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arXiv 2022年

作者： Ge, Baoliang He, Yanping Deng, Mo Rahman, Md Habibur Wang, Yijin Wu, Ziling Yang, Yongliang Kuang, Cuifang Wong, Chung Hong N. Chan, Michael K. Ho, Yi-Ping Duan, Liting Yaqoob, Zahid So, Peter T.C. Barbastathis, George Zhou, Renjie Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Laser Biomedical Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Department of Biomedical Engineering The Chinese University of Hong Kong Shatin New Territories Hong Kong School of Artificial Intelligence Science and Technology University of Shanghai for Science and Technology Shanghai200093 China Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai200093 China The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou310027 China School of Life Sciences The Chinese University of Hong Kong Shatin New Territories Hong Kong Centre for Novel Biomaterials The Chinese University of Hong Kong Shatin New Territories Hong Kong Hong Kong Branch of CAS Center for Excellence in Animal Evolution and Genetics The Chinese University of Hong Kong Shatin New Territories Hong Kong Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Singapore-MIT Alliance for Research and Technology Centre Singapore Singapore-MIT Alliance for Research and Technology Centre Singapore

Rapid, comprehensive, and accurate cell phenotyping without compromising viability, is crucial to many important biomedical applications, including stem-cell therapy, drug screening, and liquid biopsy. Typical image cytometry methods acquire two-dimensional (2D) fluorescence images, where the fluorescence labelling process may damage living cells, and the information from 2D images is not comprehensive enough for precise cell analysis. Although three-dimensional (3D) label-free image cytometry holds great promise, its high throughput development faces several technical challenges. Here, we report eXpress Single-frame CYtometer through Tomographic phasE (xSCYTE), which reconstructs 3D Refractive Index (RI) maps of cells with diffraction-limited resolution. xSCYTE is built on a versatile quantitative phase microscope, whose label-free imaging nature allows in-situ, long-time, reusable live-cell analysis. Further, with angle-multiplexing illumination and a pre-trained physics-incorporating Deep Neural Network, xSCYTE can map the 3D RI distribution of a cell by acquiring only a single frame that allows rapid image reconstruction and holds the potential for real-time analysis. By flowing large quantities of cells through a microfluidic channel and equipping xSCYTE with a high-speed camera, we have demonstrated an unprecedented 3D imaging throughput of over 20,000 cells/second while providing sufficient morphological information to distinguish different cellular species. The biomedical application potential of xSCYTE has been evaluated by visualizing and quantifying shear-induced 3D transient deformation of red blood cells that can be correlated with several blood pathologies. With these high-speed and high-precision imaging capabilities empowered by artificial intelligence, we envision xSCYTE may open up many new avenues of biomedical investigations and industries, such as multi-omic assays and quality control during cellular therapeutic manufacturing. © 2022, CC BY-NC-S

关键词： High speed cameras

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Single-Cell Mechanical Characterization in Constriction-Based Cytometry

SSRN

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SSRN 2023年

作者： Abarca-Ortega, Aldo González-Bermúdez, Blanca Castro-Domínguez, Cristina Álvarez-López, Aroa Díaz-Alcaraz, Cristina Garvía-Rodríguez, María Guinea, Gustavo V. Plaza, Gustavo R. Center for Biomedical Technology Universidad Politécnica de Madrid MadridE-28223 Spain Departamento de Ciencia de Materiales ETSI de Caminos Canales y Puertos. Universidad Politécnica de Madrid MadridE-28040 Spain Departamento de Ingeniería Mecánica Universidad de Santiago de Chile Av. Bernardo O’Higgins 3363 State Two Santiago de Chile Chile Instituto de Investigación Sanitaria Hospital Clínico San Carlos IdISSC Madrid Spain Neuro-Computing and Neuro-Robotics Research Group Complutense University of Madrid MadridE-28040 Spain Madrid Spain

Mechanical characterization of suspended cells by constriction-based microfluidic devices has currently various limitations related to the available analysis models. First, the mechanical parameters can only be obtained for a population of cells, not for single cells;second, the variability of the results is related to the fact that the models do not take into account non-linearity due to contact and relative-size effects. In this work, we propose a new methodology to analyze the experiments. This approach is based on numerical simulations to describe fluid forces and cell deformation and on an extension of the quasi-linear viscoelastic model developed by Fung. The cells are considered visco-hyperelastic, homogeneous, and isotropic. The approach allows for assessing the mechanical parameters of individual cells, notably increasing the power of the constriction-based microfluidic technique. A practical procedure to compute mechanical parameters is proposed and demonstrated by analyzing experiments with suspended cells. The methodology developed in this work provides a convenient tool to overcome critical limitations of the state of the art and to leverage the potential of these microfluidic devices. © 2023, The Authors. All rights reserved.

