IEEE transactions on medical imaging最新文献

{"title":"In Vivo Positron Emission Particle Tracking (PEPT) of Single Cells Using an Expectation Maximization Algorithm","authors":"Hieu T. M. Nguyen;Neeladrisingha Das;Rohollah Nasiri;Guillem Pratx","doi":"10.1109/TMI.2025.3640076","DOIUrl":"10.1109/TMI.2025.3640076","url":null,"abstract":"Cell tracking is crucial for understanding the complex patterns of cellular migration that underlie many physiological, pathological, and therapeutic processes. <italic>Positron emission particle tracking</i> (PEPT) is a method that uses list-mode positron emission tomography (PET) data to localize moving particles non-invasively inside opaque systems. However, while the application of this method to <italic>in vivo</i> cell tracking has previously been evoked, its implementation has been limited to tracking one cell at a time. This study investigates the feasibility of tracking multiple cells simultaneously using a recently developed expectation maximization (EM) algorithm called PEPT-EM. The primary challenge to the translation of this algorithm towards biomedical applications is the low radioactivity of the cells being tracked. We experimentally demonstrated the performance of the PEPT-EM algorithm using a preclinical PET scanner for tracking droplets and cells with activities ranging from tens to hundreds of Bq, in phantoms and in a murine model. We found that while background and multiplexing effects impact static source tracking, sensitivity is critical for dynamic tracking of moving sources. We successfully localized multiple single cells in a murine model, moving at speeds up to 25 mm/s, marking the first use of PEPT-EM for such applications. Our findings highlight the exciting potential of PEPT for real-time, high throughput tracking of multiple single cells <italic>in vivo</i>, paving the way for studying cell tracking in biological systems.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 4","pages":"1673-1685"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy","authors":"Rina Bao;Anna N. Foster;Ya’Nan Song;Rutvi Vyas;Ankush Kesri;Imad Eddine Toubal;Elham Soltani Kazemi;Gani Rahmon;Taci Kucukpinar;Mohamed Almansour;Mai-Lan Ho;K. Palaniappan;Dean Ninalga;Chiranjeewee Prasad Koirala;Sovesh Mohapatra;Gottfried Schlaug;Marek Wodzinski;Henning Muller;David G. Ellis;Michele R. Aizenberg;M. Arda Aydın;Elvin Abdinli;Gozde Unal;Nazanin Tahmasebi;Kumaradevan Punithakumar;Tian Song;Yun Peng;Sara V. Bates;Randy Hirschtick;P. Ellen Grant;Yangming Ou","doi":"10.1109/TMI.2025.3638977","DOIUrl":"10.1109/TMI.2025.3638977","url":null,"abstract":"Hypoxic Ischemic Encephalopathy (HIE) represents a brain dysfunction, affecting approximately 1 to 5 per 1000 full-term neonates. The precise delineation and segmentation of HIE-related lesions in neonatal brain Magnetic Resonance Images (MRI) are pivotal in advancing outcome predictions, identifying patients at high risk, elucidating neurological manifestations, and assessing treatment efficacies. Despite its importance, the development of algorithms for segmenting HIE lesions from MRI volumes has been impeded by data scarcity. Addressing this critical gap, we organized the first BONBID-HIE challenge with diffusion MRI data (Apparent Diffusion Coefficient (ADC) maps) for HIE lesion segmentation, in conjunction with the MICCAI 2023. Totally 14 algorithms were submitted, employing a gamut of cutting-edge automatic machine-learning-based segmentation algorithms. Our comprehensive analysis of HIE lesion segmentation and submitted algorithms facilitates an in-depth evaluation of the current technological zenith, outlines directions for future advancements, and highlights persistent hurdles. To foster ongoing research and benchmarking, the annotated HIE dataset, developed algorithm dockers, and unified evaluation codes are accessible through a dedicated online platform (<uri>https://bonbid-hie2023.grand-challenge.org</uri>).","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 4","pages":"1711-1725"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SIB-MIL: Sparsity-Induced Bayesian Neural Network for Robust Multiple Instance Learning on Whole Slide Image Analysis","authors":"Yihang Chen;Tsai Hor Chan;Jianning Chen;Li Liang;Guosheng Yin;Lequan Yu","doi":"10.1109/TMI.2025.3638243","DOIUrl":"10.1109/TMI.2025.3638243","url":null,"abstract":"Multiple instance learning (MIL) has shown prominent success in analyzing whole slide histopathology images (WSIs). However, existing MIL methods often suffer from overfitting due to weak supervision and the “needle-in-a-haystack” nature of WSIs. Additionally, most deterministic approaches lack a mechanism for uncertainty quantification. While Bayesian neural networks (BNNs) have emerged as a promising solution to mitigate overfitting and enable uncertainty estimation by imposing prior constraints, commonly used Gaussian BNNs exhibit unstable posterior predictive distributions under weak supervision and suffer from high prediction variance. To tackle these challenges, we propose a sparsity-induced Bayesian