IEEE transactions on medical robotics and bionics最新文献

{"title":"Force Measurement in Robot-Assisted Endovascular Procedures: A Systematic Review","authors":"Matteo Pantano;Teris Wolf;Philipp Mathea;Nikola Fischer;Steven J. Blacker;Christos Bergeles;Franziska Mathis-Ullrich;Thomas C. Booth;Sebastien Ourselin;Rebecca Fahrig","doi":"10.1109/TMRB.2026.3680997","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3680997","url":null,"abstract":"Use of endovascular therapy continues to expand due to its effectiveness and minimally invasive characteristics, prompting growing interest in teleoperated robotic systems that can extend access in rural settings and improve physicians well-being. These robotic platforms support physicians in the precise manipulation of endovascular devices, yet the importance for providing haptic feedback within such systems remains a topic of debate. Although several recent works outline techniques for delivering force cues to physicians, comparatively little attention has been given to how these forces are sensed on the robotic side. To clarify this aspect, this study reports a systematic literature review conducted under PRISMA guidelines, supplemented with a bias assessment to ensure cross-database consistency. From an initial pool of 641 publications, eight principal categories of endovascular forces were identified, with reported values spanning <inline-formula> <tex-math>$0.17~{mathrm {N }}$ </tex-math></inline-formula> to <inline-formula> <tex-math>$2.61~{mathrm {N }}$ </tex-math></inline-formula> and occasional peaks reaching <inline-formula> <tex-math>$25~{mathrm {N }}$ </tex-math></inline-formula>. Most measurements were obtained through sensors integrated either on the robotic drive mechanisms or directly onto endovascular devices. In addition, the review highlights emerging directions, including human limits on force sensing, hybrid sensing–estimation methods and the need for force characterization in therapeutic devices. Addressing these aspects will be essential to support future force feedback strategies and improve force assessments in both robot-assisted and manual endovascular procedures.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"594-611"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11519497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507936","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":"Disturbance-Observer-Based Sliding Mode Controller for Physical Human–Robot Interaction in Multi-Joint Exoskeleton Robots","authors":"Mostafa Mogharabi;Iman Kardan;Ali Moradi;Alireza Akbarzadeh","doi":"10.1109/TMRB.2026.3680938","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3680938","url":null,"abstract":"A robust and stable physical Human-Robot Interaction (pHRI) controller is crucial for robotic systems, particularly for rehabilitation devices that enable continuous human-robot interaction. A key challenge is to achieve a precise interaction torque that enables the robot to provide active assistance or remain transparent to the user. This paper proposes a novel direct force controller, termed as Integral Sliding Mode Controller with a Disturbance Observer Based on the Interaction torque model (ISMCDOBI), for pHRI control of multi-joint exoskeleton robots. A spring-based contact model is explicitly integrated into the robot dynamics to account for human–robot interaction effects and to compensate for human motion. Unlike SEA-based or position/velocity-based methods that indirectly regulate interaction forces, the proposed approach operates directly in the force/torque domain. An integral sliding mode controller is combined with a disturbance observer that employs a nominal interaction model to account for uncertainties and disturbances. Gravity, friction, and human motion compensators are also incorporated to reduce control effort and improve the tracking of the desired interaction torque. The effectiveness of the proposed framework is validated through realistic MATLAB–OpenSim co-simulation on a coupled human–exoskeleton biomechanical model. The results demonstrate the effectiveness of the proposed interaction controller by maintaining interaction torques less than 0.42 N·m and 0.52 N·m in transparent mode for the hip and knee. Also, a one-way ANOVA analysis depicts the superiority of the proposed controller over two common torque control methods in various modes.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"739-750"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508628","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":"Deep Reinforcement Learning for Robotic Dexterous Grasping Based on Object Shape Completion Using Transformer","authors":"Amit Kumar;Ashish Kumar Bhandari","doi":"10.1109/TMRB.2026.3680932","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3680932","url":null,"abstract":"Robotic grasping techniques separately based on self-learning and sparse point clouds have achieved remarkable performance to grasp an object using parallel grippers. However, humanoid robot’s dexterous grasping is still a challenging problem to grasp an unknown and unseen object in real-time. In this paper, we have proposed a deep reinforcement learning technique which is an improved proximal policy optimization (PPO) combined with another proposed model for object’s shape completion for dexterous grasping using a human-mimic robotic arm. The shape completion has been achieved using