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检索条件"任意字段=Conference on SPIE Image-Guided Procedures, Robotic Interventions, and Modeling"

共 167 条记录，以下是71-80 订阅

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Which point-line registration?

Which point-line registration?

引用

conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Chen, Elvis C. S. Peters, Terry M. Ma, Burton Western Univ Robarts Res Inst London ON Canada York Univ Dept Elect Engn & Comp Sci N York ON Canada

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Based on the Iterative Closest Point (ICP) framework, we present a generalized solution for the registration between homologous points and lines. The transformation we seek comprises an anisotropic scaling, followed by rotation and translation. This algorithm is demonstrated using the Perspective-n-Point (PnP) problem where lines form a bundle, and the Non-Perspective-n-Point (NPnP) problem where each line potentially has its own origin. We also prove that one existing NPnP solution is, in fact, equivalent to ICP, and that a second PnP solution differs from ICP only in the iteratively estimated translation. Applications for these types of registration include ultrasound calibration, kinematics tracking under fluoroscopic video, and camera pose estimation. Simulation results suggest this ICP algorithm compares favorably to existing PnP and NPnP algorithms, and has an extremely compact formulation.

关键词： Cameras Calibration 3D modeling Coded apertures Computer simulations 3D image processing 3D metrology Detection and tracking algorithms

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Integration of Myocardial Scar Identified by Preoperative Delayed Contrast-Enhanced MRI into a High-Resolution Mapping System for Planning and Guidance of VT Ablation procedures

Integration of Myocardial Scar Identified by Preoperative De...

引用

conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Rettmann, M. E. Suzuki, A. Wang, S. Pottinger, N. Arter, J. Netzer, A. Parker, K. Viker, K. Packer, D. L. Mayo Clin Div Cardiovasc Dis Rochester MN 55905 USA Boston Sci Rhythmia Med Burlington MA USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Myocardial scarring creates a substrate for reentrant circuits which can lead to ventricular tachycardia. In ventricular catheter ablation therapy, regions of myocardial scarring are targeted to interrupt arrhythmic electrical pathways. Low voltage regions are a surrogate for myocardial scar and are identified by generating an electro anatomic map at the start of the procedure. Recent efforts have focussed on integration of preoperative scar information generated from delayed contrast-enhanced MR imaging to augment intraprocedural information. In this work, we describe an initial feasibility study of integration of a preoperative MRI derived scar maps into a high-resolution mapping system to improve planning and guidance of VT ablation procedures.

关键词： Magnetic resonance imaging image segmentation Associative arrays Information fusion Computed tomography image registration 3D image processing 3D modeling 3D scanning Heart

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DeepInfer: Open-Source Deep Learning Deployment Toolkit for image-guided Therapy

DeepInfer: Open-Source Deep Learning Deployment Toolkit for ...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Mehrtash, Alireza Pesteie, Mehran Hetherington, Jorden Behringer, Peter A. Kapur, Tina Wells, William M., III Rohling, Robert Fedorov, Andriy Abolmaesumi, Purang Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Brigham & Womens Hosp Dept Radiol 75 Francis St Boston MA 02115 USA Univ British Columbia Dept Mech Engn Vancouver BC Canada

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

关键词： 3D modeling Data modeling Medical imaging image segmentation Prostate Visual process modeling Visualization Solid modeling Ultrasonography 3D image processing Biomedical engineering Biomedical optics Machine learning Neural networks Software development

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Evaluation of a high-resolution patient-specific model of the electrically stimulated cochlea