关键词： Microfluidics

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V2o5 Nanofiber Like Sheets Assembled on Nitrogen Doped Multiwalled Carbon Nanotubes/Carboxy Methyl Cellulose Composite Solid State Electrodes for Supercapacitor Applications

SSRN

引用

SSRN 2022年

作者： Ramesh, Sivalingam Yadav, H.M. Bathula, Chinna Palem, Ramasubba Reddy Sivasamy, Arumugam Kathalingam, A. Kim, Hyun-Seok Kim, Heung Soo Department of Mechanical Robotics and Energy Engineering Dongguk University –Seoul Pildong Jung-gu Seoul04620 Korea Republic of School of Nanoscience and Technology Shivaji University Kolhapur416004 India Division of Electronics and Electrical Engineering Dongguk University-Seoul Pildong-ro 1 gil Jung-gu Seoul04620 Korea Republic of Department of Medical Biotechnology Dongguk University Biomedical Campus 32 Gyeonggi 10326 Korea Republic of Adyar Chennai600020 India Research Center Dongguk University Seoul04620 Korea Republic of

The various 2D two-dimensional materials were synthesized great consideration due to the exceptional properties and excellent performance in the, energy storage, catalysis, electronics and energy renovation process. These materials provide the greater surface and ultrathin sheets morphology and enhances the asymmetric supercapacitor applications. Therefore, the controlled synthesis of nanofiber like sheet V2O5@N-MWCNT and V2O5@N-MWCNT/CMC composites for enhancement of asymmetric supercapacitor applications. The electrochemical properties of the composite’s electrodes were explored by cyclic voltammetry, galvanostatic charge/discharge and electrochemical impedance spectroscopy studies. The composite materials were demonstrated a specific capacitance 138F g-1 at 1A g-1 applied current density, which is much superior than that of pristine V2O5 materials. Furthermore, the composite electrodes show the excellent cyclic stability up to 5000 cycles with 98.6% retention in presence of 1M H2SO4 electrolyte. The enhancement of electrochemical behavior due to the increasing surface area and morphological properties of synthesized composite. © 2022, The Authors. All rights reserved.

关键词： Doping (additives)

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PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery

arXiv

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arXiv 2024年

作者： Das, Adrito Khan, Danyal Z. Psychogyios, Dimitrios Zhang, Yitong Hanrahan, John G. Vasconcelos, Francisco Pang, You Chen, Zhen Wu, Jinlin Zou, Xiaoyang Zheng, Guoyan Qayyum, Abdul Mazher, Moona Razzak, Imran Li, Tianbin Ye, Jin He, Junjun Plotka, Szymon Kaleta, Joanna Yamlahi, Amine Jund, Antoine Godau, Patrick Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Rivoir, Dominik Pérez, Alejandra Rodriguez, Santiago Arbeláez, Pablo Stoyanov, Danail Marcus, Hani J. Bano, Sophia Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department of Neurosurgery National Hospital for Neurology and Neurosurgery London United Kingdom HKISI CAS China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Heart and Lung Institute Faculty of Medicine Imperial College London United Kingdom Centre for Medical Image Computing University College London London United Kingdom University of New South Wales Sydney Australia Shanghai AI Lab Shanghai China Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center University of Amsterdam Amsterdam Netherlands Sano Center for Computational Medicine Krakow Poland Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg a partnership between DKFZ University Hospital Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Muroran Institute of Technology Hokkaido Japan Niigata University of Health and Welfare Niigata Japan Konica Minolta Inc. Osaka Japan National Center for Tumor Diseases Dresden Germany Centre for Tactile Internet TUD Dresden Germany Universidad de los Andes Bogota Colombia DKFZ UKDD TUD Germany

