International Journal of Computer Assisted Radiology and Surgery最新文献

{"title":"What are you looking at? Modality contribution in multimodal medical deep learning.","authors":"Christian Gapp, Elias Tappeiner, Martin Welk, Karl Fritscher, Elke R Gizewski, Rainer Schubert","doi":"10.1007/s11548-025-03523-w","DOIUrl":"https://doi.org/10.1007/s11548-025-03523-w","url":null,"abstract":"<p><strong>Purpose: </strong>High dimensional, multimodal data can nowadays be analyzed by huge deep neural networks with little effort. Several fusion methods for bringing together different modalities have been developed. Given the prevalence of high-dimensional, multimodal patient data in medicine, the development of multimodal models marks a significant advancement. However, how these models process information from individual sources in detail is still underexplored.</p><p><strong>Methods: </strong>To this end, we implemented an occlusion-based modality contribution method that is both model- and performance agnostic. This method quantitatively measures the importance of each modality in the dataset for the model to fulfill its task. We applied our method to three different multimodal medical problems for experimental purposes.</p><p><strong>Results: </strong>Herein we found that some networks have modality preferences that tend to unimodal collapses, while some datasets are imbalanced from the ground up. Moreover, we provide fine-grained quantitative and visual attribute importance for each modality.</p><p><strong>Conclusion: </strong>Our metric offers valuable insights that can support the advancement of multimodal model development and dataset creation. By introducing this method, we contribute to the growing field of interpretability in deep learning for multimodal research. This approach helps to facilitate the integration of multimodal AI into clinical practice. Our code is publicly available at https://github.com/ChristianGappGit/MC_MMD.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature distance-weighted adaptive decoupled knowledge distillation for medical image segmentation.","authors":"Xiangchun Yu, Ziyun Xiong, Miaomiao Liang, Lingjuan Yu, Jian Zheng","doi":"10.1007/s11548-025-03346-9","DOIUrl":"10.1007/s11548-025-03346-9","url":null,"abstract":"<p><strong>Purpose: </strong>This paper aims to apply decoupled knowledge distillation (DKD) to medical image segmentation, focusing on transferring knowledge from a high-performance teacher network to a lightweight student network, thereby facilitating model deployment on embedded devices.</p><p><strong>Methods: </strong>We initially decouple the distillation loss into pixel-wise target class knowledge distillation (PTCKD) and pixel-wise non-target class knowledge distillation (PNCKD). Subsequently, to address the limitations of the fixed weight paradigm in PTCKD, we propose a novel feature distance-weighted adaptive decoupled knowledge distillation (FDWA-DKD) method. FDWA-DKD quantifies the feature disparity between student and teacher, generating instance-level adaptive weights for PTCKD. We design a feature distance weighting (FDW) module that dynamically calculates feature distance to obtain adaptive weights, integrating feature space distance information into logit distillation. Lastly, we introduce a class-wise feature probability distribution loss to encourage the student to mimic the teacher's spatial distribution.</p><p><strong>Results: </strong>Extensive experiments conducted on the Synapse and FLARE22 datasets demonstrate that our proposed FDWA-DKD achieves satisfactory performance, yielding optimal Dice scores and, in some instances, surpassing the performance of the teacher network. Ablation studies further validate the effectiveness of each module within our proposed method.</p><p><strong>Conclusion: </strong>Our method overcomes the constraints of traditional distillation methods by offering instance-level adaptive learning weights tailored to PTCKD. By quantifying student-teacher feature disparity and minimizing class-wise feature probability distribution loss, our method outperforms other distillation methods.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2153-2165"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvements in dementia classification for brain SPECT volumes using vision transformer and the Brodmann areas.","authors":"Hirotaka Wakao, Tomomichi Iizuka, Akinobu Shimizu","doi":"10.1007/s11548-025-03365-6","DOIUrl":"10.1007/s11548-025-03365-6","url":null,"abstract":"<p><strong>Purpose: </strong>This study proposes a vision transformer (ViT)-based model for dementia classification, able to classify representative dementia with Alzheimer's disease, dementia with Lewy bodies, frontotemporal dementia, and healthy controls using brain single-photon emission computed tomography (SPECT) images. The proposed method allows for an input based on the anatomical structure of the brain and the efficient use of five different SPECT images.</p><p><strong>Methods: </strong>The proposed model comprises a linear projection of input patches, eight transformer encoder layers, and a multilayered perceptron for classification with the following features: 1. diverse feature extraction with a multi-head structure for five different SPECT images; 2. Brodmann area-based input patch reflecting the anatomical structure of the brain; 3. cross-attention to fusion of diverse features.