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

MUTUAL: Towards Holistic Sensing and Inference in the Operating Room. 互助：在手术室中实现整体感知和推理。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2025-01-17 DOI: 10.1007/978-3-031-77610-6_17

Julien Quarez, Yang Li, Hassna Irzan, Matthew Elliot, Oscar MacCormac, James Knigth, Martin Huber, Toktam Mahmoodi, Prokar Dasgupta, Sebastien Ourselin, Nicholas Raison, Jonathan Shapey, Alejandro Granados

{"title":"MUTUAL: Towards Holistic Sensing and Inference in the Operating Room.","authors":"Julien Quarez, Yang Li, Hassna Irzan, Matthew Elliot, Oscar MacCormac, James Knigth, Martin Huber, Toktam Mahmoodi, Prokar Dasgupta, Sebastien Ourselin, Nicholas Raison, Jonathan Shapey, Alejandro Granados","doi":"10.1007/978-3-031-77610-6_17","DOIUrl":"10.1007/978-3-031-77610-6_17","url":null,"abstract":"Embodied AI (E-AI) in the form of intelligent surgical robotics and other agents is calling for data platforms to facilitate its development and deployment. In this work, we present a cross-platform multimodal data recording and streaming software, MUTUAL, successfully deployed on two clinical studies, along with its ROS 2 distributed adaptation, MUTUAL-ROS 2. We describe and compare the two implementations of MUTUAL through their recording performance under different settings. MUTUAL offers robust recording performance at target configurations for multiple modalities, including video, audio, and live expert commentary. While this recording performance is not matched by MUTUAL-ROS 2, we demonstrate its advantages related to real-time streaming capabilities for AI inference and more horizontal scalability, key aspects for E-AI systems in the operating room. Our findings demonstrate that the baseline MUTUAL is well-suited for data curation and offline analysis, whereas MUTUAL-ROS 2, should match the recording reliability of the baseline system under a fully distributed manner where modalities are handled independently by edge computing devices. These insights are critical for advancing the integration of E-AI in surgical practice, ensuring that data infrastructure can support both robust recording and real-time processing needs.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":" ","pages":"178-188"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7617325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049392","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}

引用次数: 0

Zoom Pattern Signatures for Fetal Ultrasound Structures. 胎儿超声结构的缩放模式特征。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-06 DOI: 10.1007/978-3-031-72083-3_73

Mohammad Alsharid, Robail Yasrab, Lior Drukker, Aris T Papageorghiou, J Alison Noble

引用次数: 0

Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation. 三维解剖分割中切片传播不确定性的估计与分析。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-03 DOI: 10.1007/978-3-031-72117-5_26

Rachaell Nihalaani, Tushar Kataria, Jadie Adams, Shireen Y Elhabian

{"title":"Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation.","authors":"Rachaell Nihalaani, Tushar Kataria, Jadie Adams, Shireen Y Elhabian","doi":"10.1007/978-3-031-72117-5_26","DOIUrl":"10.1007/978-3-031-72117-5_26","url":null,"abstract":"Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available unannotated data. Slice propagation has emerged as a self-supervised approach that leverages slice registration as a self-supervised task to achieve full anatomy segmentation with minimal supervision. This approach significantly reduces the need for domain expertise, time, and the cost associated with building fully annotated datasets required for training segmentation networks. However, this shift toward reduced supervision via deterministic networks raises concerns about the trustworthiness and reliability of predictions, especially when compared with more accurate supervised approaches. To address this concern, we propose integrating calibrated uncertainty quantification (UQ) into slice propagation methods, which would provide insights into the model's predictive reliability and confidence levels. Incorporating uncertainty measures enhances user confidence in self-supervised approaches, thereby improving their practical applicability. We conducted experiments on three datasets for 3D abdominal segmentation using five UQ methods. The results illustrate that incorporating UQ improves not only model trustworthiness but also segmentation accuracy. Furthermore, our analysis reveals various failure modes of slice propagation methods that might not be immediately apparent to end-users. This study opens up new research avenues to improve the accuracy and trustworthiness of slice propagation methods.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15010 ","pages":"273-285"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549934","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}

引用次数: 0

Adaptive Subtype and Stage Inference for Alzheimer's Disease. 阿尔茨海默病的适应性亚型和分期推断。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-03 DOI: 10.1007/978-3-031-72384-1_5

Xinkai Wang, Yonggang Shi

引用次数: 0

Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer. 通过光时空变换器实现标记-正片磁共振成像序列合成

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-03 DOI: 10.1007/978-3-031-72104-5_67

Xiaofeng Liu, Fangxu Xing, Zhangxing Bian, Tomas Arias-Vergara, Paula Andrea Pérez-Toro, Andreas Maier, Maureen Stone, Jiachen Zhuo, Jerry L Prince, Jonghye Woo

