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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Fabelo, Himar Halicek, Martin Ortega, Samuel Szolna, Adam Morera, Jesus Sarmiento, Roberto Callico, Gustavo M. Fei, Baowei Univ Texas Dallas Dept Bioengn Richardson TX 75083 USA Univ Las Palmas Gran Canaria Inst Appl Microelect Las Palmas Gran Canaria Spain Emory Univ Dept Biomed Engn Atlanta GA 30322 USA Georgia Inst Tech Atlanta GA USA Univ Hosp Doctor Negrin Gran Canaria Dept Neurosurg Las Palmas Gran Canaria Spain Univ Texas Southwestern Med Ctr Dallas Adv Imaging Res Ctr Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dallas Dept Radiol Dallas TX 75390 USA

ISBN: (纸本)9781510625501

Brain cancer surgery has the goal of performing an accurate resection of the tumor and preserving as much as possible the quality of life of the patient. There is a clinical need to develop non-invasive techniques that can provide reliable assistance for tumor resection in real-time during surgical procedures. Hyperspectral imaging (HSI) arises as a new, non-invasive and non-ionizing technique that can assist neurosurgeons during this difficult task. In this paper, we explore the use of deep learning (DL) techniques for processing hyperspectral (HS) images of in-vivo human brain tissue. We developed a surgical aid visualization system capable of offering guidance to the operating surgeon to achieve a successful and accurate tumor resection. The employed HS database is composed of 26 in-vivo hypercubes from 16 different human patients, among which 258,810 labelled pixels were used for evaluation. The proposed DL methods achieve an overall accuracy of 95% and 85% for binary and multiclass classifications, respectively. The proposed visualization system is able to generate a classification map that is formed by the combination of the DL map and an unsupervised clustering via a majority voting algorithm. This map can be adjusted by the operating surgeon to find the suitable configuration for the current situation during the surgical procedure.

关键词： Brain tumor cancer surgery hyperspectral imaging intraoperative imaging deep learning supervised classification convolutional neural network (CNN) classifier

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Towards a Multi-imager Compatible Continuum Robot with Improved Dynamics Driven by Modular SMA

Towards a Multi-imager Compatible Continuum Robot with Impro...

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IEEE International conference on robotics and Automation (ICRA)

作者： Qingpeng Ding Yongkang Lu Andre Kyme Shing Shin Cheng CUHK T Stone Robotics Institute The Chinese University of Hong Kong School of Biomedical Engineering The University of Sydney Sydney NSW Australia Shun Hing Institute of Advanced Engineering and Multi-Scale Medical Robotics Center The Chinese University of Hong Kong

Most existing surgical robots employ straight rigid instruments and are not compatible with imaging modalities, especially magnetic resonance imaging (MRI) that presents restrictive constraints on the robot and actuator materials. Employing continuum distal end effector and fulfilling multi-imager compatibility will potentially lead to wide adoption of surgical robots in intraoperative image-guided minimally invasive surgery (MIS). This paper introduces a 3-dimensional (3D) printed polyamide continuum robot with 2-degree of freedom, driven by modular shape memory alloy (SMA) spring actuators that enable real-time distal manipulation in 3D workspace. The multi-imager compatibility is conditionally satisfied by utilizing no ferromagnetic materials and MRI-conditional actuators in the robotic system. The use of modular SMA allows repeatable configuration setting, easy integration with active cooling strategies, and facilitates the creation of a sterile barrier between the end effector module and the actuation module. Detailed design of the robot, kinematics modeling and actuator modeling are discussed in the paper. We experimentally verified the robot kinematics and evaluated the dynamic performance of the continuum robot. It features an operating bandwidth of 0.12 Hz at -3 dB and a root-mean-square error of 0.98 mm under model predictive control when tracking sinusoidal signals, both with ±10 mm amplitude (distal bending angle of ±80^◦). As a demonstration, the robot was used as a flexible endoscope manipulator and steered under inputs from a joystick to show its real-time performance.

