eGFR (r = 0.513, 0.489, and 0.615, respectively; all p < 0.001). Cortical and medullary FA were not significantly correlated with eGFR. Cortical RBF showed the highest single-parameter area under the curve for differentiating group 1 from group 2 (0.919; sensitivity, 95.7%; specificity, 71.4%) and group 1 from group 3 (0.969; sensitivity, 85.7%; specificity, 100%). When distinguishing group 1 from group 2, the AUCs obtained by combining cortical RBF with cortical ADC or medullary ADC, as well as the combination of all three parameters, were significantly higher than those of cortical ADC alone or medullary ADC alone (P < 0.05).</p><p><strong>Conclusion: </strong>DWI-derived ADC and ASL-derived cortical RBF are useful noninvasive MRI markers of renal allograft function, with cortical RBF showing the strongest diagnostic performance. DTI-derived FA appeared exploratory in this cohort.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110818"},"PeriodicalIF":2.1,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224388","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":"Hippocampal alterations in rats of early-stage hyperuricemia using diffusion kurtosis imaging: A preliminary study.","authors":"Ya-Lin Wang, Zhe Sun, Jin-Lu Song, Min-Jie Liang, You-Zhen Feng, Zhong-Yuan Cheng, Xiang-Ran Cai","doi":"10.1016/j.mri.2026.110821","DOIUrl":"10.1016/j.mri.2026.110821","url":null,"abstract":"<p><strong>Background: </strong>Although several quantitative methods are available for evaluating hyperuricemia-associated brain dysfunction, diffusion kurtosis imaging (DKI) exhibits greater sensitivity to subtle microstructural tissue changes, potentially enabling earlier detection of damage.</p><p><strong>Purpose: </strong>To investigate the feasibility of hyperuricemia on cognitive impairment in rats using DKI.</p><p><strong>Materials and methods: </strong>Fifty male rats were randomly divided into the normal control (CON) group and hyperuricemia model (HUA) group. Ten rats from each group were randomly selected and underwent longitudinal DKI scanning at baseline (week 0) and at weeks 2, 4, 6, and 8. DKI indicators were acquired with selecting bilateral symmetric regions of interest (ROI) in the hippocampal region, including, mean diffusivity (MD), fractional anisotropy (FA) and mean kurtosis (MK). The HE staining, GFAP and Iba-1 immunofluorescence staining were employed to reveal hippocampal histopathological changes. Additionally, the Morris water maze (MWM) test was applied to assess cognitive ability of rats at week 8th.</p><p><strong>Result: </strong>In this study, the MD and FA values of hippocampal in HUA group exhibited a gradually decreasing trend, while the MK value gradually increased. Longitudinal analysis revealed earlier MK differences (weeks 4th-8th) and later FA divergences (weeks 6th-8th) between HUA and CON groups (p < 0.05). During weeks 4th-8th, the relative fluorescence intensity (RFI) of GFAP and, Iba-1 exhibited an inverse trend with FA value, whereas their changing trend was similar to that of MK value. Compared with controls, HUA rats showed the progressively longer latencies across training days, and fewer traversals and shorter durations of the platform in spatial exploration experiments.</p><p><strong>Conclusion: </strong>DKI imaging was a non-invasive and sensitive method for identifying the HUA-induced hippocampal alterations and cognitive damage.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110821"},"PeriodicalIF":2.1,"publicationDate":"2026-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220163","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":"Adaptive local region super-resolution for high-field MRI: A diffusion-based approach with structural and frequency-domain constraints.","authors":"Jianwei Zuo, Huifang Yang, Yumeng Tang, Xiaoyan Wang, Kai Zhong, Xin Peng, Jiajia Luo","doi":"10.1016/j.mri.2026.110815","DOIUrl":"10.1016/j.mri.2026.110815","url":null,"abstract":"<p><p>High-field MRI offers superior SNR but is constrained by the trade-off between spatial resolution and acquisition time. Existing global deep learning super-resolution (SR) methods often incur computational overhead by ignoring regional image content. This motivates an adaptive local SR strategy to optimize efficiency and detail preservation. To address this, we developed a local SR method that adaptively allocates computational resources to information-rich regions. The proposed framework regularized an entropy-based local region processing strategy with a diffusion model fortified by frequency-domain and structural constraints. We retrospectively evaluated our method on two diverse datasets: a proprietary 7.0 T dental MRI dataset (25 T2-weighted DESS sequences from 10 beagle dogs) and a public 5.0 T brain MRI dataset (Diff5T, 50 diffusion-weighted sequences). Image quality was quantified using PSNR, SSIM, LPIPS, and FID. On the 7.0 