Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献

{"title":"Interpretable Local-to-Global Estimation of Brain Aging Speed From Morphological Changes Using Longitudinal Structural MRI Data.","authors":"Yuanwang Zhang, Hongming Li, Yong Fan","doi":"10.1007/978-3-032-38172-9_31","DOIUrl":"10.1007/978-3-032-38172-9_31","url":null,"abstract":"<p><p>Accurate characterization of brain aging is essential for understanding cognitive decline and assessing the risk of neurodegenerative disease. Brain age estimated from cross-sectional magnetic resonance imaging (MRI) data provides a snapshot of brain health relative to chronological age, and the derived brain age delta has emerged as a promising biomarker. However, brain age delta reflects cumulative effects at a single time point and fails to capture ongoing aging dynamics. Longitudinal approaches address this limitation by estimating age differences between scan pairs to derive aging speed. Nevertheless, existing methods primarily rely on intensity or texture differences between images, overlook the spatial heterogeneity of aging processes, and provide limited interpretability. To overcome these limitations, we propose a novel framework that estimates brain aging speed from longitudinal deformation fields obtained via diffeomorphic image registration. Instead of solely generating a single global estimate, our method produces patch-wise local aging predictions and adaptively integrates them into a unified global prediction, improving both predictive performance and interpretability. Evaluated on large-scale datasets, our approach achieves superior accuracy compared with existing methods and enhances the identification of abnormal aging patterns in diseased populations. Code is available at https://github.com/Kateridge/MorphologyAgingSpeed.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"16887 ","pages":"323-332"},"PeriodicalIF":0.0,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All-in-One Augmented Reality Guided Head and Neck Tumor Resection.","authors":"Yue Yang, Matthieu Chabanas, Carrie Reale, Annie Benson, Jason Slagle, Matthew Weinger, Michael Topf, Jie Ying Wu","doi":"10.1007/978-3-032-38236-8_3","DOIUrl":"10.1007/978-3-032-38236-8_3","url":null,"abstract":"<p><p>Positive margins are common in head and neck squamous cell carcinoma, yet intraoperative re-resection is often imprecise because margin locations are typically communicated verbally from pathology. We present an all-in-one augmented reality (AR) system that relocalizes positive margins from a resected specimen to the resection bed and visualizes them in situ using HoloLens 2 depth sensing and fully automated markerless surface registration. In a silicone phantom study with six medical trainees, markerless registration achieved target registration errors comparable to a marker-based baseline (median 1.8 mm vs. 1.7 mm; maximum < 4 mm). In a margin relocalization task, AR guidance reduced error from verbal guidance (median 14.2 mm) to a few millimeters (median 3.2 mm), with all AR localizations within 5mm error. These results support the feasibility of markerless AR margin guidance for more precise intraoperative re-resection.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"16893 ","pages":"23-31"},"PeriodicalIF":0.0,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning to Unify Deformable Shape and Texture Representations for Cardiac Video Classification.","authors":"Tonmoy Hossain, Miaomiao Zhang","doi":"10.1007/978-3-032-38098-2_38","DOIUrl":"10.1007/978-3-032-38098-2_38","url":null,"abstract":"<p><p>Deformable shape representations have proven to be robust complements to texture features in cardiac image classification, offering geometric priors that are invariant to imaging artifacts and intensity variations. However, existing deep networks perform simple concatenation to combine these distinct feature representations, which neither fully exploits their complementary nature nor learns cross-modal feature dependencies. Furthermore, this results in uniform attention across all timepoints; hence ignoring the varying diagnostic importance across the cardiac phases. In this paper, we propose a novel cardiac video classification model that, for the first time, <i>learns temporal features in an integrated space of deformable shape and image texture representations</i>. In particular, we design a bi-directional cross-attention in the latent space to fuse latent deformable shape and image features, allowing each modality to adaptively weight the other based on spatio-temporal correspondence. In contrast to current methods that apply uniform weighting across all the cardiac phases, our approach learns to dynamically adjust the contributions of shape and texture representations, derived from images, over time. We demonstrate state-of-the-art classification performance on a cine cardiac magnetic resonance (CMR) video dataset, achieving improved interpretability from attention mechanisms that identify diagnostically critical cardiac phases and modality contributions. Our code is publicly available at https://github.com/tonmoy-hossain/ShapeFuse.