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

作者： Capostagno, S. Sisniega, A. Stayman, J. W. Ehtiati, T. Weiss, C. R. Siewerdsen, J. H. Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Siemens Healthineers Forchheim Germany Johns Hopkins Univ Dept Radiol Baltimore MD USA

ISBN: (纸本)9781510633988

Purpose: Complex, involuntary, non-periodic, deformable motion presents a confounding factor to cone-beam CT (CBCT) image quality due to long (>10 s) scan times. We report and demonstrate an image-based deformable motion compensation method for CBCT, including phantom, cadaver, and animal studies as precursors to clinical studies. Methods: The method corrects deformable motion in CBCT scan data by solving for a motion vector field (MVF) that optimizes a sharpness criterion in the 3D image (viz., gradient entropy). MVFs are estimated by interpolating M locally rigid motion trajectories across N temporal nodes and are incorporated in a modified 3D filtered backprojection approach. The method was evaluated in a cervical spine phantom under flexion, and a cadaver undergoing variable magnitude of complex motion while imaged on a mobile C-arm (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Further assessment was performed on a preclinical animal study using a clinical fixed-room C-arm (Artis Zee, Siemens Healthineers, Forchheim, Germany). Results: In phantom studies, the algorithm resolved visibility of cervical vertebrae under situations of strong flexion, reducing the root-mean-square error by 60% when compared to a motion-free reference. Reduced motion artifacts (blurring, streaks, and loss of soft-tissue edges) were evident in abdominal CBCT of a cadaver imaged during small, medium, and large motion-induced deformation. The animal study demonstrated reduction of streaks from complex motion of bowel gas during the scan. Conclusion: Overall, the studies demonstrate the robustness of the algorithm to a broad range of motion amplitudes, frequencies, data sources (i.e., mobile or fixed-room C-arms) and other confounding factors in real (not simulated) experimental data (e.g., truncation and scatter). These preclinical studies successfully demonstrate reduction of motion artifacts in CBCT and support translation of the method to clinical studies in interventional

关键词： motion compensation cone-beam CT intraoperative imaging soft-tissue imaging

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Simulations and Advancements in MRI-guided Power-Driven Ferric Tools for Wireless Therapeutic interventions

Simulations and Advancements in MRI-Guided Power-Driven Ferr...

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AI in Cybersecurity (ICAIC), International conference on

作者： Wenhui Chu Aobo Jin Hardik A. Gohel Dept. of Computer Science University of Houston-Victoria Victoria USA

Designing a robotic system that functions effectively within the specific environment of a Magnetic Resonance imaging (MRI) scanner requires solving numerous technical issues, such as maintaining the robot’s precision and stability under strong magnetic fields. This research focuses on enhancing MRI’s role in medical imaging, especially in its application to guide intravascular interventions using robot-assisted devices. A newly developed computational system is introduced, designed for seamless integration with the MRI scanner, including a computational unit and user interface. This system processes MR images to delineate the vascular network, establishing virtual paths and boundaries within vessels to prevent procedural damage. Key findings reveal the system’s capability to create tailored magnetic field gradient patterns for device control, considering the vessel’s geometry and safety norms, and adapting to different blood flow characteristics for finer navigation. Additionally, the system’s modeling aspect assesses the safety and feasibility of navigating pre-set vascular paths. Conclusively, this system, based on the Qt framework and C/C++, with specialized software modules, represents a major step forward in merging imaging technology with robotic aid, significantly enhancing precision and safety in intravascular procedures.

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Augmented Reality Applications for image-guided robotic interventions Using Deep Learning Algorithms

Augmented Reality Applications for Image-Guided Robotic Inte...

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International conference on medical imaging and Computer-Aided Diagnosis, MICAD 2022

作者： Seetohul, Jenna Shafiee, Mahmood Sirlantzis, Konstantinos School of Engineering University of Kent Canterbury United Kingdom

ISBN: (纸本)9789811667749

Significant breakthrough in the field of surgery has seen the integration of augmented reality (AR) in standard robot operations, allowing anatomical objects to be digitalized and overlaid onto a real-life scenario in-situ. This paper provides an overview of the methodology used to reconstruct and register laparoscopic head and neck image sequences for an AR tool. Deep learning (DL) algorithms are designed to strategically place fiducial markers or labels in a dataset, hence enabling a virtual tool path to be set up for guiding the end effector of a robot. We introduce a dataset of 271 images of patients from four different clinics in Quebec with a proven history of head-and-neck cancer. We then propose a marker-based registration method for mapping a trajectory during surgery, utilizing an unsupervised neural network for computing the medical image transformations. During the training stage, we use an optimized convolutional neural network (CNN) which warps a set of labels from the moving image in contrast to their counterparts in the fixed image. To this end, we compare the loss functions between warped moving labels and fixed labels with respect to the ground truth. Finally, we propose a UNet architecture where we measure the accuracies in label localization throughout the test sequences relative to the initial output results. Our experiments showed that the UNet outperformed the initial CNN architecture, with optimum performance outcomes in losses being closer to 1.0. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Applications of VR medical image visualization to chordal length measurements for cardiac procedures

Applications of VR medical image visualization to chordal le...