{"title":"Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer.","authors":"Xiaofeng Liu, Fangxu Xing, Zhangxing Bian, Tomas Arias-Vergara, Paula Andrea Pérez-Toro, Andreas Maier, Maureen Stone, Jiachen Zhuo, Jerry L Prince, Jonghye Woo","doi":"10.1007/978-3-031-72104-5_67","DOIUrl":"10.1007/978-3-031-72104-5_67","url":null,"abstract":"Tagged magnetic resonance imaging (MRI) has been successfully used to track the motion of internal tissue points within moving organs. Typically, to analyze motion using tagged MRI, cine MRI data in the same coordinate system are acquired, incurring additional time and costs. Consequently, tagged-to-cine MR synthesis holds the potential to reduce the extra acquisition time and costs associated with cine MRI, without disrupting downstream motion analysis tasks. Previous approaches have processed each frame independently, thereby overlooking the fact that complementary information from occluded regions of the tag patterns could be present in neighboring frames exhibiting motion. Furthermore, the inconsistent visual appearance, e.g., tag fading, across frames can reduce synthesis performance. To address this, we propose an efficient framework for tagged-to-cine MR sequence synthesis, leveraging both spatial and temporal information with relatively limited data. Specifically, we follow a split-and-integral protocol to balance spatialtemporal modeling efficiency and consistency. The light spatial-temporal transformer (LiST2) is designed to exploit the local and global attention in motion sequence with relatively lightweight training parameters. The directional product relative position-time bias is adapted to make the model aware of the spatial-temporal correlation, while the shifted window is used for motion alignment. Then, a recurrent sliding fine-tuning (ReST) scheme is applied to further enhance the temporal consistency. Our framework is evaluated on paired tagged and cine MRI sequences, demonstrating superior performance over comparison methods.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15007 ","pages":"701-711"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11517403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524019","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}

引用次数: 0

CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning. 基于高效大语言模型和快速微调的语言-图像对比学习。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-23 DOI: 10.1007/978-3-031-72390-2_44

Yuexi Du, Brian Chang, Nicha C Dvornek

{"title":"CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning.","authors":"Yuexi Du, Brian Chang, Nicha C Dvornek","doi":"10.1007/978-3-031-72390-2_44","DOIUrl":"10.1007/978-3-031-72390-2_44","url":null,"abstract":"Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples. We introduce a novel language-image Contrastive Learning method with an Efficient large language model and prompt Fine-Tuning (CLEFT) that harnesses the strengths of the extensive pre-trained language and visual models. Furthermore, we present an efficient strategy for learning context-based prompts that mitigates the gap between informative clinical diagnostic data and simple class labels. Our method demonstrates state-of-the-art performance on multiple chest X-ray and mammography datasets compared with various baselines. The proposed parameter efficient framework can reduce the total trainable model size by 39% and reduce the trainable language model to only 4% compared with the current BERT encoder.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15012 ","pages":"465-475"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11709740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960994","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}

引用次数: 0

Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment. 结构性和功能性核磁共振成像的注意力增强融合，用于分析艾滋病毒相关的无症状神经认知障碍。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-03 DOI: 10.1007/978-3-031-72120-5_11

Yuqi Fang, Wei Wang, Qianqian Wang, Hong-Jun Li, Mingxia Liu

{"title":"Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment.","authors":"Yuqi Fang, Wei Wang, Qianqian Wang, Hong-Jun Li, Mingxia Liu","doi":"10.1007/978-3-031-72120-5_11","DOIUrl":"10.1007/978-3-031-72120-5_11","url":null,"abstract":"Asymptomatic neurocognitive impairment (ANI) is a predominant form of cognitive impairment among individuals infected with human immunodeficiency virus (HIV). The current diagnostic criteria for ANI primarily rely on subjective clinical assessments, possibly leading to different interpretations among clinicians. Some recent studies leverage structural or functional MRI containing objective biomarkers for ANI analysis, offering clinicians companion diagnostic tools. However, they mainly utilize a single imaging modality, neglecting complementary information provided by structural and functional MRI. To this end, we propose an attention-enhanced structural and functional MRI fusion (ASFF) framework for HIV-associated ANI analysis. Specifically, the ASFF first extracts data-driven and human-engineered features from structural MRI, and also captures functional MRI features via a graph isomorphism network and Transformer. A mutual cross-attention fusion module is then designed to model the underlying relationship between structural and functional MRI. Additionally, a semantic inter-modality constraint is introduced to encourage consistency of multimodal features, facilitating effective feature fusion. Experimental results on 137 subjects from an HIV-associated ANI dataset with T1-weighted MRI and resting-state functional MRI show the effectiveness of our ASFF in ANI identification. Furthermore, our method can identify both modality-shared and modality-specific brain regions, which may advance our understanding of the structural and functional pathology underlying ANI.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15011 ","pages":"113-123"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11512738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142516842","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}