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed;and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery;during live surgery;and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision;and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://***/10.5522/04/26531686. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Correction to: Development of a dedicated 3D printed myocardial perfusion phantom: proof-of-concept in dynamic SPECT

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Medical & biological engineering & computing 2022年第6期60卷 1551-1553页

作者： Marije E Kamphuis Gijs J de Vries Henny Kuipers Marloes Saaltink Jacqueline Verschoor Marcel J W Greuter Riemer H J A Slart Cornelis H Slump Multi-Modality Medical Imaging (M3i) Group Faculty of Science and Technology Technical Medical Centre 2386 University of Twente P.O. Box 217 7500 AE Enschede The Netherlands. kamphuis@utwente.nl. Robotics and Mechatronics (RaM) Group Faculty of Electrical Engineering Mathematics and Computer Science Technical Medical Centre University of Twente Enschede The Netherlands. kamphuis@utwente.nl. Robotics and Mechatronics (RaM) Group Faculty of Electrical Engineering Mathematics and Computer Science Technical Medical Centre University of Twente Enschede The Netherlands. Department of Nuclear Medicine Ziekenhuis Groep Twente Hengelo The Netherlands. Medical Imaging Centre Department of Nuclear Medicine and Molecular Imaging University Medical Center Groningen University of Groningen Groningen The Netherlands. Biomedical Photonic Imaging Group Faculty of Science and Technology Technical Medical Centre University of Twente Enschede The Netherlands.

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images

arXiv

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arXiv 2024年

作者： Bai, Jieyun Zhou, Zihao Ou, Zhanhong Koehler, Gregor Stock, Raphael Maier-Hein, Klaus Elbatel, Marawan Martí, Robert Li, Xiaomeng Qiu, Yaoyang Gou, Panjie Chen, Gongping Zhao, Lei Zhang, Jianxun Dai, Yu Wang, Fangyijie Silvestre, Guénolé Curran, Kathleen Sun, Hongkun Xu, Jing Cai, Pengzhou Jiang, Lu Lan, Libin Ni, Dong Zhong, Mei Chen, Gaowen Campello, Víctor M. Lu, Yaosheng Lekadir, Karim Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand Heidelberg Germany Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Computer Vision and Robotics Group University of Girona Girona Spain Co. LTD Beijing China College of Artificial Intelligence Nankai University Tianjin China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Medicine University College Dublin Dublin Ireland School of Computer Science University College Dublin Dublin Ireland School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China School of Computer Science & Engineering Chongqing University of Technology Chongqing China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China NanFang Hospital of Southern Medical University Guangzhou China Zhujiang Hospital of Southern Medical University Guangzhou China Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain Institute The University of Auckland Private Bag 92019 Auckland1142 New Zealand

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging. © 2024, CC0.

关键词： Ultrasonic imaging

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Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge

arXiv

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arXiv 2024年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Wang, Ershuai Zhou, Qin Huang, Ziyan Lyu, Pengju He, Jian Wang, Bo The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Center UHN AI Hub University Health Network Vector Institute Toronto Canada Lenovo Research Beijing China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Ocean University of China Qingdao China Department of Research and Development ShenZhen Yorktal DMIT Co. LTD. Shenzhen China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Jiao Tong University Shanghai AI Laboratory Shanghai China City University of Macau China Hanglok-Tech Co. Ltd. Hengqin China The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing China The Peter Munk Cardiac Center University Health Network Department of Laboratory Medicine and Pathobiology Department of Computer Science University of Toronto Vector Institute UHN AI Hub University Health Network Toronto Canada

Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first international competition on abdominal organ and pan-cancer segmentation by providing a large-scale and diverse dataset, including 4650 CT scans with various cancer types from over 40 medical centers. The winning team established a new state-of-the-art with a deep learning-based cascaded framework, achieving average Dice Similarity Coefficient (DSC) scores of 92.3% for organs and 64.9% for lesions on the hidden multi-national testing set. The dataset and code of top teams are publicly available, offering a benchmark platform to drive further innovations https://***/competitions/12239. © 2024, CC BY-NC-ND.