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"16886 ","pages":"399-409"},"PeriodicalIF":0.0,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13670926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovering Heterogeneous Neurodegenerative Disease Patterns From MRI Data for Improved Prediction.","authors":"Yuanwang Zhang, Hongming Li, Yong Fan","doi":"10.1007/978-3-032-38239-9_16","DOIUrl":"10.1007/978-3-032-38239-9_16","url":null,"abstract":"<p><p>Neurodegenerative diseases exhibit substantial heterogeneity, complicating both diagnosis and prognosis. Identifying clinically meaningful subtypes is crucial for understanding disease mechanisms and improving diagnostic precision and prognostic accuracy. Existing subtyping approaches primarily rely on unsupervised learning of patient data for capturing inter-individual variability, but they may fail to uncover subtypes that are directly informative for diagnosis or prognosis. To address this limitation, we propose a novel mixture-of-experts (MoE) framework that integrates predictive modeling with subtype discovery. Unlike traditional subtyping methods, our approach learns a router to assign individuals to specialized expert networks, each corresponding to a distinct subtype, for improving predictive accuracy. By coupling subtype identification with prediction, the proposed MoE framework encourages the discovery of subtypes that are not only statistically distinct but also clinically informative. We evaluate the framework on a real-world dataset of mild cognitive impairment (MCI) subjects and a semi-simulated dataset, demonstrating superior performance in predicting progression from MCI to Alzheimer's disease while revealing distinct, clinically meaningful MCI subtypes. Code is available at https://github.com/Kateridge/MoESubtyping.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"16894 ","pages":"160-170"},"PeriodicalIF":0.0,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unpaired Multi-Site Brain MRI Harmonization with Image Style-Guided Latent Diffusion.","authors":"Mengqi Wu, Minhui Yu, Weili Lin, Pew-Thian Yap, Mingxia Liu","doi":"10.1007/978-3-032-04947-6_65","DOIUrl":"10.1007/978-3-032-04947-6_65","url":null,"abstract":"<p><p>Multi-site brain MRI heterogeneity caused by differences in scanner field strengths, acquisition protocols, and software versions poses a significant challenge for consistent analysis. Image-level harmonization, leveraging advanced learning methods, has attracted increasing attention. However, existing methods often rely on paired data (<i>e.g.</i>, human traveling phantoms) for training, which are not always available. Some methods perform MRI harmonization by transferring target-style features to source images but require explicitly learning disentangled image styles (<i>e.g.</i>, contrast) via encoder-decoder networks, which increases computational complexity. This paper presents an unpaired MRI harmonization (UMH) framework based on a new image style-guided diffusion model. UMH operates in two stages: (1) a <i>coarse harmonizer</i> that aligns multi-site MRIs to a unified domain via a conditional latent diffusion model while preserving anatomical content; and (2) a <i>fine harmonizer</i> that adapts coarsely harmonized images to a specific target using style embeddings derived from a pre-trained Contrastive Language-Image Pre-training (CLIP) encoder, which captures semantic style differences between the original MRIs and their coarsely-aligned counterparts, eliminating the need for paired data. By leveraging rich semantic style representations of CLIP, UMH avoids learning image styles explicitly, thereby reducing computation costs. We evaluate UMH on 4,123 MRIs from three distinct multi-site datasets, with results suggesting its superiority over several state-of-the-art (SOTA) methods across image-level comparison, downstream classification, and brain tissue segmentation tasks.