引用次数: 0

Physics Informed Neural Networks for Estimation of Tissue Properties from Multi-echo Configuration State MRI. 基于物理信息的神经网络在多回波构型状态MRI中估计组织特性。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-03 DOI: 10.1007/978-3-031-72120-5_47

Samuel I Adams-Tew, Henrik Odéen, Dennis L Parker, Cheng-Chieh Cheng, Bruno Madore, Allison Payne, Sarang Joshi

{"title":"Physics Informed Neural Networks for Estimation of Tissue Properties from Multi-echo Configuration State MRI.","authors":"Samuel I Adams-Tew, Henrik Odéen, Dennis L Parker, Cheng-Chieh Cheng, Bruno Madore, Allison Payne, Sarang Joshi","doi":"10.1007/978-3-031-72120-5_47","DOIUrl":"10.1007/978-3-031-72120-5_47","url":null,"abstract":"This work investigates the use of configuration state imaging together with deep neural networks to develop quantitative MRI techniques for deployment in an interventional setting. A physics modeling technique for inhomogeneous fields and heterogeneous tissues is presented and used to evaluate the theoretical capability of neural networks to estimate parameter maps from configuration state signal data. All tested normalization strategies achieved similar performance in estimating <math> <msub><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> </msub> </math> and <math> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mi>*</mi></mrow> </msubsup> </math> . Varying network architecture and data normalization had substantial impacts on estimated flip angle and <math> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn></mrow> </msub> </math> , highlighting their importance in developing neural networks to solve these inverse problems. The developed signal modeling technique provides an environment that will enable the development and evaluation of physics-informed machine learning techniques for MR parameter mapping and facilitate the development of quantitative MRI techniques to inform clinical decisions during MR-guided treatments.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15011 ","pages":"502-511"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857422","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}

引用次数: 0

Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases. 针对阿尔茨海默病的自导式知识注入图神经网络。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-04 DOI: 10.1007/978-3-031-72069-7_36

Zhepeng Wang, Runxue Bao, Yawen Wu, Guodong Liu, Lei Yang, Liang Zhan, Feng Zheng, Weiwen Jiang, Yanfu Zhang

{"title":"Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.","authors":"Zhepeng Wang, Runxue Bao, Yawen Wu, Guodong Liu, Lei Yang, Liang Zhan, Feng Zheng, Weiwen Jiang, Yanfu Zhang","doi":"10.1007/978-3-031-72069-7_36","DOIUrl":"10.1007/978-3-031-72069-7_36","url":null,"abstract":"Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15002 ","pages":"378-388"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484836","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}

引用次数: 0

Development of Effective Connectome from Infancy to Adolescence. 从婴儿期到青春期有效连接组的发展。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-03 DOI: 10.1007/978-3-031-72384-1_13

Guoshi Li, Kim-Han Thung, Hoyt Taylor, Zhengwang Wu, Gang Li, Li Wang, Weili Lin, Sahar Ahmad, Pew-Thian Yap

{"title":"Development of Effective Connectome from Infancy to Adolescence.","authors":"Guoshi Li, Kim-Han Thung, Hoyt Taylor, Zhengwang Wu, Gang Li, Li Wang, Weili Lin, Sahar Ahmad, Pew-Thian Yap","doi":"10.1007/978-3-031-72384-1_13","DOIUrl":"10.1007/978-3-031-72384-1_13","url":null,"abstract":"Delineating the normative developmental profile of functional connectome is important for both standardized assessment of individual growth and early detection of diseases. However, functional connectome has been mostly studied using functional connectivity (FC), where undirected connectivity strengths are estimated from statistical correlation of resting-state functional MRI (rs-fMRI) signals. To address this limitation, we applied regression dynamic causal modeling (rDCM) to delineate the developmental trajectories of effective connectivity (EC), the directed causal influence among neuronal populations, in whole-brain networks from infancy to adolescence (0-22 years old) based on high-quality rs-fMRI data from Baby Connectome Project (BCP) and Human Connectome Project Development (HCP-D). Analysis with linear mixed model demonstrates significant age effect on the mean nodal EC which is best fit by a \"U\" shaped quadratic curve with minimal EC at around 2 years old. Further analysis indicates that five brain regions including the left and right cuneus, left precuneus, left supramarginal gyrus and right inferior temporal gyrus have the most significant age effect on nodal EC (p < 0.05, FDR corrected). Moreover, the frontoparietal control (FPC) network shows the fastest increase from early childhood to adolescence followed by the visual and salience networks. Our findings suggest complex nonlinear developmental profile of EC from infancy to adolescence, which may reflect dynamic structural and functional maturation during this critical growth period.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15003 ","pages":"131-140"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11758277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049390","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}

引用次数: 0