the proposed Transformer-based encoder-decoder model by generating complete point cloud 3D data. To further enhance the grasping accuracy, the improved PPO parameters are fine tuned using the fuzzy-based artificial bee colony (ABC) optimization technique. The shape completion model has been trained on the generated mixture of synthetic ShapeNet and ModelNet datasets and non-synthetic YCB-Video dataset. The Kinect depth camera is taken to sense the environment, and hardware experiments have been carried out using the robotic arm developed by us. The grasping performance of the proposed deep reinforcement model has also been observed on unseen but similar objects and the grasping accuracy of 96% is achieved in real-time. The performance of the proposed pipeline strategy is compared with the five state-of-the-art efficient grasping deep learning models through hardware experiments. It is observed that the proposed technique outperforms all other existing techniques with the improvement of grasping time and accuracy. The results are further validated through the statistical Student’s t-test and 95% confidence is achieved.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"627-641"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508718","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":"Markerless Pose Estimation and Tracking on Facial Regions for In Vivo Robot-Assisted Terahertz Sensing","authors":"Shruti Chakraborty;Arturo I. Hernandez-Serrano;Anubhav Dogra;Dominic Jones;Agrima Agarwal;Pietro Valdastri;Emma Pickwell-MacPherson","doi":"10.1109/TMRB.2026.3680973","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3680973","url":null,"abstract":"Terahertz (THz) light’s sensitivity to water, non-ionizing nature, and sub-millimeter resolution make it ideal for medical imaging, particularly for diagnosing and managing skin conditions such as eczema, psoriasis, and skin cancer. Traditional handheld THz probes suffer from positional errors, tremors, and inconsistent contact pressure, which worsen with prolonged use. To address this, robotic THz acquisition systems can be leveraged to segment regions of interest on the skin surface and compute the appropriate probe pose for scanning. This paper proposes a markerless method for automatic identification and estimation of the pose for the placement of the THz probe over high-curvature facial regions. An RGB-D camera is utilized to select, recognize, and track the scanning targets on the patient using an end-to-end network-based model for 3D human face mesh prediction. We then solve for the landing pose to position the probe at normal incidence to the tissue surface at the target scan regions. We evaluated scanning area localization accuracy, motion execution accuracy, and THz acquisition capability using a mannequin head and human volunteers. Results demonstrated target registration error of 1.71 mm, scan target localization accuracy of less than 3 mm and probe landing pose estimation accuracy of <inline-formula> <tex-math>$2.769~pm 3.02$ </tex-math></inline-formula> mm in X, <inline-formula> <tex-math>$6.62~pm ~0.6$ </tex-math></inline-formula> mm in Y and <inline-formula> <tex-math>$4.792~pm ~2.18$ </tex-math></inline-formula> in Z and orientation error of <inline-formula> <tex-math>$1.6pm 0.8$ </tex-math></inline-formula> degrees.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"877-884"},"PeriodicalIF":3.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147923913","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":"Robot-Assisted Stromal Injection in Mice: Precision Delivery and Validation in Experimental Ophthalmic Surgery","authors":"Yanting Luo;Junming Yang;Jun Yang;Juanjuan Zhong;Pisong Yan;Hong He","doi":"10.1109/TMRB.2026.3680989","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3680989","url":null,"abstract":"Because the murine cornea is extremely thin, manual corneal intrastromal injection is technically challenging and lacks a unified standard protocol, and no robot-assisted solution has yet been systematically validated for this model. This paper therefore aims to systematically evaluate the feasibility and safety of a robot-assisted microinjection system for corneal intrastromal injection in mice, to investigate its potential as a standardized technical pathway for precise corneal drug delivery in small-animal models, and to report its first application in experimental ophthalmic surgery in the mouse. A portable high-precision ophthalmic surgical robot was employed. Following a gentle epithelial incision made by the ophthalmologist with a 15° knife, a 41G microneedle mounted on the robotic end-effector advanced at 0.05 mm/s to deliver <inline-formula> <tex-math>$2mu $ </tex-math></inline-formula>L of trypan blue into the stromal layer. Surgical success rates and intraoperative complications, primarily corneal perforation, were recorded. Postoperative evaluation included anterior segment optical coherence tomography (AS-OCT) to measure tunnel length and depth, slit-lamp photography and whole-mount imaging to assess dye diffusion, and frozen histological sections for validation. A total of 84 eyes were included: 44 in the robot-assisted group and 40 in the manual group. The success rate was 81.81% for the robot-assisted group, significantly higher than the 20% in the manual group. The mean tunnel length was <inline-formula> <tex-math>$389.8~pm ~44.47~mu $ </tex-math></inline-formula>m (CV = 11.41%), and the mean depth was <inline-formula> <tex-math>$74.19~pm ~13.01~mu $ </tex-math></inline-formula>m(CV = 17.53%), indicating high consistency. Imaging confirmed homogeneous stromal dye distribution. AS-OCT revealed no significant changes in central corneal thickness with injection volume, except for mild edema at <inline-formula> <tex-math>$3mu $ </tex-math></inline-formula>L (<inline-formula> <tex-math>$p lt 0.05$ </tex-math></inline-formula>). Dye infiltration area expanded significantly with increasing volume (<inline-formula> <tex-math>$3mu $ </tex-math></inline-formula>L<inline-formula> <tex-math>$gt 2mu $ </tex-math></inline-formula>L, <inline-formula> <tex-math>$p lt 0.001$ </tex-math></inline-formula>), without excessive tissue damage. Histology further demonstrated uniform dispersion across stromal lamellae. These findings establish the feasibility of robot-assisted stromal injection in mice, providing a reliable and standardized platform for precise delivery in small-animal ophthalmic models and laying the groundwork for translational ocular therapeutics.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"885-893"},"PeriodicalIF":3.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147923917","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":"Remote Teleoperation of Endovascular Intervention Robots: A Systematic Review","authors":"Xingyu Chen;Yinchao Yang;Nikola Fischer;Harry Robertshaw;Benjamin Jackson;Mohammad Shikh-Bahaei;Christos Bergeles;Thomas C. Booth","doi":"10.1109/TMRB.2026.3681030","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3681030","url":null,"abstract":"Remote robotic-assisted endovascular intervention offers a promising approach to reduce clinician radiation exposure and physical strain, while extending specialized vascular care to geographically distant regions. Despite advancements, teleoperated endovascular intervention remains underexplored, especially for time-sensitive interventions like mechanical thrombectomy for acute stroke. The aim of the current review was to determine the evidence regarding teleoperated endovascular robotic systems, covering technical feasibility, communication infrastructure, and clinical outcomes. The review further identified research gaps and future directions. Following PRISMA guidelines, 16 studies were included that met the inclusion criteria out of 2501 initial search results. We found that teleoperated catheters and guidewires, driven by mechanical or electromagnetic systems, can be navigated across distances up to 7000 km. With robust communication infrastructure, network latency remained within clinically acceptable limits (30-163 ms). Although initial outcomes highlighted 100% procedural success in small-scale human trials, most evidence stemmed from animal or phantom models. Overall, the findings suggest that teleoperated endovascular intervention can reduce occupational hazards, expand patient access to urgent procedures, and optimize resource allocation. Future research should be conducted in low and middle income countries to demonstrate broader geographical access. Ultimately, multi-center clinical trials are required to validate the safety, efficacy, and generalization in diverse clinical settings.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"579-593"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11475205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507934","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":"Multi-View Active Vision System for Robust Surgical Instrument Tracking","authors":"Yu Cai;Xianzhong Xie;Mingzhu Zhu;Bingwei He;Yan Liu","doi":"10.1109/TMRB.2026.3681025","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3681025","url":null,"abstract":"Instrument tracking serves as the backbone of surgical navigation systems, enabling precise real-time localization of surgical tools. However, most visual tracking systems adopt fixed camera configurations that passively observe the surgical instrument. Such static architectures have limited measurement volumes and cannot fully accommodate complex instrument trajectories during surgery, leading to tracking failures. To avoid this situation, we propose a multi-view active visual tracking system to maintain consistent tracking of moving surgical instruments. Multiple cameras observing from different positions localize the instrument by constructing multi-view multi-point perspective equations. Within this positioning framework, an online viewpoint planning algorithm is developed to actively adjust non-ideal viewpoints by modeling suboptimal-view conditions and formulating corresponding inequality constraints. The system employs a visual servoing based camera control mechanism to execute viewpoint adjustments in real time. Following each adjustment, the extrinsic parameters for the camera are recalibrated using an online multi-frame extrinsic calibration method, ensuring consistent spatial accuracy. Both simulation and real-world experiments are conducted, and the proposed system achieves excellent localization performance and exhibits superior robustness against viewpoint limitations compared with fixed-camera systems.