T dental test dataset (2 subjects), our method achieved a PSNR of 27.31 [26.83, 27.79] dB and the lowest LPIPS of 0.19 [0.18, 0.20]. On the 5.0 T brain test dataset (10 subjects), the method achieved a PSNR of 27.15 ± 0.10 dB, an LPIPS of 0.14 ± 0.00 and an FID of 54.01, with lower perceptual metrics than the evaluated baselines. Ablation studies indicated that the proposed frequency-domain and structural constraints reduced LPIPS and FID, with trade-offs in PSNR and SSIM. In conclusion, the evaluated framework showed improvements in perceptual metrics by integrating targeted local processing with frequency-domain and structural regularization, accompanied by a trade-off in pixel-wise fidelity metrics, supporting the technical feasibility of addressing the resolution-time trade-off in high-field MRI. Future work will focus on expanding the dataset and evaluating the framework in human dental cohorts to address anatomical differences between the current canine model and human dental anatomy.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110815"},"PeriodicalIF":2.1,"publicationDate":"2026-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220437","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":"Determining the turbulent energy spectrum tensor using multidimensional diffusion MRI.","authors":"Daniel Clarke, Daniel Topgaard","doi":"10.1016/j.mri.2026.110816","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110816","url":null,"abstract":"<p><p>Turbulent flows are of interest for various engineering and medical applications. Pulsed field gradient diffusion encoding sequences quantify overall turbulent velocity fluctuations, but do not resolve spectral characteristics of the flow. Modulated gradient sequences have the potential to measure the diffusion spectrum but are limited by unidirectional encoding. In this article, we explore multidimensional diffusion encoding via the double-rotation of the q vector as a tool to study anisotropic turbulent flow. We demonstrate that encoding durations beyond the mean correlation time were required to satisfy the assumption of a Gaussian phase distribution. The diffusion spectrum tensor was reconstructed with reasonable accuracy. However, the assumption of single-peak encoding spectra did not hold. The development of advanced data inversion protocols is required to derive detailed insights into turbulent flow phenomena.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110816"},"PeriodicalIF":2.1,"publicationDate":"2026-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220169","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 hierarchical residual Swin transformer with dual-level locality compensation for accelerated MRI reconstruction.","authors":"Jinhong Huang, Guanglong Sun, Xinzhen Li, Zihan Yang, Genjiao Zhou","doi":"10.1016/j.mri.2026.110820","DOIUrl":"10.1016/j.mri.2026.110820","url":null,"abstract":"<p><p>Magnetic resonance imaging (MRI) provides high spatial resolution, excellent soft-tissue contrast, and reliable anatomical discrimination, and is therefore widely used in clinical diagnosis. However, long acquisition time still limits imaging efficiency and clinical throughput. Although convolutional neural network (CNN)-based deep learning approaches have shown considerable promise for reconstructing images from undersampled k-space data, existing methods frequently fail to preserve fine structural details and texture continuity under aggressive undersampling conditions. In this study, we propose a novel reconstruction network based on the Swin Transformer, which integrates three key components: (i) long residual connections to facilitate the recovery of high-frequency details; (ii) a dual-level locality compensation mechanism to restore local spatial information; and (iii) a channel recalibration module to explicitly capture global dependencies among channels, thereby enhancing global feature extraction. Extensive experiments conducted on the IXI and MICCAI 2013 datasets demonstrate that the proposed method consistently outperforms several compared generative adversarial network (GAN)-based approaches for MRI reconstruction across all undersampling mask types, achieving a PSNR improvement of approximately 0.6-1 dB over the best competing method under various settings. The implementation code is available at https://github.com/cvgjhuor/HR-SwinGAN.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110820"},"PeriodicalIF":2.1,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216406","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 multimodal MRI-based study of early-stage Parkinson's disease across age-at-onset groups.","authors":"Guanchen Liu, Yuan Tian, Dong Nan, Ying Shi, Peng Sun, Hongxia Zhang, Yingzhe Cui, Pengfei Liu","doi":"10.1016/j.mri.2026.110822","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110822","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate distinct iron deposition and neurite microstructural alterations in deep gray matter nuclei between early-onset Parkinson's disease (EOPD) and late-onset Parkinson's disease (LOPD) patients using quantitative susceptibility mapping (QSM) and neurite orientation dispersion and density imaging (NODDI), and to evaluate the ability of these quantitative metrics to distinguish EOPD and LOPD from their corresponding healthy controls.