</p><p><strong>Results: </strong>The proposed method achieved a classification accuracy of 85.89% for 418 SPECT images from real clinical cases, significantly outperforming previous studies. Ablation studies were conducted to investigate the validity of each contribution, in which the consistency between the model's attention map and the physician's attention region was analyzed in detail.</p><p><strong>Conclusion: </strong>The proposed ViT-based model demonstrated superior dementia classification accuracy compared to previous methods, and is thus expected to contribute to early diagnosis and treatment of dementia using SPECT imaging. In the future, we aim to further improve the accuracy through the incorporation of patient clinical information.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2095-2105"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite element analyses, 3D-printed guides and navigation system optimizes fragment reorientation for periacetabular osteotomy.","authors":"Sheng-Hsun Lee, Sergio Guarin Perez, Adam J Wentworth, Timothy L Rossman, Rafael J Sierra","doi":"10.1007/s11548-025-03376-3","DOIUrl":"10.1007/s11548-025-03376-3","url":null,"abstract":"<p><strong>Purpose: </strong>Periacetabular osteotomy (PAO) is an effective treatment to correct developmental dysplasia of the hip (DDH). Traditionally, the goal of correction during PAO is based on parameters measured on 2-dimensional images. The aim of the study is to introduce an optimized workflow of PAO in DDH patients by means of personalized correction goal and accuracy of execution.</p><p><strong>Methods: </strong>Five patients with DDH were prospectively enrolled. Preoperative computed tomography was performed. Surgical planning was done by the treating surgeon and engineers. The planned correction involved reorienting the osteotomized fragment to achieve a target lateral center-edge angle (LCEA) of 25°-40°.The pelvic model with the preoperative and planned correction was analyzed by finite element analysis, which simulated single-leg stance condition. Average and maximal acetabular stresses in different anatomical areas were calculated and are presented as a dashboard at ± 3° increments to help the surgeon determine the ideal correction. To ensure accuracy of the osteotomy and correction as planned, 3D-printed cutting and reorientation guides were used.</p><p><strong>Results: </strong>Average operation time (101 ± 23 min) and blood loss (651 ± 176 ml) were comparable to previous reports. Radiographic parameters improved significantly, including LCEA (20.0^° ± 6.4^° vs. 30.2^° ± 3.1^°, p = 0.037) and AI (12.5^° ± 3.1^° vs. 0.8^° ± 1.6^°, p = 0.001). The planned correction was similar to the final correction (LCEA planned 31.1^° ± 2.0^° vs. final 30.2^° ± 3.1^°, p = 0.268; AI planned 1.8^° ± 1.5^° vs. final 0.8^° ± 1.6^°, p = 0.349). During an average follow-up period of 1.2 years, all osteotomies healed and these patients reported a significant reduction in mean global pain scale from 70 preoperatively to 23 postoperatively (p = 0.016).</p><p><strong>Conclusion: </strong>The workflow with FEA simulations to optimize mechanical stress and 3D-printed cutting guides to achieve accurate execution was an effective and safe approach to optimize DDH treatment. Further refinements and further evaluation of navigation systems aimed at obtaining planned correction is necessary.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2031-2041"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A shape completion model for corrective osteotomy of distal radius malunion.","authors":"Camiel J Smees, Judith Olde Heuvel, Stein van der Heide, Esmee D van Uum, Anne J H Vochteloo, Gabriëlle J M Tuijthof","doi":"10.1007/s11548-025-03454-6","DOIUrl":"10.1007/s11548-025-03454-6","url":null,"abstract":"<p><strong>Purpose: </strong>When performing 3D planning for osteotomies in patients with distal radius malunion, the contralateral radius is commonly used as a template for reconstruction. However, in approximately 10% of the cases, the contralateral radius is not suitable for use. A shape completion model may provide an alternative by generating a healthy radius model based on the proximal part of the malunited bone. The aim of this study is to develop and clinically evaluate such a shape completion model.</p><p><strong>Method: </strong>A total of 100 segmented CT scans of healthy radii were used, with 80 scans used to train a statistical shape model (SSM). This SSM formed the base for a shape completion model capable of predicting the distal 12% based on the proximal 88%. Hyperparameters were optimized using 10 segmented 3D models, and the remaining 10 models were reserved for testing the performance of the shape completion model.