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"691-703"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508625","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":"Optically Navigated Surgical Robotic System for Automated In Situ Bioprinting in Confined Anatomical Spaces","authors":"Zitong Wang;Yuncheng Wu;Shuoyu Ji;Zhihong Cao;Liang Feng;Changru Zhang;Jinwu Wang","doi":"10.1109/TMRB.2026.3680970","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3680970","url":null,"abstract":"Robot-assisted <italic>in situ</i> bioprinting allows for the direct deposition of biomaterials at defect sites, offering a minimally invasive alternative to traditional scaffold implantation. Yet, its clinical translation remains hindered by limited accessibility in anatomically constrained regions and the lack of precise control. To address these challenges, this study introduces optical navigation from computer-assisted surgery into <italic>in situ</i> bioprinting to achieve explicit image–patient registration and translate anatomical localization into accurate robot motion. An optically navigated active remote center of motion (RCM) strategy is further developed to support constrained motion in confined anatomical spaces. The system was validated through autonomous <italic>in situ</i> printing of a two-layer scaffold on a femoral defect phantom using a six-degree-of-freedom robotic arm. Quantitative evaluation demonstrated a target registration error of 0.583 mm and a maximum RCM distance error of 0.052 mm, indicating accurate localization and constrained motion. The printed scaffold exhibited high geometric fidelity based on multiple structural metrics. These results highlight the precision and reliability of the proposed framework and its potential for autonomous <italic>in situ</i> bioprinting in deep or subdermal environments.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"856-867"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509308","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":"Multimodal Cross-Attention Fusion of B-Mode Ultrasound and Strain Elastography for Tumor Segmentation in Robotics-Assisted Surgery","authors":"Sara Abkhofte;Anish S. Naidu;Rajni V. Patel;Jayender Jagadeesan","doi":"10.1109/TMRB.2026.3681001","DOIUrl":"10.1109/TMRB.2026.3681001","url":null,"abstract":"Robot-assisted minimally invasive surgery offers high precision and dexterity, opening up new possibilities for intraoperative imaging to improve tumor localization. B-mode ultrasound provides real-time anatomical views, while strain elastography (SE) adds tissue stiffness information. Used together, they provide complementary cues that help increase the accuracy of tumor localization by improving boundary detection. We present a robotics-integrated framework for autonomous ultrasound scanning and multimodal tumor segmentation. The system utilizes the da Vinci Classic surgical robot with the da Vinci Research Kit (dVRK) and a custom ultrasound transducer. It simultaneously acquires paired B-mode and SE images from silicone phantoms and <italic>ex vivo</i> porcine livers. Our architecture extends the conventional U-Net by integrating dual encoders with cross-attention and selective fusion. Cross-attention allows one modality to refine the features of the other, while selective fusion balances their contributions. We performed experiments on a multimodal dataset to validate the effectiveness of the proposed method. Results indicate that our approach outperforms unimodal baselines and standard fusion methods across both segmentation and classification metrics. These findings suggest that multimodal cross-attention and selective fusion can improve tumor segmentation in ultrasound imaging. Ultimately, the goal is to use such advances to support intraoperative guidance for robot-assisted liver surgery.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"679-690"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254736","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":"Modeling of Coupled Input-Force-Displacement Hysteresis in Pneumatic Artificial Muscles","authors":"Yanding Qin;Tianying Dong;Haoqi Zhang;Jianda Han","doi":"10.1109/TMRB.2026.3680981","DOIUrl":"https://doi.org/10.1109/TMRB.2026.3680981","url":null,"abstract":"Pneumatic artificial muscle (PAM) has been widely applied in robotic systems due to its compliance and lightweight characteristics. However, it exhibits complex hysteresis, and strong couplings are observed between the output force and displacement, resulting in a complex three-dimensional (3-D) nonlinear input-force-displacement relationship. Currently, most hysteresis models only describe the two-dimensional (2-D) input-output hysteretic relationships and ignore the complex couplings between the outputs. This significantly degrades the system modeling accuracy and motion control performance, especially when the external load varies obviously. This paper proposes a 3-D hysteresis modeling method based on a two-stage superposed Prandtl窶的shlinskii model, which captures the hysteresis among control input, displacement, and output force, and enables effective force estimation. Experimental validations under multiple scenarios and comparative analysis demonstrate that the proposed model can accurately predict the behavior of the PAM. The proposed method is independent of specific materials and input forms, allowing applicability to the 3D hysteresis modeling of other smart actuators.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"8 2","pages":"868-876"},"PeriodicalIF":3.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147923895","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}