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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51-60 订阅

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Fusion of Intraoperative Force Sensoring, Surface Reconstruction and Biomechanical modeling

Fusion of Intraoperative Force Sensoring, Surface Reconstruc...

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

作者： Roehla, S. Bodenstedt, S. Kuederle, C. Suwelack, S. Kenngott, H. Mueller-Stich, B. P. Dillmann, R. Speidel, S. Karlsruhe Inst Technol Dept Comp Sci Inst Anthropomat Karlsruhe Germany Univ Heidelberg Hosp Dept Gen Visceral & Transplantat Surg Heidelberg Germany

ISBN: (纸本)9780819489654

Minimally invasive surgery is medically complex and can heavily benefit from computer assistance. One way to help the surgeon is to integrate preoperative planning data into the surgical workflow. This information can be represented as a customized preoperative model of the surgical site. To use it intraoperatively, it has to be updated during the intervention due to the constantly changing environment. Hence, intraoperative sensor data has to be acquired and registered with the preoperative model. Haptic information which could complement the visual sensor data is still not established. In addition, biomechanical modeling of the surgical site can help in reflecting the changes which cannot be captured by intraoperative sensors. We present a setting where a force sensor is integrated into a laparoscopic instrument. In a test scenario using a silicone liver phantom, we register the measured forces with a reconstructed surface model from stereo endoscopic images and a finite element model. The endoscope, the instrument and the liver phantom are tracked with a Polaris optical tracking system. By fusing this information, we can transfer the deformation onto the finite element model. The purpose of this setting is to demonstrate the principles needed and the methods developed for intraoperative sensor data fusion. One emphasis lies on the calibration of the force sensor with the instrument and first experiments with soft tissue. We also present our solution and first results concerning the integration of the force sensor as well as accuracy to the fusion of force measurements, surface reconstruction and biomechanical modeling.

关键词： image-guided Therapy Force sensoring Sensor data fusion Registration Finite Element modelling

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Lung tumor motion prediction during lung brachytherapy using Finite Element Model

Lung tumor motion prediction during lung brachytherapy using...

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

作者： Shirzadi, Zahra Naini, Ali Sadeghi Samani, Abbas Univ Western Ontario Grad Program Biomed Engn London ON Canada Univ Western Ontario Dept Elect & Comp Engn London ON Canada Robarts Res Inst RRI Imaging Res Labs London ON Canada Univ Western Ontario Dept Med Biophys London ON Canada

ISBN: (纸本)9780819489654

A biomechanical model is proposed to predict deflated lung tumor motion caused by diaphragm respiratory motion. This model can be very useful for targeting the tumor in tumor ablative procedures such as lung brachytherapy. To minimize motion within the target lung, these procedures are performed while the lung is deflated. However, significant amount of tissue deformation still occurs during respiration due to the diaphragm contact forces. In the absence of effective real-time image guidance, biomechanical models can be used to estimate tumor motion as a function of diaphragm's position. To develop this model, Finite Element Method (FEM) was employed. To demonstrate the concept, we conducted an animal study of an ex-vivo porcine deflated lung with a tumor phantom. The lung was deformed by compressing a diaphragm mimicking cylinder against it. Before compression, 3D-CT image of this lung was acquired, which was segmented and turned into FE mesh. The lung tissue was modeled as hyperelastic material with a contact loading to calculate the lung deformation and tumor motion during respiration. To validate the results from FE model, the motion of a small area on the surface close to the tumor was tracked while the lung was being loaded by the cylinder. Good agreement was demonstrated between the experiment results and simulation results. Furthermore, the impact of tissue hyperelastic parameters uncertainties in the FE model was investigated. For this purpose, we performed in-silico simulations with different hyperelastic parameters. This study demonstrated that the FEM was accurate and robust for tumor motion prediction.

关键词： Biomechanical modeling Finite Element Method Deflated lung Brachytherapy Hyperelastic parameters Diaphragm contact effect

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Patient-specific liver deformation modeling for tumor tracking

Patient-specific liver deformation modeling for tumor tracki...