Neural Network to be adopted in the MIL scheme, named SIB-MIL, for robust WSI prediction. Instead of using Gaussian prior distributions, we place a sparsity-induced prior, the Horseshoe prior, on the BNN parameters to address the variance overflowing issue. Such sparsity also filters unimportant noise and highlights salient regions, which only occupy a small proportion in WSIs. Empirical evaluations on cancer classification and subtyping tasks corroborate that not only can our method improve the existing MIL networks, but it also performs well in uncertainty quantification. Codes are available at <uri>https://github.com/HKU-MedAI/SIB-MIL</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 4","pages":"1638-1650"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Paced Learning for Images of Antinuclear Antibodies","authors":"Yiyang Jiang;Guangwu Qian;Jiaxin Wu;Qi Huang;Qing Li;Yongkang Wu;Xiao-Yong Wei","doi":"10.1109/TMI.2025.3637237","DOIUrl":"10.1109/TMI.2025.3637237","url":null,"abstract":"Antinuclear antibody (ANA) testing is a critical method for diagnosing autoimmune disorders such as Lupus, Sjögren’s syndrome, and scleroderma. Despite its importance, manual ANA detection is slow, labor-intensive, and demands years of training. ANA detection is complicated by over 100 coexisting antibody types, resulting in vast fluorescent pattern combinations. Although machine learning and deep learning have enabled automation, ANA detection in real-world clinical settings presents unique challenges as it involves multi-instance, multi-label (MIML) learning. In this paper, a novel framework for ANA detection is proposed that handles the complexities of MIML tasks using unaltered microscope images without manual preprocessing. Inspired by human labeling logic, it identifies consistent ANA sub-regions and assigns aggregated labels accordingly. These steps are implemented using three task-specific components: an instance sampler, a probabilistic pseudo-label dispatcher, and self-paced weight learning rate coefficients. The instance sampler suppresses low-confidence instances by modeling pattern confidence, while the dispatcher adaptively assigns labels based on instance distinguishability. Self-paced learning adjusts training according to empirical label observations. Our framework overcomes limitations of traditional MIML methods and supports end-to-end optimization. Extensive experiments on one ANA dataset and three public medical MIML benchmarks demonstrate the superiority of our framework. On the ANA dataset, our model achieves up to +7.0% F1-Macro and +12.6% mAP gains over the best prior method, setting new state-of-the-art results. It also ranks top-2 across all key metrics on public datasets, reducing Hamming loss and one-error by up to 18.2% and 26.9%, respectively. The source code can be accessed at <uri>https://github.com/fletcherjiang/ANA-SelfPacedLearning</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 4","pages":"1661-1672"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging With FairLoRA.","authors":"Minghan Li, Congcong Wen, Yu Tian, Min Shi, Yan Luo, Hao Huang, Yi Fang, Mengyu Wang","doi":"10.1109/TMI.2025.3622522","DOIUrl":"10.1109/TMI.2025.3622522","url":null,"abstract":"<p><p>Fairness remains a critical concern in healthcare, where unequal access to services and treatment outcomes can adversely affect patient health. While Federated Learning (FL) presents a collaborative and privacy-preserving approach to model training, ensuring fairness is challenging due to heterogeneous data across institutions, and current research primarily addresses non-medical applications. To fill this gap, we establish the first experimental benchmark for fairness in medical FL, evaluating six representative FL methods across diverse demographic attributes and imaging modalities. We introduce FairFedMed, the first medical FL dataset specifically designed to study group fairness (i.e., consistent performance across demographic groups). It comprises two parts: FairFedMed-Oph, featuring 2D fundus and 3D OCT ophthalmology samples with six demographic attributes; and FairFedMed-Chest, which simulates real cross-institutional FL using subsets of CheXpert and MIMIC-CXR. Together, they support both simulated and real-world FL across diverse medical modalities and demographic groups. Existing FL models often underperform on medical images and overlook fairness across demographic groups. To address this, we propose FairLoRA, a fairness-aware FL framework based on SVD-based low-rank approximation. It customizes singular value matrices per demographic group while sharing singular vectors, ensuring both fairness and efficiency. Experimental results on the FairFedMed dataset demonstrate that FairLoRA not only achieves state-of-the-art performance in medical image classification but also significantly improves fairness across diverse populations. Our code and dataset can be accessible via GitHub link: https://github.com/Harvard-AI-and-Robotics-Lab/FairFedMed.