关键词： Actuators Three-dimensional displays medical robotics Endoscopes Robot kinematics Magnetic resonance imaging End effectors

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Magnetic resonance image-based modeling for neurosurgical interventions

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MCB Molecular and Cellular Biomechanics 2019年第4期16卷 245-251页

作者： Li, Yongqiang Lai, Changxin Zhang, Chengchen Singer, Alexa Qiu, Suhao Sun, Boming Sacks, Michael S. Feng, Yuan Institute for Medical Imaging Technology School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200030 China Department of Functional Neurosurgery Rujin Hospital Affiliated Shanghai Jiao Tong University School of Medicine Shanghai200030 China Department of Biomedical Engineering University of Texas at Austin AustinTX78712 United States

Surgeries such as implantation of deep brain stimulation devices require accurate placement of devices within the brain. Because placement affects performance, image guidance and robotic assistance techniques have been widely adopted. These methods require accurate prediction of brain deformation during and following implantation. In this study, a magnetic resonance (MR) image-based finite element (FE) model was proposed by using a coupled Eulerian-Lagrangian method. Anatomical accuracy was achieved by mapping image voxels directly to the volumetric mesh space. The potential utility was demonstrated by evaluating the effect of different surgical approaches on the deformation of the corpus callosum (CC) region. The results showed that the maximum displacement of the corpus callosum increase with an increase of interventional angle with respect to the midline. The maximum displacement of the corpus callosum for different interventional locations was predicted, which is related to the brain curvature and the distance between the interventional area and corpus callosum (CC). The estimated displacement magnitude of the CC region followed those obtained from clinical observations. The proposed method provided an automatic pipeline for generating realistic computational models for interventional surgery. Results also demonstrated the potential of constructing patient-specific models for image-guided, robotic neurological surgery. © 2019 Tech Science Press. All rights reserved.

关键词： Magnetic resonance imaging

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Special Section Guest Editorial: Technology Platforms for Treatment and Discovery in Human Systems: Novel Work in image-guided procedures, robotic interventions, and modeling

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Journal of medical imaging 2018年第2期5卷 021201页

作者： Simpson, Amber Miga, Michael Memorial Sloan Kettering Cancer Center Department of Surgery New YorkNY United States Vanderbilt University Department of Biomedical Engineering NashvilleTN United States

This guest editorial introduces the Special Section on image-guided procedures, robotic interventions, and modeling in JMI Vol. 5 Issue 2.

关键词： Systems modeling robotics Surgery Data modeling medical imaging Cancer image registration Laparoscopy Tissues Ultrasonography

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Deep learning to detect catheter tips in vivo during photoacoustic-guided catheter interventions 53

Deep learning to detect catheter tips in vivo during photoac...

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53rd Annual conference on Information Sciences and Systems (CISS)

作者： Allman, Derek Assis, Fabrizio Chrispin, Jonathan Bell, Muyinatu A. Lediju Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Med Inst Div Cardiol Baltimore MD 21205 USA

ISBN: (纸本)9781728111513

Catheter guidance is typically performed with fluoroscopy, which requires patient and operator exposure to ionizing radiation. Our group is exploring robotic photoacoustic imaging as an alternative to fluoroscopy to track catheter tips. However, the catheter tip segmentation step in the photoacoustic-based robotic visual servoing algorithm is limited by the presence of confusing photoacoustic artifacts. We previously demonstrated that a deep neural network is capable of detecting photoacoustic sources in the presence of artifacts in simulated, phantom, and in vivo data. This paper directly compares the in vivo results obtained with linear and phased ultrasound receiver arrays. Two convolutional neural networks (CNNs) were trained to detect point sources in simulated photoacoustic channel data and tested with in vivo images from a swine catheterization procedure. The CNN trained with a linear array receiver model correctly classified 88.8% of sources, and the CNN trained with a phased array receiver model correctly classified 91.4% of sources. These results demonstrate that a deep learning approach to photoacoustic image formation is capable of detecting catheter tips during interventional procedures. Therefore, the proposed approach is a promising replacement to the segmentation step in photoacoustic-based robotic visual servoing algorithms.