Evaluation of a high-resolution patient-specific model of th...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Cakir, Ahmet Dwyer, Robert T. Noble, Jack. H. Vanderbilt Univ Dept Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Hearing & Speech Sci Nashville TN 37232 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Cochlear implants (CIs) are surgically-implanted medical devices used to treat individuals with sensorineural hearing loss who do not receive benefit from appropriately fit amplification. Although these devices have been remarkably successful at restoring audibility, many patients experience poor outcomes. Our group has developed the first image-guided CI programming (IGCIP) technique where the positions of the electrodes are found in CT images and used to estimate neural activation patterns, which is unique information that audiologists can use to define patient-specific processor settings. In our current system, neural activation is estimated using only the distance from each electrode to the neural activation sites. This approach might be less accurate than using a high-resolution electro-anatomical model (EAM) of the electrically stimulated cochlea to perform physics-based estimation of neural activation. In this work, we propose a patient-customized EAM approach where the EAM is spatially and electrically adapted to a patient-specific configuration. Spatial adaptation is done through non-rigid registration of the model with the patient CT image. Electrical adaptation is done by adjusting tissue resistivity parameters so that the intra-cochlear voltage distributions predicted by the model best match those directly measured for the patient via their implant. We demonstrated our approach for N=7 patients. We found that our approach results in mean percent differences between direct and simulated measurements of voltage distributions of 11%. In addition, visual comparison shows the simulated and measured voltage distributions are qualitatively in good agreement. This represents a crucial step toward developing and validating the first in vivo patient-specific cochlea EAMs.

关键词： Cochlear implant modeling image-guided cochlear implant programming

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Face-based Smoothed Finite Element Method for Real-time Simulation of soft tissue

Face-based Smoothed Finite Element Method for Real-time Simu...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Mendizabal, Andrea Duparc, Remi Bessard Bui, Huu Phuoc Paulus, Christoph J. Peterlik, Igor Cotin, Stephane Inria Nancy Grand Est F-54603 Villers Les Nancy France Univ Strasbourg CNRS ICube F-67000 Strasbourg France

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

In soft tissue surgery, a tumor and other anatomical structures are usually located using the preoperative CT or MR images. However, due to the deformation of the concerned tissues, this information suffers from inaccuracy when employed directly during the surgery. In order to account for these deformations in the planning process, the use of a bio-mechanical model of the tissues is needed. Such models are often designed using the finite element method (FEM), which is, however, computationally expensive, in particular when a high accuracy of the simulation is required. In our work, we propose to use a smoothed finite element method (S-FEM) in the context of modeling of the soft tissue deformation. This numerical technique has been introduced recently to overcome the overly stiff behavior of the standard FEM and to improve the solution accuracy and the convergence rate in solid mechanics problems. In this paper, a face-based smoothed finite element method (FS-FEM) using 4-node tetrahedral elements is presented. We show that in some cases, the method allows for reducing the number of degrees of freedom, while preserving the accuracy of the discretization. The method is evaluated on a simulation of a cantilever beam loaded at the free end and on a simulation of a 3D cube under traction and compression forces. Further, it is applied to the simulation of the brain shift and of the kidney's deformation. The results demonstrate that the method outperforms the standard FEM in a bending scenario and that has similar accuracy as the standard FEM in the simulations of the brain-shift and of the kidney's deformation.

关键词： smoothed finite element method strain smoothing real-time simulation soft tissue modeling

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Statistical shape modeling based renal volume measurement using tracked ultrasound

Statistical shape modeling based renal volume measurement us...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Raikar, Vipul Pai Kwartowitz, David M. Clemson Univ Dept Bioengn Clemson SC 29634 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Autosomal dominant polycystic kidney disease (ADPKD) is the fourth most common cause of kidney transplant worldwide accounting for 7-10% of all cases. Although ADPKD usually progresses over many decades, accurate risk prediction is an important task.(1) Identifying patients with progressive disease is vital to providing new treatments being developed and enable them to enter clinical trials for new therapy. Among other factors, total kidney volume (TKV) is a major biomarker predicting the progression of ADPKD. Consortium for Radiologic Imaging Studies in Polycystic Kidney Disease (CRISP)2 have shown that TKV is an early, and accurate measure of cystic burden and likely growth rate. It is strongly associated with loss of renal function.(3) While ultrasound (US) has proven as an excellent tool for diagnosing the disease;monitoring short-term changes using ultrasound has been shown to not be accurate. This is attributed to high operator variability and reproducibility as compared to tomographic modalities such as CT and MR (Gold standard). Ultrasound has emerged as one of the standout modality for intra-procedural imaging and with methods for spatial localization has afforded us the ability to track 2D ultrasound in physical space which it is being used. In addition to this, the vast amount of recorded tomographic data can be used to generate statistical shape models that allow us to extract clinical value from archived image sets. In this work, we aim at improving the prognostic value of US in managing ADPKD by assessing the accuracy of using statistical shape model augmented US data, to predict TKV, with the end goal of monitoring short-term changes.

