ArXiv最新文献

{"title":"MRI motion correction via efficient residual-guided denoising diffusion probabilistic models.","authors":"Mojtaba Safari, Shansong Wang, Qiang Li, Zach Eidex, Richard L J Qiu, Chih-Wei Chang, Hui Mao, Xiaofeng Yang","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Purpose: </strong>Motion artifacts in magnetic resonance imaging (MRI) significantly degrade image quality and hinder quantitative downstream applications. Conventional methods to mitigate these artifacts, including repeated acquisitions or motion tracking, impose substantial financial and workflow burdens. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model specifically designed for MRI motion artifact correction.</p><p><strong>Methods: </strong>Res-MoCoDiff exploits a novel residual error shifting mechanism during the forward diffusion process to incorporate information from motion-corrupted images. This mechanism allows the model to simulate the evolution of noise with a probability distribution closely matching that of the corrupted data, enabling a reverse diffusion process that requires only four steps. The model employs a U-net backbone, with conventional attention layers replaced by Swin Transformer blocks, to enhance robustness across various image resolutions. Furthermore, the training process integrates a combined <math> <mrow><msub><mo>ℓ</mo> <mn>1</mn></msub> <mo>+</mo> <msub><mo>ℓ</mo> <mn>2</mn></msub> </mrow> </math> loss function, which promotes image sharpness and reduces pixel-level errors. Res-MoCoDiff was evaluated on both an <i>in-silico</i> dataset generated using a realistic motion simulation framework and an <i>in-vivo</i> MR-ART dataset. Comparative analyses were conducted against established methods, including CycleGAN, Pix2pix, and a diffusion model with a vision transformer backbone (MT-DDPM), using quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE).</p><p><strong>Results: </strong>The proposed method demonstrated superior performance in removing motion artifacts across minor, moderate, and heavy distortion levels. Res-MoCoDiff consistently achieved the highest SSIM and the lowest NMSE values, with a PSNR of up to 41.91 ± 2.94 dB for minor distortions. Notably, the average sampling time was reduced to 0.37 seconds per batch of two image slices, compared with 101.74 seconds for conventional approaches.</p><p><strong>Conclusion: </strong>Res-MoCoDiff offers a robust and efficient solution for correcting MRI motion artifacts, preserving fine structural details while significantly reducing computational overhead. Its rapid processing speed and high restoration fidelity underscore its potential for seamless integration into clinical workflows, ultimately enhancing diagnostic and treatment accuracy and patient care.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095829","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":"DUAL PROMPTING FOR DIVERSE COUNT-LEVEL PET DENOISING.","authors":"Xiaofeng Liu, Yongsong Huang, Thibault Marin, Samira Vafay Eslahi, Amal Tiss, Yanis Chemli, Keith A Johnson, Georges El Fakhri, Jinsong Ouyang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The to-be-denoised positron emission tomography (PET) volumes are inherent with diverse count levels, which imposes challenges for a unified model to tackle varied cases. In this work, we resort to the recently flourished prompt learning to achieve generalizable PET denoising with different count levels. Specifically, we propose dual prompts to guide the PET denoising in a divide-and-conquer manner, i.e., an explicitly count-level prompt to provide the specific prior information and an implicitly general denoising prompt to encode the essential PET denoising knowledge. Then, a novel prompt fusion module is developed to unify the heterogeneous prompts, followed by a prompt-feature interaction module to inject prompts into the features. The prompts are able to dynamically guide the noise-conditioned denoising process. Therefore, we are able to efficiently train a unified denoising model for various count levels, and deploy it to different cases with personalized prompts. We evaluated on 1940 low-count PET 3D volumes with uniformly randomly selected 13-22% fractions of events from 97 ¹⁸F-MK6240 tau PET studies. It shows our dual prompting can largely improve the performance with informed count-level and outperform the count-conditional model.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095447","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":"Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture.","authors":"Tien Comlekoglu, J Quetzalcóatl Toledo-Marín, Tina Comlekoglu, Douglas W Desimone, Shayn M Peirce, Geoffrey Fox, James A Glazier","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate <i>in vitro</i> vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 590 times compared to CPM code execution. Over multiple recursive evaluations, our model effectively captures the emergent behaviors demonstrated by the original Cellular-Potts model of such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM of biological processes at greater spatial and temporal scales.