关键词： Catheters Phased arrays Arrays In vivo Transducers Visual servoing

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Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning 23rd

Simultaneous Estimation of X-Ray Back-Scatter and Forward-Sc...

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23rd International conference on medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Roser, Philipp Zhong, Xia Birkhold, Annette Preuhs, Alexander Syben, Christopher Hoppe, Elisabeth Strobel, Norbert Kowarschik, Markus Fahrig, Rebecca Maier, Andreas Erlangen Germany Diagnostic Imaging Siemens Healthcare GmbH Erlangen Germany Advanced Therapies Siemens Healthcare GmbH Forchheim Germany Erlangen Germany Institute of Medical Engineering University of Applied Sciences Würzburg-Schweinfurt Schweinfurt Germany

ISBN: (纸本)9783030597122

Scattered radiation is a major concern impacting X-ray image-guided procedures in two ways. First, back-scatter significantly contributes to patient (skin) dose during complicated interventions. Second, forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. While conventionally employed anti-scatter grids improve image quality by blocking X-rays, the additional attenuation due to the anti-scatter grid at the detector needs to be compensated for by a higher patient entrance dose. This also increases the room dose affecting the staff caring for the patient. For skin dose quantification, back-scatter is usually accounted for by applying pre-determined scalar back-scatter factors or linear point spread functions to a primary kerma forward projection onto a patient surface point. However, as patients come in different shapes, the generalization of conventional methods is limited. Here, we propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector as well as the back-scatter affecting the patient skin dose. Knowing the forward-scatter, we can correct X-ray projections, while a good estimate of the back-scatter component facilitates an improved skin dose assessment. To simultaneously estimate forward-scatter as well as back-scatter, we propose a multi-task approach for joint back- and forward-scatter estimation by combining X-ray physics with neural networks. We show that, in theory, highly accurate scatter estimation in both cases is possible. In addition, we identify research directions for our multi-task framework and learning-based scatter estimation in general. © 2020, Springer Nature Switzerland AG.

关键词： image enhancement

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Force classification during robotic interventions through simulation-trained neural networks

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2019年第9期14卷 1601-1610页

作者： Mendizabal, Andrea Sznitman, Raphael Cotin, Stephane INRIA Strasbourg France Univ Strasbourg ICube Strasbourg France Univ Bern Bern Switzerland

Purpose Intravitreal injection is among the most frequent treatment strategies for chronic ophthalmic diseases. The last decade has seen a serious increase in the number of intravitreal injections, and with it, adverse effects and drawbacks. To tackle these problems, medical assistive devices for robotized injections have been suggested and are projected to enhance delivery mechanisms for a new generation of pharmacological solutions. In this paper, we present a method aimed at improving the safety characteristics of upcoming robotic systems. Our vision-based method uses a combination of 2D OCT data, numerical simulation and machine learning to classify the range of the force applied by an injection needle on the sclera. Methods We design a neural network to classify force ranges from optical coherence tomography (OCT) images of the sclera directly. To avoid the need for large real data sets, the network is trained on images of simulated deformed sclera. This simulation is based on a finite element method, and the model is parameterized using a Bayesian filter applied to observations of the deformation in OCT images. Results We validate our approach on real OCT data collected on five ex vivo porcine eyes using a robotically guided needle. The thorough parameterization of the simulations leads to a very good agreement between the virtually generated samples used to train the network and the real OCT acquisitions. Results show that the applied force range on real data can be predicted with 93% accuracy. Conclusions Through a simulation-trained neural network, our approach estimates the force range applied by a robotically guided needle on the sclera based solely on a single OCT slice of the deformed sclera. Being real-time, this solution can be integrated in the control loop of the system, permitting the prompt withdrawal of the needle for safety reasons.