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

作者： Oh, Young-Taek Hwang, Youngkyoo Kim, Jung-Bae Bang, Won-Chul Kim, James D. K. Kim, ChangYeong Samsung Adv Inst Technol Yongin 446712 Gyeonggi Do South Korea

ISBN: (纸本)9780819494450

We present a new method for patient-specific liver deformation modeling for tumor tracking. Our method focuses on deforming two main blood vessels of the liver - hepatic and portal vein - to utilize them as features. A novel centerline editing algorithm based on ellipse fitting is introduced for vessel deformation. Centerline-based blood vessel model and various interpolation methods are often used for generating a deformed model at the specific time t. However, it may introduce artifacts when models used in interpolation are not consistent. One of main reason of this inconsistency is the location of bifurcation points differs from each image. To solve this problem, our method generates a base model from one of patient's CT images. Next, we apply a rigid iterative closest point (ICP) method to the base model with centerlines of other images. Because the transformation is rigid, the length of each vessel's centerline is preserved while some part of the centerline is slightly deviated from centerlines of other images. We resolve this mismatch using our centerline editing algorithm. Finally, we interpolate three deformed models of liver, blood vessels, tumor using quadratic Bezier curves. We demonstrate the effectiveness of the proposed approach with the real patient data.

关键词： Tumor tracking Centerline editing Deformation Spline curve Interpolation

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A Biomechanical approach for in vivo Lung Tumor Motion Prediction during External Beam Radiation Therapy

A Biomechanical approach for <i>in vivo</i> Lung Tumor Motio...

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

作者： Karami, Elham Gaede, Stewart Lee, Ting-Yim Samani, Abbas Univ Western Ontario Dept Med Biophys London ON Canada Robarts Res Inst Imaging Res Labs London ON N6A 5C1 Canada London Reg Canc Program Dept Phys & Engn London ON Canada Univ Western Ontario Dept Oncol London ON Canada Lawson Hlth Res Inst Imaging Program London ON Canada Univ Western Ontario Dept Med Imaging London ON Canada Univ Western Ontario Dept Elect & Comp Engn London ON Canada Univ Western Ontario Grad Program Biomed Engn London ON Canada

ISBN: (纸本)9781628415056

Lung Cancer is the leading cause of cancer death in both men and women. Among various treatment methods currently being used in the clinic, External Beam Radiation Therapy (EBRT) is used widely not only as the primary treatment method, but also in combination with chemotherapy and surgery. However, this method may lack desirable dosimetric accuracy because of respiration induced tumor motion. Recently, biomechanical modeling of the respiratory system has become a popular approach for tumor motion prediction and compensation. This approach requires reasonably accurate data pertaining to thoracic pressure variation, diaphragm position and biomechanical properties of the lung tissue in order to predict the lung tissue deformation and tumor motion. In this paper, we present preliminary results of an in vivo study obtained from a Finite Element Model (FEM) of the lung developed to predict tumor motion during respiration.

关键词： Lung Tumors Motion models Tissues 3D modeling In vivo imaging Data modeling Lung cancer Radiotherapy Finite element methods

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Procedural wound geometry and blood flow generation for medical training simulators

Procedural wound geometry and blood flow generation for medi...

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

作者： Aras, Rifat Shen, Yuzhong Li, Jiang Old Dominion Univ Dept Modeling Simulat & Visualizat Eng Norfolk VA 23529 USA Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA

ISBN: (纸本)9780819489654

Efficient application of wound treatment procedures is vital in both emergency room and battle zone scenes. In order to train first responders for such situations, physical casualty simulation kits, which are composed of tens of individual items, are commonly used. Similar to any other training scenarios, computer simulations can be effective means for wound treatment training purposes. For immersive and high fidelity virtual reality applications, realistic 3D models are key components. However, creation of such models is a labor intensive process. In this paper, we propose a procedural wound geometry generation technique that parameterizes key simulation inputs to establish the variability of the training scenarios without the need of labor intensive remodeling of the 3D geometry. The procedural techniques described in this work are entirely handled by the graphics processing unit (GPU) to enable interactive real-time operation of the simulation and to relieve the CPU for other computational tasks. The visible human dataset is processed and used as a volumetric texture for the internal visualization of the wound geometry. To further enhance the fidelity of the simulation, we also employ a surface flow model for blood visualization. This model is realized as a dynamic texture that is composed of a height field and a normal map and animated at each simulation step on the GPU. The procedural wound geometry and the blood flow model are applied to a thigh model and the efficiency of the technique is demonstrated in a virtual surgery scene.