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15962 ","pages":"683-693"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12706746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Core-Periphery Principle Guided State Space Model for Functional Connectome Classification.","authors":"Minheng Chen, Xiaowei Yu, Jing Zhang, Tong Chen, Chao Cao, Yan Zhuang, Yanjun Lyu, Lu Zhang, Tianming Liu, Dajiang Zhu","doi":"10.1007/978-3-032-05162-2_23","DOIUrl":"10.1007/978-3-032-05162-2_23","url":null,"abstract":"<p><p>Understanding the organization of human brain networks has become a central focus in neuroscience, particularly in the study of functional connectivity, which plays a crucial role in diagnosing neurological disorders. Advances in functional magnetic resonance imaging and machine learning techniques have significantly improved brain network analysis. However, traditional machine learning approaches struggle to capture the complex relationships between brain regions, while deep learning methods, particularly Transformer-based models, face computational challenges due to their quadratic complexity in long-sequence modeling. To address these limitations, we propose a Core-Periphery State-Space Model (CP-SSM), an innovative framework for functional connectome classification. Specifically, we introduce Mamba, a selective state-space model with linear complexity, to effectively capture long-range dependencies in functional brain networks. Furthermore, inspired by the core-periphery (CP) organization, a fundamental characteristic of brain networks that enhances efficient information transmission, we design CP-MoE, a CP-guided Mixture-of-Experts that improves the representation learning of brain connectivity patterns. We evaluate CP-SSM on two benchmark fMRI datasets: ABIDE and ADNI. Experimental results demonstrate that CP-SSM surpasses Transformer-based models in classification performance while significantly reducing computational complexity. These findings highlight the effectiveness and efficiency of CP-SSM in modeling brain functional connectivity, offering a promising direction for neuroimaging-based neurological disease diagnosis. Our code is available at https://github.com/m1nhengChen/cpssm.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15971 ","pages":"236-246"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12715851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs.","authors":"Bahram Jafrasteh, Wei Peng, Cheng Wan, Yimin Luo, Ehsan Adeli, Qingyu Zhao","doi":"10.1007/978-3-032-04937-7_65","DOIUrl":"10.1007/978-3-032-04937-7_65","url":null,"abstract":"<p><p>Generative models enhance neuroimaging through data augmentation, quality improvement, and rare condition studies. Despite advances in realistic synthetic MRIs, evaluations focus on texture and perception, lacking sensitivity to crucial morphometric fidelity. This study proposes a new metric, called WASABI (Wasserstein-Based Anatomical Brain Index), to assess the morphometric plausibility of synthetic brain MRIs. WASABI leverages <i>SynthSeg</i>, a deep learning-based brain parcellation tool, to derive volumetric measures of brain regions in each MRI and uses the multivariate Wasserstein distance to compare distributions between real and synthetic anatomies. Based on controlled experiments on two real datasets and synthetic MRIs from five generative models, WASABI demonstrates higher sensitivity in quantifying morphometric discrepancies compared to traditional image-level metrics, even when synthetic images achieve near-perfect visual quality. Our findings advocate for shifting the evaluation paradigm beyond visual inspection and conventional metrics, emphasizing morphometric fidelity as a crucial benchmark for clinically meaningful brain MRI synthesis. Our code is available at https://github.com/BahramJafrasteh/wasabi-mri.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15961 ","pages":"684-694"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry-Guided Local Alignment for Multi-View Visual Language Pre-Training in Mammography.","authors":"Yuexi Du, Lihui Chen, Nicha C Dvornek","doi":"10.1007/978-3-032-04978-0_29","DOIUrl":"10.1007/978-3-032-04978-0_29","url":null,"abstract":"<p><p>Mammography screening is an essential tool for early detection of breast cancer. The speed and accuracy of mammography interpretation has the potential to be improved with deep learning methods. However, the development of a foundation visual language model (VLM) is hindered by limited data and domain differences between natural and medical images. Existing mammography VLMs, adapted from natural images, often ignore domain-specific characteristics, such as multi-view relationships in mammography. Unlike radiologists who analyze both views together to process ipsilateral correspondence, current methods treat them as independent images or do not properly model the multi-view correspondence learning, losing critical geometric context and resulting in suboptimal prediction. We propose <b>GLAM</b>: <b>G</b>lobal and <b>L</b>ocal <b>A</b>lignment for <b>M</b>ulti-view mammography for VLM pretraining using geometry guidance. By leveraging the prior knowledge about the multi-view imaging process of mammograms, our model learns local cross-view alignments and fine-grained local features through joint global and local, visual-visual, and visual-language contrastive learning. Pretrained on EMBED [14], one of the largest open mammography datasets, our model outperforms baselines across multiple datasets under different settings.