关键词： Finite element modeling Bayesian inference Artificial neural networks Force estimation in robotics

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image-guided procedures, robotic interventions, and modeling

Image-guided procedures, robotic interventions, and modeling

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medical imaging 2018: image-guided procedures, robotic interventions, and modeling

作者： Fei, Baowei Webster, Robert J.

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3D Tissue Mimicking Biophantoms for Ultrasound imaging: Bioprinting and image Analysis

3D Tissue Mimicking Biophantoms for Ultrasound Imaging: Biop...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Azizi, Shekoofeh Bayat, Sharareh Rajaram, Ajay Anas, Emran M. A. Mohamed, Tamer Walus, Konrad Abolmaesumi, Purang Mousavi, Parvin Univ British Columbia Vancouver BC Canada Queens Univ Kingston ON Canada Johns Hopkins Univ Baltimore MD USA Aspect Biosyst Vancouver BC Canada

ISBN: (数字)9781510616424

ISBN: (纸本)9781510616424

Tissue-mimicking phantoms can be used to study various diagnostic imaging techniques and image-guided therapeutic interventions. Bioprinting enables the incorporation of live cells into printed phantoms. Some advantages of bioprinted phantoms include their close similarity to in vivo condition and change in phantom composition with time as the cells proliferate and secrete the extra cellular matrix components. In this study, we 3D-print alginate to form three different types of phantoms;those containing human vascular smooth muscle cells, human liver cancer cells, and no cells, each representing benign tissue, cancer tissue, and controls, respectively. The phantoms are imaged with a clinical ultrasound scanner and Temporal Enhanced Ultrasound (TeUS) data is collected. The comparison of the power spectrum of TeUS depicts separation among the three phantom types.

关键词： Tissue printing Ultrasound imaging Biophantom Temporal enhanced ultrasound Cancer detection

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Interactive and Immersive image-guided Control of Interventional Manipulators with a Prototype Holographic Interface 19

Interactive and Immersive Image-guided Control of Interventi...

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19th Annual IEEE International conference on Bioinformatics and Bioengineering (BIBE)

作者： Mojica, Cristina M. Morales Velazco-Garcia, Jose Daniel Zhao, Haoran Seimenis, Ioannis Leiss, Ernst L. Shah, Dipan Webb, Andrew Becker, Aaron T. Tsiamyrtzis, Panagiotis Tsekos, Nikolaos V. Univ Houston Dept Comp Sci MRI Lab Houston TX 77204 USA Univ Houston Dept Elect & Comp Engn Houston TX USA Univ Thrace Sch Med Democritus Alexandroupolis Greece Houston Methodist DeBakey Heart & Vasc Ctr Cardiovasc MRI Lab Houston TX USA Leiden Univ Med Ctr CJ Gorter Ctr High Field MRI Leiden Netherlands Athens Univ Econ & Business Dept Stat Athens Greece

ISBN: (纸本)9781728146171

The emerging potential of augmented reality (AR) to improve 3D medical image visualization for diagnosis, by immersing the user into 3D morphology is further enhanced with the advent of wireless head-mounted displays (HMD). Such information-immersive capabilities may also enhance planning and visualization of interventional procedures. To this end, we introduce a computational platform to generate an augmented reality holographic scene that fuses pre-operative magnetic resonance imaging (MRI) sets, segmented anatomical structures, and an actuated model of an interventional robot for performing MRI-guided and robot-assisted interventions. The interface enables the operator to manipulate the presented images and rendered structures using voice and gestures, as well as to robot control. The software uses forbidden-region virtual fixtures that alerts the operator of collisions with vital structures. The platform was tested with a HoloLens HMD in silico. To address the limited computational power of the HMD, we deployed the platform on a desktop PC with two-way communication to the HMD. Operation studies demonstrated the functionality and underscored the importance of interface customization to fit a particular operator and/or procedure, as well as the need for on-site studies to assess its merit in the clinical realm.

关键词： augmented reality robot-assistance image-guided interventions

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