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

作者： Carnahan, Patrick Moore, John Bainbridge, Daniel Wheeler, Gavin Deng, Shujie Pushparajah, Kuberan Chen, Elvis C. S. Simpson, John M. Peters, Terry M. Robarts Res Inst Imaging Labs London ON Canada Western Univ Sch Biomed Engn London ON Canada Western Univ London Hlth Sci Ctr Dept Anesthesiol London ON Canada Kings Coll London Sch Biomed Engn & Imaging Sci London England Evelina London Childrens Hosp Dept Congenital Heart Dis London England Western Univ Dept Med Biophys London ON Canada

ISBN: (纸本)9781510633988

surgeons rely on diagnostic imaging for preoperative planning. Recently, developments have been made on improving 3D ultrasound (US) spatial compounding tailored for cardiac images. Compounded 3D ultrasound volumes are able to capture complex anatomical structures at a level similar to a CT scan, however these images are difficult to display and visualize due to an increased amount of surrounding tissue captured including excess noise at the volume boundaries. Traditional medical image visualization software does not easily allow for viewing 2D slices at arbitrary angles, and 3D rendering techniques do not adequately capture depth information without the use of advanced transfer functions or other depth-encoding techniques that must be tuned to each individual data set. Previous studies have shown that the effective use of virtual reality (VR) can improve image visualization, usability and reduce surgical errors in case planning. We demonstrate the novel use of a VR system for the application of measuring chordal lengths from compounded transesophageal and transgastric echocardiography (TEE, TTE) ultrasound images. Compounded images are constructed from TEE (en-face) views registered and spatially compounded with multiple TEE transgastric views in order to capture both the mitral valve leaflets and chordae tendineae with high levels of detail. Users performed the task of taking linear measurements of chordae visible in these images using both traditional software and a VR platform. Compared to traditional software, the VR platform offered a more intuitive experience with respect to orientation, however users felt there was a lack of precision when performing the measurement tasks.

关键词： Virtual Reality 3D Echocardiography Cardiac Procedure Planning Chordae Tendinae Measurement

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Machine learning to detect brain lesions in focal epilepsy

Machine learning to detect brain lesions in focal epilepsy

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

作者： Perera-Ortega, Andrea Sedghi, Alireza Isen, Jonah Vos, Sjoerd B. Mousavi, Parvin Winston, Gavin P. Queens Univ Sch Comp Med Informat Lab Kingston ON Canada UCL Ctr Med Image Comp London England UCL Neuroradiol Acad Unit Inst Neurol Queen Sq London England Queens Univ Dept Med Kingston ON Canada Queens Univ Ctr Neurosci Studies Kingston ON Canada UCL Dept Clin & Expt Epilepsy Inst Neurol Queen Sq London England

ISBN: (纸本)9781510640269

PURPOSE: Identifying areas of abnormality on MRI brain scans in individuals with focal epilepsy is fundamental to the diagnosis and treatment of the condition. However, in about a third of patients with focal epilepsy, brain scans appear to be normal (MRI-negative) as human observers cannot detect any abnormality with current imaging technology. The objective of this paper is to provide a novel approach in presenting localization using machine learning in order to locate areas of abnormality on patients with focal epilepsy on a per-voxel basis by comparing them with healthy controls. As a proof-of-concept, the technique is first applied to patients with visible lesions providing a ground truth (MRI-positive), but future work will extend this to MRI-negative subjects. METHODS: Our data consists of multi-modal brain MR images from 62 healthy control subjects and 44 MRI-positive patients with focal epilepsy. We utilized a support vector machine (SVM) as our probabilistic classifier and train it with two classes of data. We generate probability maps applying our machine learning classifier on all voxels of a test subjects to visualize the predictions. Overlap scores are used to evaluate the classifier performance in MRI-positive patients. RESULTS: Our model reached 83% specificity, 91% sensitivity, and an Area Under the Curve (AUC) of 0.896 for the task of voxel-based classification of normal versus abnormal voxels. In addition, Dice scores of up to 0.66 were achieved for the overlap measure of lesion probability map and the ground truth labels annotated by a neurologist. CONCLUSION: We demonstrated a novel approach in presenting localization using machine learning techniques to localize focal epilepsy lesions from multi-modal MR images.