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15965 ","pages":"299-310"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13127458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">LNODE: Uncovering the Latent Dynamics of <ns0:math><ns0:mi>A</ns0:mi> <ns0:mi>β</ns0:mi></ns0:math> in Alzheimer's Disease.","authors":"Zheyu Wen, George Biros","doi":"10.1007/978-3-032-05182-0_31","DOIUrl":"10.1007/978-3-032-05182-0_31","url":null,"abstract":"<p><p><math><mtext>A</mtext> <mi>β</mi></math> Positron Emission Tomography (PET) is often used to manage Alzheimer's disease (AD). To better understand <math><mtext>A</mtext> <mi>β</mi></math> progression, we introduce and evaluate a mathematical model that couples <math><mtext>A</mtext> <mi>β</mi></math> at parcellated gray matter regions. We term this model LNODE for \"<i>latent network ordinary differential equations</i>\". At each region, we track normal <math><mtext>A</mtext> <mi>β</mi></math> , abnormal <math><mtext>A</mtext> <mi>β</mi></math> , and <math><mi>m</mi></math> latent states that intend to capture unobservable mechanisms coupled to <math><mtext>A</mtext> <mi>β</mi></math> progression. LNODE is parameterized by subject-specific parameters and cohort parameters. We jointly invert for these parameters by fitting the model to <math><mtext>A</mtext> <mi>β</mi></math> -PET data from 585 subjects from the ADNI dataset. Although underparameterized, our model achieves population <math> <msup><mrow><mi>R</mi></mrow> <mrow><mn>2</mn></mrow> </msup> <mo>≥</mo> <mn>98</mn> <mo>%</mo></math> compared to <math> <msup><mrow><mi>R</mi></mrow> <mrow><mn>2</mn></mrow> </msup> <mo>≤</mo> <mn>60</mn> <mo>%</mo></math> when fitting without latent states. Furthermore, these preliminary results suggest the existence of different subtypes of <math><mtext>A</mtext> <mi>β</mi></math> progression.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15974 ","pages":"313-322"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12784421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145954748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FluoroSAM: A Language-promptable Foundation Model for Flexible X-ray Image Segmentation.","authors":"Benjamin D Killeen, Liam J Wang, Blanca Iñígo, Han Zhang, Mehran Armand, Russell H Taylor, Greg Osgood, Mathias Unberath","doi":"10.1007/978-3-032-04981-0_24","DOIUrl":"10.1007/978-3-032-04981-0_24","url":null,"abstract":"<p><p>Language promptable X-ray image segmentation would enable greater flexibility for human-in-the-loop workflows in diagnostic and interventional precision medicine. Prior efforts have contributed task-specific models capable of solving problems within a narrow scope, but expanding to broader use requires additional data, annotations, and training time. Recently, language-aligned foundation models (LFMs) - machine learning models trained on large amounts of highly variable image and text data thus enabling broad applicability - have emerged as promising tools for automated image analysis. Existing foundation models for medical image analysis focus on scenarios and modalities where large, richly annotated datasets are available. However, the X-ray imaging modality features highly variable image appearance and applications, from diagnostic chest X-rays to interventional fluoroscopy, with varying availability of data. To pave the way toward an LFM for comprehensive and language-aligned analysis of arbitrary medical X-ray images, we introduce FluoroSAM, a language-promptable variant of the Segment-Anything Model, trained from scratch on 3M synthetic X-ray images from a wide variety of human anatomies, imaging geometries, and viewing angles. These include pseudo-ground truth masks for 128 organ types and 464 tools with associated text descriptions. FluoroSAM is capable of segmenting myriad anatomical structures and tools based on natural language prompts, thanks to the novel incorporation of vector quantization (VQ) of text embeddings in the training process. We demonstrate FluoroSAM's performance quantitatively on real X-ray images and showcase on several applications how FluoroSAM is a key enabler for rich human-machine interaction in the X-ray image acquisition and analysis context. <i>Code is available at</i> https://github.com/arcadelab/fluorosam.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15966 ","pages":"248-258"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12822567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146032259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}