</p><p><strong>Results: </strong>The shape completion model consistently produced clinically viable 3D reconstructions. The mean absolute errors between the predicted and corresponding reference models in the rotational errors were 2.6 ± 1.7° for radial inclination, 3.6 ± 2.2° for volar tilt, and 2.6 ± 2.8° for axial rotation. Translational errors were 0.7 ± 0.6 mm in dorsal shift, 0.8 ± 0.5 mm in radial shift, and 1.7 ± 1.1 mm in lengthening.</p><p><strong>Conclusion: </strong>This study successfully developed a shape completion model capable of reconstructing healthy 3D radius models based on the proximal bone. The observed errors indicate that the model is viable for use in 3D planning for patients lacking a healthy contralateral radius. However, routine use in patients with a healthy contralateral radius is not yet advised, as error margins exceed bilateral differences observed in healthy populations. The most clinically relevant error found in the model, length mismatch, can be easily corrected during 3D planning if the ipsilateral ulna remains intact.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2075-2085"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The quiet eye phenomenon in minimally invasive surgery.","authors":"Alaa Eldin Abdelaal, Rachelle Van Rumpt, Sayem Zaman, Irene Tong, Anthony Jarc, Gary L Gallia, Masaru Ishii, Gregory D Hager, Septimiu E Salcudean","doi":"10.1007/s11548-025-03367-4","DOIUrl":"10.1007/s11548-025-03367-4","url":null,"abstract":"<p><strong>Purpose: </strong>The quiet eye (QE) behavior is a gaze behavior that has been extensively studied in sports training and has been associated with higher level of expertise in multiple sports. In this paper, we report our observations of this gaze behavior in two minimally invasive surgery settings and we report how this behavior changes based on task success and the surgeon's expertise level.</p><p><strong>Methods: </strong>We investigated the QE behavior in two independently collected data sets in a sinus surgery setting and a robotic surgery setting. The sinus surgery data set was used to study how the QE behavior changes in successful and unsuccessful tasks. The robotic surgery data set was used to study how the QE behavior changes based on the surgeon's expertise level.</p><p><strong>Results: </strong>Using the sinus surgery data set, our results show that the QE behavior is more likely to occur and that its duration is significantly longer, in successful tasks, compared with unsuccessful ones. Using the robotic surgery data set, our results show similar trends in tasks performed by experienced surgeons, compared with less experienced ones.</p><p><strong>Conclusion: </strong>The results of our study open the door to use the QE behavior in training and skill assessment in the explored minimally invasive surgery settings. Training novices to adopt the QE behavior can potentially improve their motor skill learning, replicating the success of doing so in sports training. In addition, the well-defined characteristics of the QE behavior can provide an explainable way to distinguish between different skill levels in minimally invasive surgery.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2087-2093"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144007503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of a novel, automated 2D-3D registration software for measuring cup position in total hip arthroplasty.","authors":"Jevan Arulampalam, Moritz F Ehlke, Christopher Plaskos, Qing Li, Catherine Z Stambouzou, James A Sullivan, Michael I Solomon, Eric M Slotkin","doi":"10.1007/s11548-025-03389-y","DOIUrl":"10.1007/s11548-025-03389-y","url":null,"abstract":"<p><strong>Purpose: </strong>This study evaluated the accuracy of an automated 2D-3D registration software for matching preoperative 3D models of the pelvis and acetabular component to intraoperative 2D fluoroscopy images in total hip arthroplasty (THA).</p><p><strong>Methods: </strong>We developed a 2D-3D registration software that registers a 3D model of the pelvis from preoperative CT and a 3D model of the acetabular implant to intraoperative fluoroscopic imaging, thereby calculating the implant position relative to the 3D pelvic reference frame. A total of 145 datasets were used including 65 digitally reconstructed radiographs, 20 dry bone phantoms datasets and 60 clinical datasets with preoperative CT and intraoperative fluoroscopy imaging. Achieved acetabular positions from the clinical images were determined from post-operative CT using a 3D/3D registration method. Accuracy was assessed by comparing the calculated acetabular position from the 2D-3D software to the ground truth data.</p><p><strong>Results: </strong>Mean absolute difference between ground truth and the 2D-3D software was 1.9° [signed error range: -4.4, 4.8] for inclination, 1.5° [-7.3, 4.1] for anteversion, 1.6 mm [-5, 3.8] for cup height and 1.8 mm [-7.3, 4.1] for depth across all datasets. In total, 100% of inclination results and 98% of anteversion results were within 5° while 90% of height and 81% of depth results were within 3 mm.</p><p><strong>Conclusion: </strong>We validated the accuracy of an automated 2D-3D registration software for use in THA. While our method requires preoperative data from CT, the results are comparable to robotics and image-based navigation, and present a promising, simple technology that can be easily integrated into an operating room for THA.