关键词： procedural techniques wound geometry surface blood flow medical training simulators

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Two new ad-hoc models of detection physics and their evaluation for navigated beta probe surface imaging

Two new ad-hoc models of detection physics and their evaluat...

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

作者： Shakir, Dzhoshkun I. Hartl, Alexander Schneider, Florian R. Pulko, Jozef Ziegler, Sibylle I. Navab, Nassir Lasser, Tobias Tech Univ Munich CAMP D-8000 Munich Germany Tech Univ Munich Nucl Med Dept Klin Rech Isar Munich Germany Tech Univ Munich Dept Phys Munich Germany

ISBN: (纸本)9780819489654

Intra-operative surface imaging with navigated beta probes in conjunction with positron-emitting radio-tracers like F-18-FDG has been shown to enable control of tumor resection borders. We showed previously that employing iterative reconstruction (MLEM) in conjunction with an ad-hoc model of the detection physics (based on solid-angle geometry, SA) improves the image quality. In this study, we sampled the beta probe readings of a point source using a precision step-motor to generate a look-up-table (LUT) model. We also generated a simplified geometrical model (SG) based on this data set. To see how these two models influence the image quality compared to the old SA model, we reconstructed images from sparsely sampled datasets of a phantom with three hotspots using each model. The images yielded 76% (SA), 81% (SG), and 81% (LUT) mean NCC compared to the ground truth. The SG and LUT models, however, could resolve the hotspots better in the datasets where the detector-to-phantom distance was larger. Additionally, we compared the deviations of the SA and SG analytical models to the measured LUT model, where we found that the SG model gives estimates substantially closer to the actual beta probe readings than the previous SA model.

关键词： Data modeling Seaborgium Physics image quality Statistical modeling Sensors Tumors Signal attenuation Instrument modeling Visualization

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Geometric modeling of the Aortic Inner and Outer Vessel Wall from CTA for Aortic Dissection Analysis

Geometric Modeling of the Aortic Inner and Outer Vessel Wall...

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

作者： Eigen, Katharina Weis, Michael Dohle, Daniel-Sebastian Suehling, Michael Maier, Andreas Friedrich Alexander Univ Erlangen Nuremberg Erlangen Germany Siemens Healthcare GmbH Forchheim Germany Univ Med Ctr Mainz Germany

ISBN: (数字)9781510616424

ISBN: (纸本)9781510616424

In this paper, we present a novel method for modeling both layers of the aortic walls in cases of aortic dissections for analysis from Computed Tomography Angiography. It involves a fast initialization of the associated physiological and pathological lumina and further editing on non-linearly formatted and cross-sectional views. Fast and accurate derivation of 3D models of these inner and outer vessel walls is crucial to analyze true and false lumen, to accelerate processing times in research studies, and to answer therapy questions. Since the aorta is a relatively large vessel, our system makes use of a point-based surface interpolation with compactly supported radial basis functions requiring only few surface constraints. Where possible, we use a semi-automatic approach to segment the vessel walls using an Active Contour Model, which detects the contours in the vessel's cross-sectional planes, stating the constraints for interpolation. After initialization, editing on non-linearly formatted and cross-sectional views is possible due to handling user input through tangent frame bundles to dismiss contradictory surface samples before updating the models with the new constraints. Our proposed method was evaluated in a user study to measure processing times and achievable model accuracy with respect to an expert-defined ground truth. The users needed 19 minutes on average to derive one model (both walls) and attained a mean surface distance of about 1.0 mm for the outer vessel wall, respectively, 1.6 mm for the inner wall. Using our method instead of open source program for geometric modeling saves 26 minutes per dataset.