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096089","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":"Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI.","authors":"Merve Gülle, Sebastian Weingärtner, Mehmet Akçakaya","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Real-time (RT) dynamic MRI plays a vital role in capturing rapid physiological processes, offering unique insights into organ motion and function. Among these applications, RT cine MRI is particularly important for functional assessment of the heart with high temporal resolution. RT imaging enables free-breathing, ungated imaging of cardiac motion, making it a crucial alternative for patients who cannot tolerate conventional breath-hold, ECG-gated acquisitions. However, achieving high acceleration rates in RT cine MRI is challenging due to aliasing artifacts from extra-cardiac tissues, particularly at high undersampling factors. In this study, we propose a novel outer volume removal (OVR) method to address this challenge by eliminating aliasing contributions from non-cardiac regions in a post-processing framework. Our approach estimates the outer volume signal for each timeframe using composite temporal images from time-interleaved undersampling patterns, which inherently contain pseudo-periodic ghosting artifacts. A deep learning (DL) model is trained to identify and remove these artifacts, producing a clean outer volume estimate that is subsequently subtracted from the corresponding k-space data. The final reconstruction is performed with a physics-driven DL (PD-DL) method trained using an OVR-specific loss function to restore high spatio-temporal resolution images. Experimental results show that the proposed method at high accelerations achieves image quality that is visually comparable to clinical baseline images, while outperforming conventional reconstruction techniques, both qualitatively and quantitatively. The proposed approach provides a practical and effective solution for artifact reduction in RT cine MRI without requiring acquisition modifications, offering a pathway to higher acceleration rates while preserving diagnostic quality.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000351","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":"TumorTwin: A python framework for patient-specific digital twins in oncology.","authors":"Michael Kapteyn, Anirban Chaudhuri, Ernesto A B F Lima, Graham Pash, Rafael Bravo, Karen Willcox, Thomas E Yankeelov, David A Hormuth Ii","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Advances in the theory and methods of computational oncology have enabled accurate characterization and prediction of tumor growth and treatment response on a patient-specific basis. This capability can be integrated into a digital twin framework in which bi-directional data-flow between the physical tumor and the digital tumor facilitate dynamic model re-calibration, uncertainty quantification, and clinical decision-support via recommendation of optimal therapeutic interventions. However, many digital twin frameworks rely on bespoke implementations tailored to each disease site, modeling choice, and algorithmic implementation.</p><p><strong>Findings: </strong>We present TumorTwin, a modular software framework for initializing, updating, and leveraging patient-specific cancer tumor digital twins. TumorTwin is publicly available as a Python package, with associated documentation, datasets, and tutorials. Novel contributions include the development of a patient-data structure adaptable to different disease sites, a modular architecture to enable the composition of different data, model, solver, and optimization objects, and CPU- or GPU-parallelized implementations of forward model solves and gradient computations. We demonstrate the functionality of TumorTwin via an in silico dataset of high-grade glioma growth and response to radiation therapy.</p><p><strong>Conclusions: </strong>The TumorTwin framework enables rapid prototyping and testing of image-guided oncology digital twins. This allows researchers to systematically investigate different models, algorithms, disease sites, or treatment decisions while leveraging robust numerical and computational infrastructure.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031900","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":"MovementVR: An open-source tool for the study of motor control and learning in virtual reality.","authors":"Cristina Rossi, Rini Varghese, Amy J Bastian","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Virtual reality (VR) is increasingly used to enhance the ecological validity of motor control and learning studies by providing immersive, interactive environments with precise motion tracking. However, designing realistic VR-based motor tasks remains complex, requiring advanced programming skills and limiting accessibility in research and clinical settings. MovementVR is an open-source platform designed to address these challenges by enabling the creation of customizable, naturalistic reaching tasks in VR without coding expertise. It integrates physics-based hand-object interactions, real-time hand tracking, and flexible experimental paradigms, including motor adaptation and reinforcement learning. The intuitive graphical user interface (GUI) allows researchers to customize task parameters and paradigm structure. Unlike existing platforms, MovementVR eliminates the need for scripting while supporting extensive customization and preserving ecological validity and realism. In addition to reducing technical barriers, MovementVR lowers financial constraints by being compatible with consumer-grade VR headsets. It is freely available with comprehensive documentation, facilitating broader adoption in movement research and rehabilitation.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055861","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":"Efficient Spatial Estimation of Perceptual Thresholds for Retinal Implants via Gaussian Process Regression.","authors":"Roksana Sadeghi, Michael Beyeler","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Retinal prostheses restore vision by electrically stimulating surviving neurons, but calibrating perceptual thresholds (i.e., the minimum stimulus intensity required for perception) remains a time-intensive challenge, especially for high-electrode-count devices. Since neighboring electrodes exhibit spatial correlations, we propose a Gaussian Process Regression (GPR) framework to predict thresholds at unsampled locations while leveraging uncertainty estimates to guide adaptive sampling. Using perceptual threshold data from four Argus II users, we show that GPR with a Matern kerneĺ provides more