关键词： Lung cancer

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Human brain local field potential recordings during a battery of multilingual cognitive and eye-tracking tasks

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Scientific Data 2025年第1期12卷 1-14页

作者： Cimbalnik, J. Garcia-Salinas, J.S. Hamedi, N. Prathapagiri, S. Sarmiento Rivera, L.F. Galanina, M. Dolezal, J. Jurkovicova, L. Daniel, P. Kojan, M. Roman, R. Pail, M. Sredniawa, W. Fortuna, W. Sluzewska-Niedzwiedz, M. Smarzewska, K. Reinacher, P. Weiser, A. Skok, T. Piernicki, T. Orzol, A. Lier, A. Seifzadeh, S. Kostek, Bozena Czyzewski, Andrzej Tabakow, Pawel Brazdil, Milan Kucewicz, Michal T. Department of Biomedical Engineering International Clinical Research Center St. Anne’s University Hospital Brno Brno Czech Republic Brain & Mind Electrophysiology laboratory BioTechMed Center Multimedia Systems Department Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic Brno Epilepsy Center Department of Neurology St. Anne’s University Hospital Faculty of Medicine Masaryk University member of ERN EpiCARE Brno Czech Republic Central European Institute of Technology Masaryk University Brno Czech Republic Department of Neurosurgery Wroclaw Medical University Wroclaw Poland Department of Neurology Wroclaw Medical University Wroclaw Poland Department of Physiology & Biomedical Engineering Mayo Clinic Rochester MN United States

Intracranial human brain recordings from multiple implanted depth electrodes using stereo-EEG (sEEG) technology for seizure localization provide unique local field potential signals (LFP) sampled with standard macro- and special micro-electrode contacts. Over one hundred macro- and micro-contact LFP signals localized in particular brain regions were recorded from each sEEG monitoring case as patients engaged in an automated battery of verbal memory and non-verbal gaze movement tasks. Subject eye and vocal responses in both visual and auditory task versions were automatically detected in Polish, Czech, and Slovak languages with accurate timing of the correct and incorrect verbal responses using our web-based transcription tool. The behavioral events, LFP and pupillometric signals were synchronized and stored in a standard BIDS data structure with corresponding metadata. Each dataset contains recordings from at least one battery task performed over at least one day. The same set of 180 common nouns in the three languages was used across different battery tasks and recording days to enable the analysis of selective responses to specific word stimuli. © The Author(s) 2025.

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Safer Motion Planning of Steerable Needles via a Shaft-to-Tissue Force Model

arXiv

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arXiv 2021年

作者： Bentley, Michael Rucker, Caleb Reddy, Chakravarthy Salzman, Oren Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States The Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KnoxvilleTN37996 United States Huntsman Cancer Institute and School of Medicine University of Utah Salt Lake CityUT84112 United States Department of Computer Science Technion - Israel Institute of Technology Technion City Haifa3200003 Israel

Steerable needles are capable of accurately targeting difficult-to-reach clinical sites in the body. By bending around sensitive anatomical structures, steerable needles have the potential to reduce the invasiveness of many medical procedures. However, inserting these needles with curved trajectories increases the risk of tissue damage due to perpendicular forces exerted on the surrounding tissue by the needle's shaft, potentially resulting in lateral shearing through tissue. Such forces can cause significant damage to surrounding tissue, negatively affecting patient outcomes. In this work, we derive a tissue and needle force model based on a Cosserat string formulation, which describes the normal forces and frictional forces along the shaft as a function of the planned needle path, friction model and parameters, and tip piercing force. We propose this new force model and associated cost function as a safer and more clinically relevant metric than those currently used in motion planning for steerable needles. We fit and validate our model through physical needle robot experiments in a gel phantom. We use this force model to define a bottleneck cost function for motion planning and evaluate it against the commonly used path-length cost function in hundreds of randomly generated 3-D environments. Plans generated with our force-based cost show a 62% reduction in the peak modeled tissue force with only a 0.07% increase in length on average compared to using the path-length cost in planning. Additionally, we demonstrate the ability to plan motions with our force-based cost function in a lung tumor biopsy scenario from a segmented computed tomography (CT) scan. By planning motions for the needle that aim to minimize the modeled needle-to-tissue force explicitly, our method plans needle paths that may reduce the risk of significant tissue damage while still reaching desired targets in the body. © 2021, CC BY.

关键词： Needles

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