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2043-2051"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144008373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based robotic ultrasound 3D tracking for capsule robot in GI tract.","authors":"Xiaoyun Liu, Changyan He, Mulan Wu, Ann Ping, Anna Zavodni, Naomi Matsuura, Eric Diller","doi":"10.1007/s11548-025-03445-7","DOIUrl":"10.1007/s11548-025-03445-7","url":null,"abstract":"<p><strong>Purpose: </strong>Ultrasound (US) imaging is a promising modality for real-time monitoring of robotic capsule endoscopes navigating through the gastrointestinal (GI) tract. It offers high temporal resolution and safety but is limited by a narrow field of view, low visibility in gas-filled regions and challenges in detecting out-of-plane motions. This work addresses these issues by proposing a novel robotic ultrasound tracking system capable of long-distance 3D tracking and active re-localization when the capsule is lost due to motion or artifacts.</p><p><strong>Methods: </strong>We develop a hybrid deep learning-based tracking framework combining convolutional neural networks (CNNs) and a transformer backbone. The CNN component efficiently encodes spatial features, while the transformer captures long-range contextual dependencies in B-mode US images. This model is integrated with a robotic arm that adaptively scans and tracks the capsule. The system's performance is evaluated using ex vivo colon phantoms under varying imaging conditions, with physical perturbations introduced to simulate realistic clinical scenarios.</p><p><strong>Results: </strong>The proposed system achieved continuous 3D tracking over distances exceeding 90 cm, with a mean centroid localization error of 1.5 mm and over 90% detection accuracy. We demonstrated 3D tracking in a more complex workspace featuring two curved sections to simulate anatomical challenges. This suggests the strong resilience of the tracking system to motion-induced artifacts and geometric variability. The system maintained real-time tracking at 9-12 FPS and successfully re-localized the capsule within seconds after tracking loss, even under gas artifacts and acoustic shadowing.</p><p><strong>Conclusion: </strong>This study presents a hybrid CNN-transformer system for automatic, real-time 3D ultrasound tracking of capsule robots over long distances. The method reliably handles occlusions, view loss and image artifacts, offering millimeter-level tracking accuracy. It significantly reduces clinical workload through autonomous detection and re-localization. Future work includes improving probe-tissue interaction handling and validating performance in live animal and human trials to assess physiological impacts.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2011-2018"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Noctopus: a novel device and method for patient registration and navigation in image-guided cranial surgery.","authors":"Yusuf Özbek, Zoltán Bárdosi, Wolfgang Freysinger","doi":"10.1007/s11548-024-03251-7","DOIUrl":"10.1007/s11548-024-03251-7","url":null,"abstract":"","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2189"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BEA-CACE: branch-endpoint-aware double-DQN for coronary artery centerline extraction in CT angiography images.","authors":"Yuyang Zhang, Gongning Luo, Wei Wang, Shaodong Cao, Suyu Dong, Daren Yu, Xiaoyun Wang, Kuanquan Wang","doi":"10.1007/s11548-025-03483-1","DOIUrl":"10.1007/s11548-025-03483-1","url":null,"abstract":"<p><strong>Purpose: </strong>In order to automate the centerline extraction of the coronary tree, three challenges must be addressed: tracking branches automatically, passing through plaques successfully, and detecting endpoints accurately. This study aims to develop a method to solve the three challenges.</p><p><strong>Methods: </strong>We propose a branch-endpoint-aware coronary centerline extraction framework. The framework consists of a deep reinforcement learning-based tracker and a 3D dilated CNN-based detector. The tracker is designed to predict the actions of an agent with the objective of tracking the centerline. The detector identifies bifurcation points and endpoints, assisting the tracker in tracking branches and terminating the tracking process automatically. The detector can also estimate the radius values of the coronary artery.</p><p><strong>Results: </strong>The method achieves the state-of-the-art performance in both the centerline extraction and radius estimate. Furthermore, the method necessitates minimal user interaction to extract a coronary tree, a feature that surpasses other interactive methods.</p><p><strong>Conclusion: </strong>The method can track branches automatically, pass through plaques successfully and detect endpoints accurately. Compared with other interactive methods that require multiple seeds, our method only needs one seed to extract the entire coronary tree.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2131-2143"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144765756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}