关键词： Machine learning focal epilepsy support vector machine magnetic resonance imaging

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Intraoperative Guidance of Orthopaedic Instruments Using 3D Correspondence of 2D Object Instance Segmentations

Intraoperative Guidance of Orthopaedic Instruments Using 3D ...

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

作者： Bataeva, I Shah, K. Vijayan, R. Han, R. Sheth, N. M. Kleinszig, G. Vogt, S. Osgood, G. M. Siewerdsen, J. H. Uneri, A. Johns Hopkins Univ Dept Mech Engn Baltimore MD 21218 USA Johns Hopkins Univ Lab Computat Sensing & Robot Baltimore MD 21218 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Siemens Healthineers Erlangen Germany Johns Hopkins Med Dept Orthopaed Surg Baltimore MD USA

ISBN: (纸本)9781510640269

Purpose. Surgical placement of pelvic instrumentation is challenged by complex anatomy and narrow bone corridors, and despite heavy reliance on intraoperative fluoroscopy, trauma surgery lacks a reliable solution for 3D surgical navigation that is compatible with steep workflow requirements. We report a method that uses routinely acquired fluoroscopic images in standard workflow to automatically detect and localize orthopaedic instruments for 3D guidance. Methods. The proposed method detects, establishes correspondence of, and localizes orthopaedic devices from a pair of radiographs. Instrument detection uses Mask R-CNN for segmentation and keypoint detection, trained on 4000 cadaveric pelvic radiographs with simulated guidewires. Keypoints on individual images are corresponded using prior calibration of the imaging system to backproject, identify, and rank-order ray intersections. Estimation of 3D instrument tip and direction was evaluated on a cadaveric specimen and patient images from an IRB-approved clinical study. Results. The detection network successfully generalized to cadaver and clinical images, achieving 87% recall and 98% precision. Mean geometric accuracy in estimating instrument tip and direction was (1.9 +/- 1.6) mm and (1.8 +/- 1.3)degrees, respectively. Simulation studies demonstrated 1.1 mm median error in 3D tip and 2.3 degrees in 3D direction estimation. Preliminary tests in cadaver and clinical images show the basic feasibility of the overall approach. Conclusions. Experimental studies demonstrate the feasibility and highlight the potential of deep learning for 3D-2D registration of orthopaedic instruments as applied in fixation of pelvic fractures. The approach is compatible with routine orthopaedic workflow, does not require additional equipment (such as surgical trackers), uses common imaging equipment (mobile C-arm fluoroscopy), and does not require vendor-specific device models.

关键词： Intraoperative imaging image-guided surgery deep learning 3D-2D image registration object detection instance segmentation

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robotic ultrasound imaging: State-of-the-art and future perspectives

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medical image ANALYSIS 2023年第1期89卷 102878页

作者： Jiang, Zhongliang Salcudean, Septimiu E. Navab, Nassir Tech Univ Munich Comp Aided Med Procedures Munich Germany Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Johns Hopkins Univ Comp Aided Med Procedures Baltimore MD USA Tech Univ Munich Fak Informat I16 Boltzmannstr 3 D-85748 Garching Germany

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques. Additionally, we present the challenges that the scientific community needs to face in the coming years in order to achieve its ultimate goal of developing intelligent robotic sonographer colleagues. These colleagues are expected to be capable of collaborating with human sonographers in dynamic environments to enhance both diagnostic an

关键词： Ultrasound imaging robotic ultrasound Telesonography medical robotics Orientation optimization Path planning Visual servoing Compliant control robotic US Robot learning Reinforcement learning Learning from demonstrations Ultrasound standard plane Ultrasound imgaing quality Teleoperated robotic ultrasound

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SpineCloud: image Analytics for Predictive modeling of Spine Surgery Outcomes

SpineCloud: Image Analytics for Predictive Modeling of Spine...

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

作者： De Silva, T. Vedula, S. S. Perdomo-Pantoja, A. Vijayan, R. Doerr, S. A. Uneri, A. Han, R. Ketcha, M. D. Skolasky, R. L. Caffo, B. Hager, G. Witham, T. Theodore, N. Siewerdsen, J. H. Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21205 USA Johns Hopkins Univ Malone Ctr Engn Healthcare Baltimore MD 21205 USA Johns Hopkins Univ Dept Neurosurg Baltimore MD 21205 USA Johns Hopkins Univ Dept Orthopaed Surg Baltimore MD USA Johns Hopkins Univ Dept Biostat Baltimore MD 21205 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781510633988