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":"1337-1351"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention","authors":"Tianyi Wang;Jianan Fan;Dingxin Zhang;Dongnan Liu;Yong Xia;Heng Huang;Weidong Cai","doi":"10.1109/TMI.2025.3632555","DOIUrl":"10.1109/TMI.2025.3632555","url":null,"abstract":"Histopathology and transcriptomics are fundamental modalities in cancer diagnostics, encapsulating the morphological and molecular characteristics of the disease. Multi-modal self-supervised learning has demonstrated remarkable potential in learning pathological representations by integrating diverse data sources. Conventional multi-modal integration methods primarily emphasize modality alignment, while paying insufficient attention to retaining the modality-specific intrinsic structures. However, unlike conventional scenarios where multi-modal inputs often share highly overlapping features, histopathology and transcriptomics exhibit pronounced heterogeneity, offering orthogonal yet complementary insights. Histopathology data provides morphological and spatial context, elucidating tissue architecture and cellular topology, whereas transcriptomics data delineates molecular signatures through quantifying gene expression patterns. This inherent disparity introduces a major challenge in aligning these modalities while maintaining modality-specific fidelity. To address these challenges, we present MIRROR, a novel multi-modal representation learning framework designed to foster both modality alignment and retention. MIRROR employs dedicated encoders to extract comprehensive feature representations for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles. Furthermore, a modality retention module safeguards unique attributes from each modality, while a style clustering module mitigates redundancy and enhances disease-relevant information by modeling and aligning consistent pathological signatures within a clustering space. Extensive evaluations on The Cancer Genome Atlas (TCGA) cohorts for cancer subtyping and survival analysis highlight MIRROR’s superior performance, demonstrating its effectiveness in constructing comprehensive oncological feature representations and benefiting the cancer diagnosis. Code is available at <uri>https://github.com/TianyiFranklinWang/MIRROR</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 4","pages":"1620-1637"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OPTIKS: Optimized Gradient Properties Through Timing in k-Space","authors":"Matthew A. McCready;Xiaozhi Cao;Kawin Setsompop;John M. Pauly;Adam B. Kerr","doi":"10.1109/TMI.2025.3639398","DOIUrl":"10.1109/TMI.2025.3639398","url":null,"abstract":"A customizable method (OPTIKS) for designing fast trajectory-constrained gradient waveforms with optimized time domain properties was developed. Given a specified multidimensional <inline-formula> <tex-math>$k$ </tex-math></inline-formula>-space trajectory, the method optimizes traversal speed (and therefore timing) with position along the trajectory. OPTIKS facilitates optimization of objectives dependent on the time domain gradient waveform and the arc-length domain <inline-formula> <tex-math>$k$ </tex-math></inline-formula>-space speed. OPTIKS is applied to design waveforms which limit peripheral nerve stimulation (PNS), minimize mechanical resonance excitation, and reduce acoustic noise. A variety of trajectory examples are presented including spirals, circular echo-planar-imaging, and rosettes. Design performance is evaluated based on duration, standardized PNS models, field measurements, gradient coil back-EMF measurements, and calibrated acoustic measurements. We show reductions in back-EMF of up to 94% and field oscillations up to 91.1%, acoustic noise decreases of up to 9.22 dB, and with efficient use of PNS models speed increases of up to 11.4%. The design method implementation is made available as an open source Python package through GitHub (<uri>https://github.com/mamccready/optiks</uri>).","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 4","pages":"1651-1660"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145663017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis.","authors":"Yu-An Huang, Yao Hu, Yue-Chao Li, Xiyue Cao, Xinyuan Li, Kay Chen Tan, Zhu-Hong You, Zhi-An Huang","doi":"10.1109/TMI.2026.3675606","DOIUrl":"https://doi.org/10.1109/TMI.2026.3675606","url":null,"abstract":"<p><p>Functional MRI (fMRI) and single-cell transcri ptomics are pivotal in Alzheimer's disease (AD) research, each providing unique insights into neural function and molecular mechanisms. However, integrating these complementary modalities remains largely unexplored. Here, we introduce scBIT, a novel method for enhancing AD prediction by combining fMRI with single-nucleus RNA (snRNA). scBIT leverages snRNA as an auxiliary modality, significantly improving fMRI-based prediction models and providing comprehensive interpretability. It employs a sampling strategy to segment snRNA data into cell-type-specific gene networks and utilizes a self-explainable graph neural network to extract critical subgraphs. Additionally, we use demographic and genetic similarities to pair snRNA and fMRI data across individuals, enabling robust cross-modal learning. Extensive experiments validate scBIT's effectiveness in revealing intricate brain region-gene associations and enhancing diagnostic prediction accuracy. By advancing brain imaging transcriptomics to the single-cell level, scBIT sheds new light on biomarker discovery in AD research. Experimental results show that incorporating snRNA data into the scBIT model significantly boosts accuracy, improving binary classification by 3.39% and five-class classification by 26.59%. The codes were implemented in Python and have been released on GitHub (https://github.com/77YQ77/scBIT) and Zenodo (https://zenodo.org/records/11599030) with detailed instructions.