关键词： Statistical Shape modeling Tracked Ultrasound ADPKD

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Skull registration for prone patient position using tracked ultrasound

Skull registration for prone patient position using tracked ...

引用

conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Underwood, Grace Ungi, Tamas Baum, Zachary Lasso, Andras Kronreif, Gernot Fichtinger, Gabor Queens Univ Lab Percutaneous Surg Kingston ON Canada Austrian Ctr Med Innovat & Technol Wiener Neustadt Austria

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

PURPOSE: Tracked navigation has become prevalent in neurosurgery. Problems with registration of a patient and a preoperative image arise when the patient is in a prone position. Surfaces accessible to optical tracking on the back of the head are unreliable for registration. We investigated the accuracy of surface-based registration using points accessible through tracked ultrasound. Using ultrasound allows access to bone surfaces that are not available through optical tracking. Tracked ultrasound could eliminate the need to work (i) under the table for registration and (ii) adjust the tracker between surgery and registration. In addition, tracked ultrasound could provide a non- invasive method in comparison to an alternative method of registration involving screw implantation. METHODS: A phantom study was performed to test the feasibility of tracked ultrasound for registration. An initial registration was performed to partially align the pre-operative computer tomography data and skull phantom. The initial registration was performed by an anatomical landmark registration. Surface points accessible by tracked ultrasound were collected and used to perform an Iterative Closest Point Algorithm. RESULTS: When the surface registration was compared to a ground truth landmark registration, the average TRE was found to be 1.6 +/- 0.1mm and the average distance of points off the skull surface was 0.6 +/- 0.1mm. CONCLUSION: The use of tracked ultrasound is feasible for registration of patients in prone position and eliminates the need to perform registration under the table. The translational component of error found was minimal. Therefore, the amount of TRE in registration is due to a rotational component of error.

关键词： Ultrasonography Skull image registration Optical tracking 3D modeling Bone image segmentation Computed tomography Transform theory Surgery Head Tomography

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Towards quantitative quasi-static elastography with a gravity-induced deformation source

Towards quantitative quasi-static elastography with a gravit...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Griesenauer, Rebekah H. Weis, Jared A. Arlinghaus, Lori R. Meszoely, Ingrid M. Miga, Michael I. Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Med Ctr Inst Imaging Sci Nashville TN 37235 USA Vanderbilt Univ Dept Surg Med Ctr Nashville TN 37240 USA Vanderbilt Univ Vanderbilt Inst Surg & Engn 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Radiol & Radiol Sci Nashville TN 37232 USA Vanderbilt Univ Med Ctr Dept Neurol Surg Nashville TN 37235 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Biomechanical breast models have been employed for applications in image registration and analysis, breast augmentation simulation, and for surgical and biopsy guidance. Accurate applications of stress-strain relationships of tissue within the breast can improve the accuracy of biomechanical models that attempt to simulate breast movements. Reported stiffness values for adipose, glandular, and cancerous tissue types vary greatly. Variations in reported stiffness properties are mainly due to differences in testing methodologies and assumptions, measurement errors, and natural inter patient differences in tissue elasticity. Therefore, patient specific, in vivo determination of breast tissue properties is ideal for these procedural applications. Many in vivo elastography methods are not quantitative and/or do not measure material properties under deformation conditions that are representative of the procedure being simulated in the model. In this study, we developed an elasticity estimation method that is performed using deformations representative of supine therapeutic procedures. Reconstruction of material properties was performed by iteratively fitting two anatomical images before and after tissue stimulation. The method proposed is work flow friendly, quantitative, and uses a non-contact, gravity-induced deformation source. We tested this material property optimization procedure in a healthy volunteer and in simulation. In simulation, we show that the algorithm can reconstruct properties with errors below 1% for adipose and 5.6% for glandular tissue regardless of the starting stiffness values used as initial guesses. In clinical data, reconstruction errors are higher (3.6% and 24.2%) due to increased noise in the system. In a clinical context, the elastography method was shown to be promising for use in biomechanical model assisted supine procedures.