Spinal degeneration and deformity present an enormous healthcare burden, with spine surgery among the main treatment modalities. Unfortunately, spine surgery (e.g., lumbar fusion) exhibits broad variability in the quality of outcome, with similar to 20-40% of patients gaining no benefit in pain or function ("failed back surgery") and earning criticism that is difficult to reconcile versus rapid growth in frequency and cost over the last decade. Vital to advancing the quality of care in spine surgery are improved clinical decision support (CDS) tools that are accurate, explainable, and actionable: accurate in prediction of outcomes;explainable in terms of the physical / physiological factors underlying the prediction;and actionable within the shared decision process between a surgeon and patient in identifying steps that could improve outcome. This technical note presents an overview of a novel outcome prediction framework for spine surgery (dubbed SpineCloud) that leverages innovative image analytics in combination with explainable prediction models to achieve accurate outcome prediction. Key to the SpineCloud framework are image analysis methods for extraction of high-level quantitative features from multi-modality peri-operative images (CT, MR, and radiography) related to spinal morphology (including bone and soft-tissue features), the surgical construct (including deviation from an ideal reference), and longitudinal change in such features. The inclusion of such image-based features is hypothesized to boost the predictive power of models that conventionally rely on demographic / clinical data alone (e.g., age, gender, BMI, etc.). Preliminary results using gradient boosted decision trees demonstrate that such prediction models are explainable (i.e., why a particular prediction is made), actionable (identifying features that may be addressed by the surgeon and/or patient), and boost predictive accuracy compared to analysis based on demographics alone (e.g., AUC imp

关键词： spine surgery image analysis surgical outcomes predictive modeling clinical decision support

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modeling the Effects of Bipolar Helical Cuff Electrodes in Vagus Nerve Stimulation

Modeling the Effects of Bipolar Helical Cuff Electrodes in V...

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

作者： Ding, Alice K. Luo, Ma Miga, Michael, I Vanderbilt Inst Surg & Engn Nashville TN 37212 USA SyBBURE Searle Undergrad Res Program Nashville TN 37212 USA Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Dept Radiol & Radiol Sci Med Ctr Nashville TN 37235 USA Vanderbilt Univ Dept Neurol Surg Med Ctr Nashville TN 37235 USA Vanderbilt Univ Dept Otolaryngol Head & Neck Surg Med Ctr Nashville TN 37235 USA

ISBN: (纸本)9781510640269

While mainly used for reducing seizures in epileptic patients, vagus nerve stimulation (VNS) has been implied to be capable of treating various other diseases. However, the therapeutic extent and control of neuromodulation for these conditions are still uncertain and are limited by the ability to predict neural activation responses upon targeting certain fascicles. Generally, VNS is administered through a bipolar helical cuff electrode implanted around the left vagus nerve. The electrode delivers pulses of electricity to the nerve to recruit axons. This work focuses on predicting percent activation and regions of activation based on different adjustable factors such as injected current amplitude and pulse width of stimuli and the activated region of the electrode. To achieve, a simplified finite element model was created using cylindrical geometries as nerve components with an addition of helical cuff electrodes. All of these components were encased in surrounding tissue with assumed properties similar to adipose. Electric potential distribution in the model was processed with an activating function defined along the axonal length which estimates the injected current at the nodes of Ranvier (NoR). These values were then enforced in the neuron simulation as a current clamp approximation applied at the NoR and the likelihood of an action potential was determined. Presence of action potentials were then detected to determine which axons were recruited in VNS. This preliminary work determined that electrode configurations can target specific fascicles in the nerve while amplitudes and pulse widths of stimuli contribute to the percentage of the nerve activated. This demonstrates the ability for patient-specific control over targeting fascicles. Additionally, this work presents initial steps to improving the model by using histological data to create a geometric- specific approach.

关键词： Vagus nerve stimulation finite element modeling bipolar helical cuff neural computational modeling epilepsy

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Enhanced Guidance for Endovascular robotics

Enhanced Guidance for Endovascular Robotics

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Dennis Kundrat Alfred Schell Jelle Bijlsma Giulio Dagnino Robotics and Mechatronics University of Twente Enschede The Netherlands Fraunhofer IMTE Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering Lübeck Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Increasing interest surrounds the use of robotic and computer technologies for precise endovascular interventions. However, a limitation in current robotic procedures is the reliance on 2D fluoroscopy for surgical navigation and lack of haptic guidance. In addressing this, we present an improved guidance framework for CathBot, our MR-compatible endovascular robot. This includes visual guidance through fluoroscopy and MRI, along with vision-based haptic guidance. The framework enables real-time acquisition and processing of fluoroscopy and MRI images, integrated into the robotic platform’s control architecture for enhanced guidance. Assessment experiments show the potential of this approach, also marking a crucial step towards MR-guided robot-assisted interventions, aiming to reduce ionizing radiation exposure, enabling functional imaging and enhance the overall intervention workflow.

关键词： Visualization Three-dimensional displays Magnetic resonance imaging Pose estimation Process control Software Real-time systems Haptic interfaces Robots Remote control

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