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unified and Semantically Grounded Domain Adaptation for Medical Image Segmentation.","authors":"Xin Wang, Yin Guo, Jiamin Xia, Kaiyu Zhang, Niranjan Balu, Mahmud Mossa-Basha, Linda Shapiro, Chun Yuan","doi":"10.1109/TMI.2026.3672802","DOIUrl":"https://doi.org/10.1109/TMI.2026.3672802","url":null,"abstract":"<p><p>Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to either the source-accessible setting, where adaptation is guided by source-target alignment, or the source-free setting, which typically resorts to implicit adaptation mechanisms such as pseudo-labeling and network distillation. This substantial divergence in methodological designs between the two settings reveals an inherent flaw: the lack of an explicit, structured construction of anatomical knowledge that naturally generalizes across domains and settings. To bridge this longstanding divide, we introduce a unified, semantically grounded framework that supports both source-accessible and source-free adaptation. Fundamentally distinct from all prior works, our framework's adaptability emerges naturally as a direct consequence of the model architecture, without relying on explicit cross-domain alignment strategies. Specifically, our model learns a domain-agnostic probabilistic manifold as a global space of anatomical regularities, mirroring how humans establish visual understanding. Thus, the structural content in each image can be interpreted as a canonical anatomy retrieved from the manifold and a spatial transformation capturing individual-specific geometry. This disentangled, interpretable formulation enables semantically meaningful prediction with intrinsic adaptability. Extensive experiments on challenging cardiac and abdominal datasets show that our framework achieves state-of-the-art results in both settings, with source-free performance closely approaching its source-accessible counterpart, a level of consistency rarely observed in prior works. Beyond quantitative improvement, we demonstrate strong interpretability of the proposed framework via manifold traversal for smooth shape manipulation. The results provide a principled foundation for anatomically informed, interpretable, and unified solutions for domain adaptation in medical imaging. The code and trained models will be released upon acceptance.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147438530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation.","authors":"Theodore Barfoot, Luis C Garcia-Peraza-Herrera, Samet Akcay, Ben Glocker, Tom Vercauteren","doi":"10.1109/TMI.2026.3673118","DOIUrl":"https://doi.org/10.1109/TMI.2026.3673118","url":null,"abstract":"<p><p>Deep neural networks for medical image segmentation are often overconfident, compromising both reliability and clinical utility. In this work, we propose differentiable formulations of marginal L1 Average Calibration Error (mL1-ACE) as an auxiliary loss that can be computed on a per-image basis. We compare both hard-and soft-binning approaches to directly improve pixel-wise calibration. Our experiments on four datasets (ACDC, AMOS, KiTS, BraTS) demonstrate that incorporating mL1-ACE significantly reduces calibration errors, particularly Average Calibration Error (ACE) and Maximum Calibration Error (MCE), while largely maintaining high Dice Similarity Coefficients (DSCs). We find that the soft-binned variant yields the greatest improvements in calibration, over the DSC plus cross-entropy loss baseline, but often compromises segmentation performance, with hard-binned mL1-ACE maintaining segmentation performance, albeit with weaker calibration improvement. To gain further insight into calibration performance and its variability across an imaging dataset, we introduce dataset reliability histograms, an aggregation of per-image reliability diagrams. The resulting analysis highlights improved alignment between predicted confidences and true accuracies. Overall, our approach provides practitioners with explicit control over the calibration-accuracy trade-off, enabling more reliable integration of deep learning methods into clinical workflows. We share our code here: https://github.com/ cai4cai/Average-Calibration-Losses.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147438510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}