accurate threshold predictions than a Radial Basis Function (RBF) kernel (<i>p</i> < .001, Wilcoxon signed-rank test). In addition, spatially optimized sampling yielded lower prediction error than uniform random sampling for Participants 1 and 3 (<i>p</i> < .05). While adaptive sampling dynamically selects electrodes based on model uncertainty, its accuracy gains over spatial sampling were not statistically significant (<i>p</i> > .05), though it approached significance for Participant 1 (<i>p</i> = .074). These findings establish GPR with spatial sampling as a scalable, efficient approach to retinal prosthesis calibration, minimizing patient burden while maintaining predictive accuracy. More broadly, this framework offers a generalizable solution for adaptive calibration in neuroprosthetic devices with spatially structured stimulation thresholds, paving the way for faster, more personalized system fitting in future high-channel-count implants.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484846","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":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.","authors":"Luoting Zhuang, Stephen H Park, Steven J Skates, Ashley E Prosper, Denise R Aberle, William Hsu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484813","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":"Consensus Recommendations for Hyperpolarized [1-¹³C]pyruvate MRI Multi-center Human Studies.","authors":"Shonit Punwani, Peder Ez Larson, Christoffer Laustsen, Jan VanderMeulen, Jan Henrik Ardenkjær-Larsen, Adam W Autry, James A Bankson, Jenna Bernard, Robert Bok, Lotte Bonde Bertelsen, Jenny Che, Albert P Chen, Rafat Chowdhury, Arnaud Comment, Charles H Cunningham, Duy Dang, Ferdia A Gallagher, Adam Gaunt, Yangcan Gong, Jeremy W Gordon, Ashley Grimmer, James Grist, Esben Søvsø Szocska Hansen, Mathilde Hauge Lerche, Richard L Hesketh, Jan-Bernd Hoevener, Ching-Yi Hsieh, Kayvan R Keshari, Sebastian Kozerke, Titus Lanz, Dirk Mayer, Mary McLean, Jae Mo Park, Jim Slater, Damian Tyler, Jean-Luc Vanderheyden, Cornelius von Morze, Fulvio Zaccagna, Vlad Zaha, Duan Xu, Daniel Vigneron","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Magnetic resonance imaging of hyperpolarized (HP) [1-¹³C]pyruvate allows in-vivo assessment of metabolism and has translated into human studies across diseases at 15 centers worldwide. Consensus on best practice for multi-center studies is required to develop clinical applications. This paper presents the results of a 2-round formal consensus building exercise carried out by experts with HP [1-¹³C]pyruvate human study experience. Twenty-nine participants from 13 sites brought together expertise in pharmacy methods, MR physics, translational imaging, and data-analysis; with the goal of providing recommendations and best practice statements on conduct of multi-center human studies of HP [1-¹³C]pyruvate MRI. Overall, the group reached consensus on approximately two-thirds of 246 statements in the questionnaire, covering 'HP ¹³C-Pyruvate Preparation', 'MRI System Setup, Calibration, and Phantoms', 'Acquisition and Reconstruction', and 'Data Analysis and Quantification'. Consensus was present across categories, examples include that: (i) different HP pyruvate preparation methods could be used in human studies, but that the same release criteria have to be followed; (ii) site qualification and quality assurance must be performed with phantoms and that the same field strength must be used, but that the rest of the system setup and calibration methods could be determined by individual sites;(iii) the same pulse sequence and reconstruction methods were preferable, but the exact choice should be governed by the anatomical target; (iv) normalized metabolite area-under-curve (AUC) values and metabolite AUC were the preferred metabolism metrics. The work confirmed areas of consensus for multi-center study conduct and identified where further research is required to ascertain best practice.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055175","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":"Light-Based Fast Timing in Bulk CsPbBr3 Crystals for TOF-PET and Proton Range Verification.","authors":"Nicolaus Kratohwil, Leonor Rebolo, Indra R Pandey, Joshua W Cates, Emilie Roncali, Joshua H Palmer, Gerard Arino-Estrada","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Halide perovskite semiconductors such as CsPbBr3 (CLB) are emerging gamma-ray detectors for applications requiring very high energy resolution and potential for fine detector segmentation. Semiconductor detectors typically offer poor time resolution due to the long drift times. Recently, we proposed to use the Cherenkov light component in partially transparent semiconductors to boost the timing capability of such detectors. Cherenkov light produced upon 511 keV gamma-ray interaction with CLB was investigated by means of optical simulations and experimental measurements. The timing capability of a pair of identical CLB crystals ( 3 x 3 x 5 mm3) coupled to NUV- MT silicon photomultipliers was measured. On average, 9.5 Cherenkov photons are produced in CLB between 555 and 900 nm for 511 keV photoelectric interactions based on our simulation framework. Experimentally, we observe 2-to-3 times more photons detected than in the simulation. The two most likely explanations for these additional detected optical photons are either the partial transparency of CLB in the UV, or a mild scintillation light emitted by CLB at room temperature. A coincidence time resolution (CTR) of 419 ps FWHM was obtained by triggering on more than 2 fired SiPM cells and after time walk correction. The measured CTR confirms the feasibility to use the Cherenkov light-component for fast timing applications on top of the charge readout, toward full 3D localization.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031894","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}