</p><p><strong>Methods: </strong>Fifty-three early-stage PD patients (15 EOPD and 38 LOPD) and 58 healthy controls (17 young healthy controls and 41 older healthy controls) underwent multimodal MRI. QSM-derived magnetic susceptibility and NODDI-derived parameters (NDI, Viso, ODI) were analyzed in the substantia nigra pars compacta (SNc), substantia nigra pars reticulata (SNr), red nucleus (RN), globus pallidus (GP), putamen (PT), and caudate nucleus (CN).</p><p><strong>Results: </strong>In EOPD, the iron content in deep gray matter nuclei was not significantly different from that in young healthy controls, but ODI was reduced in the left SNc. In LOPD, increased QSM values were observed in bilateral SNc, PT, and left CN, alongside decreased NDI and increased Viso in the left SNc, and reduced ODI in bilateral SNc and PT. Multivariate logistic regression analysis revealed that the combination of the NDI and ODI in the left SNc could effectively differentiate patients with EOPD from young healthy controls, whereas the optimal combination for distinguishing patients with LOPD from older healthy controls consisted of the NDI, ODI, and QSM in the left SNc.</p><p><strong>Conclusion: </strong>Multimodal MRI revealed distinct spatial involvement patterns in early-stage EOPD and LOPD and showed potential for distinguishing PD patients from their corresponding healthy controls.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110822"},"PeriodicalIF":2.1,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216423","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":"Multicenter external validation of a multimodal MRI deep-learning model to predict meningioma bone invasion.","authors":"Zhouyuan Wei, Wei Hu, Fuqiang Tang, Jialiang Ren, Junlin Zhou, Mengjia Liu, Ling Su, Jing Zhang","doi":"10.1016/j.mri.2026.110819","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110819","url":null,"abstract":"<p><strong>Background: </strong>Preoperative detection of meningioma bone invasion lacks objective biomarkers and relies on subjective radiological assessment, posing a risk of excessive bone resection or residual tumor. We therefore developed and externally validated a parsimonious six-feature DenseNet121 signature derived from multiparametric MRI across two independent institutions.</p><p><strong>Methods: </strong>In this retrospective diagnostic accuracy study, 438 consecutive patients (mean age, 53 ± 11 years; 77% women) with histologically confirmed meningioma were allocated to training (n = 182), internal validation (n = 77), and external test (n = 179) sets. Following N4 bias-field correction and isotropic resampling, contrast-enhanced T1-weighted, T2-weighted, and diffusion-weighted volumes were fed into a DenseNet121 architecture to extract a six-feature signature via nested cross-validation. Discrimination (area under the ROC curve, AUC), calibration (Hosmer-Lemeshow test), and clinical utility (decision-curve analysis) were evaluated.</p><p><strong>Results: </strong>In the external cohort (prevalence 11%), the signature achieved an AUC of 0.82 (95% CI 0.73-0.91), sensitivity 75%, specificity 77% and negative predictive value 96%, with an AUC improvement of 0.155 over conventional radiomics (95% CI, 0.01-0.30; p = 0.035). At a 20% risk threshold, decision-curve analysis confirmed a positive net clinical benefit, primarily driven by the high negative predictive value. SHAP analysis demonstrated the dominant and consistent contribution of low-ADC features.</p><p><strong>Conclusion: </strong>The DenseNet121-derived six-feature signature serves as a reliable rule-out aid for preoperative meningioma bone invasion, with a high negative predictive value that may safely support decisions to avoid extensive bone resection in predicted-negative cases. Positive predictions require confirmatory CT or intraoperative biopsy to mitigate false positives. This tool could assist nurses and surgeons in preoperative planning, facilitating personalized patient care and potentially reducing operative time and complications.