关键词： elastography MRI lumpectomy image guidance biomechanical modeling registration breast cancer

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Validation of Model-Based Brain Shift Correction in Neurosurgery via Intraoperative Magnetic Resonance Imaging: Preliminary Results

Validation of Model-Based Brain Shift Correction in Neurosur...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Luo, Ma Frisken, Sarah F. Weis, Jared A. Clements, Logan W. Unadkat, Prashin Thompson, Reid C. Golby, Alexandra J. Miga, Michael I. Vanderbilt Univ Dept Biomed Engn Nashville TN 37232 USA Brigham & Womens Hosp Dept Radiol 75 Francis St Boston MA 02115 USA Vanderbilt Univ Med Ctr Dept Neurol Surg Nashville TN 37232 USA Vanderbilt Univ Med Ctr Dept Radiol & Radiol Sci Nashville TN 37232 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

The quality of brain tumor resection surgery is dependent on the spatial agreement between preoperative image and intraoperative anatomy. However, brain shift compromises the aforementioned alignment. Currently, the clinical standard to monitor brain shift is intraoperative magnetic resonance (iMR). While iMR provides better understanding of brain shift, its cost and encumbrance is a consideration for medical centers. Hence, we are developing a model-based method that can be a complementary technology to address brain shift in standard resections, with resource-intensive cases as referrals for iMR facilities. Our strategy constructs a deformation 'atlas' containing potential deformation solutions derived from a biomechanical model that account for variables such as cerebrospinal fluid drainage and mannitol effects. Volumetric deformation is estimated with an inverse approach that determines the optimal combinatory 'atlas' solution fit to best match measured surface deformation. Accordingly, preoperative image is updated based on the computed deformation field. This study is the latest development to validate our methodology with iMR. Briefly, preoperative and intraoperative MR images of 2 patients were acquired. Homologous surface points were selected on preoperative and intraoperative scans as measurement of surface deformation and used to drive the inverse problem. To assess the model accuracy, subsurface shift of targets between preoperative and intraoperative states was measured and compared to model prediction. Considering subsurface shift above 3 mm, the proposed strategy provides an average shift correction of 59% across 2 cases. While further improvements in both the model and ability to validate with iMR are desired, the results reported are encouraging.

关键词： Brain shift finite element modeling image guided surgery

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Computational modeling of Radiofrequency Ablation: Evaluation on Ex Vivo Data Using Ultrasound Monitoring

Computational Modeling of Radiofrequency Ablation: Evaluatio...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Audigier, Chloe Kim, Younsu Dillow, Austin Boctor, Emad M. Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Radiol Baltimore MD 21218 USA Johns Hopkins Univ Dept Mech Engn Baltimore MD 21218 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Radiofrequency ablation (RFA) is the most widely used minimally invasive ablative therapy for liver cancer, but it is challenged by a lack of patient-specific monitoring. Inter-patient tissue variability and the presence of blood vessels make the prediction of the RFA difficult. A monitoring tool which can be personalized for a given patient during the intervention would be helpful to achieve a complete tumor ablation. However, the clinicians do not have access to such a tool, which results in incomplete treatment and a large number of recurrences. Computational models can simulate the phenomena and mechanisms governing this therapy. The temperature evolution as well as the resulted ablation can be modeled. When combined together with intra-operative measurements, computational modeling becomes an accurate and powerful tool to gain quantitative understanding and to enable improvements in the ongoing clinical settings. This paper shows how computational models of RFA can be evaluated using intra-operative measurements. First, simulations are used to demonstrate the feasibility of the method, which is then evaluated on two ex vivo datasets. RFA is simulated on a simplified geometry to generate realistic longitudinal temperature maps and the resulted necrosis. Computed temperatures are compared with the temperature evolution recorded using thermometers, and with temperatures monitored by ultrasound (US) in a 2D plane containing the ablation tip. Two ablations are performed on two cadaveric bovine livers, and we achieve error of 2.2 degrees C on average between the computed and the thermistors temperature and 1.4 degrees C and 2.7 degrees C on average between the temperature computed and monitored by US during the ablation at two different time points (t = 240 s and t = 900 s).

关键词： Radiofrequency ablation Computational modeling Ultrasound thermal monitoring Ex vivo Validation

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