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110819"},"PeriodicalIF":2.1,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216436","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":"Advanced diffusion-weighted imaging for evaluating neoadjuvant chemotherapy in breast cancer.","authors":"Yanqiao Shi, Jianbin Li","doi":"10.1016/j.mri.2026.110817","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110817","url":null,"abstract":"<p><p>Neoadjuvant chemotherapy (NAC) is widely used for locally advanced breast cancer. Imaging monitoring during NAC facilitates timely treatment adjustments and leads to improved patient outcomes. Dynamic contrast-enhanced MRI plays a key role in assessing NAC response; however, early during treatment, tumor volume changes lag behind intracellular microstructural and functional alterations. Diffusion-weighted imaging (DWI) provides average tissue diffusion information based on Gaussian water motion but fails to capture intratumoral heterogeneity. Advanced DWI techniques, including intravoxel incoherent motion (IVIM), stretched exponential modeling (SEM), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI), and DWI-based virtual magnetic resonance elastography (vMRE), have attracted growing interest in evaluating breast cancer during NAC. These techniques require no gadolinium-based contrast agents and can provide valuable information on tumor microstructure, heterogeneity, and microcirculation. However, a notable gap persists between the potential benefits of advanced DWI and their actual clinical utility. This review assesses the current applications and limitations of advanced DWI techniques in NAC for breast cancer.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110817"},"PeriodicalIF":2.1,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216355","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":"Multiparametric MRI with amide proton transfer imaging for assessing stromal invasion depth of early- stage cervical cancer.","authors":"Chongshuang Yang, Min Wu, Chenbo Tan, Yuexin Qiao, Chunfang Zhang, Xin Yi, Junyu Jing, Zhihong Qin, Tianliang Shi","doi":"10.1016/j.mri.2026.110814","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110814","url":null,"abstract":"<p><strong>Objective: </strong>To explore the value of multiparametric magnetic resonance imaging (MRI) with amide proton transfer (APT) imaging in assessing the depth of stromal invasion (DSI) in early-stage cervical cancer.</p><p><strong>Methods: </strong>Clinical and imaging data of patients with early-stage cervical cancer who were pathologically diagnosed at our hospital between January 2020 and June 2025 were retrospectively analyzed. All patients underwent routine MRI, diffusion-weighted imaging (DWI), APT imaging, and dynamic contrast-enhanced MRI (DCE-MRI) before treatment. Apparent diffusion coefficient (ADC), APT values, and semi-quantitative DCE-MRI parameters were measured. According to pathological findings, patients were divided into two groups: DSI ≥1/2 and DSI <1/2. Normally distributed continuous variables were compared using independent-samples t-tests, whereas skewed variables were compared using non-parametric tests. Receiver operating characteristic (ROC) analysis was performed to assess the sensitivity, specificity, and area under the curve (AUC) of ADC, APT, DCE-MRI parameters, and their combination for predicting DSI. Diagnostic performance was compared using the DeLong test.</p><p><strong>Results: </strong>A total of 61 patients were included, with 33 in the DSI <1/2 group and 28 in the DSI (≥1/2) group. The ADC values were significantly lower in the DSI ≥1/2 group compared to the DSI <1/2 group (P < 0.01). In contrast, APT, maximum relative enhancement (MRE), and wash-in rate (WIR) values were significantly higher in the DSI ≥1/2 group (all P < 0.05). The AUCs for APT, ADC, MRE, WIR, and their combination in predicting DSI were 0.73 (95% CI: 0.60-0.83), 0.71 (95% CI: 0.58-0.82), 0.70 (95% CI: 0.57-0.81), 0.68 (95% CI: 0.55-0.80), and 0.85 (95% CI: 0.73-0.93), respectively. Moreover, combining APT, ADC, MRE, and WIR yielded significantly improved diagnostic performance over each parameter alone (P < 0.05).</p><p><strong>Conclusion: </strong>APT imaging is useful for predicting DSI in early-stage cervical cancer before surgery. When combined with ADC and DCE-MRI parameters, its diagnostic accuracy is further improved. This multimodal imaging approach may offer a more reliable method for preoperative assessment of stromal invasion in early-stage cervical cancer.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110814"},"PeriodicalIF":2.1,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216400","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":"Corrigendum to \"Noise removal and intensity inhomogeneity correction in brain MRI: A comprehensive review from classical filters to transformer and diffusion models\" [Magn. Reson. Imaging 134 (2026) 110779].","authors":"Neelam Syamala, Yepuganti Karuna","doi":"10.1016/j.mri.2026.110809","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110809","url":null,"abstract":"","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110809"},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149111759","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}