关键词： Aortic Dissection Active Contour Model Tangent Frame Bundles

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Automatic Pose Correction for image-guided Nonhuman Primate Brain Surgery Planning

Automatic Pose Correction for Image-Guided Nonhuman Primate ...

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conference on spie image-guided procedures, robotic interventions, and modeling

作者： Ghafurian, Soheil Chen, Antong Hines, Catherine Dogdas, Belma Bone, Ashleigh Lodge, Kenneth OMalley, Stacey Winkelmann, Christopher T. Bagchi, Ansuman Lubbers, Laura S. Uslaner, Jason M. Johnson, Colena Renger, John Zariwala, Hatim A. Merck Res Labs Informat IT Appl Math & Modeling West Point PA 19486 USA Rutgers State Univ Dept Ind & Syst Engn New Brunswick NJ 08901 USA Merck Res Labs Translat Imaging Biomarkers West Point PA 19486 USA Merck Res Labs Appl Math & Modeling Informat IT Rahway NJ 07065 USA Merck Res Labs Safety Assessment & Lab Anim Resources West Point PA 19486 USA Pfizer Comparat Med La Jolla CA 92121 USA Merck Res Labs Pharmacol West Point PA 19486 USA

ISBN: (纸本)9781510600218

Intracranial delivery of recombinant DNA and neurochemical analysis in nonhuman primate (NHP) requires precise targeting of various brain structures via imaging derived coordinates in stereotactic surgeries. To attain targeting precision, the surgical planning needs to be done on preoperative three dimensional (3D) CT and/or MR images, in which the animals head is fixed in a pose identical to the pose during the stereotactic surgery. The matching of the image to the pose in the stereotactic frame can be done manually by detecting key anatomical landmarks on the 3D MR and CT images such as ear canal and ear bar zero position. This is not only time intensive but also prone to error due to the varying initial poses in the images which affects both the landmark detection and rotation estimation. We have introduced a fast, reproducible, and semi-automatic method to detect the stereotactic coordinate system in the image and correct the pose. The method begins with a rigid registration of the subject images to an atlas and proceeds to detect the anatomical landmarks through a sequence of optimization, deformable and multimodal registration algorithms. The results showed similar precision (maximum difference of 1.71 in average in-plane rotation) to a manual pose correction.

关键词： stereotactic surgery MRI atlas rigid nonrigid registration deep brain targeting mid-sagittal plane

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Target Registration Error for Rigid Shape-based Registration with Heteroscedastic Noise

Target Registration Error for Rigid Shape-based Registration...

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

作者： Ma, Burton Choi, Joy Huai, Hong Ming York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 2R7 Canada

ISBN: (纸本)9780819498298

We propose an analytic equation for approximating expected root mean square (RMS) target registration error (TRE) for rigid shape-based registration where measured noisy data points are matched to a rigid shape. The noise distribution of the data points is assumed to be zero-mean, independent, and non-identical;i.e., the noise covariance may be different for each data point. The equation was derived by extending a previously published spatial stiffness model of registration. The equation was validated by performing registration experiments with both synthetic registration data and data collected using an optically tracked pointing stylus. The synthetic registration data were generated from the surface of an ellipsoid. The optically tracked data were collected from three plastic replicas of human radii and registered to isosurface models of the radii computed from CT scans. Noise covariances for the data points were computed by considering the pose of the tracked stylus, the positions of the individual fiducial markers on the stylus coordinate reference frame, and the calibrated position of the stylus tip;these quantities and an estimate of the fiducial localization covariance of the tracking system were used as inputs to a previously published algorithm for estimating the covariance of TRE for point-based (fiducial) registration. Registration simulations were performed using a modified version of the iterated closest point algorithm and the resulting RMS TREs were compared to the values predicted by our analytic equation.

关键词： image registration Mahalanobis distance Detection and tracking algorithms Error analysis Optical tracking Computed tomography Computer simulations Data modeling Calibration Computing systems

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Determining In-Silico Left Ventricular Contraction Force of Myocardial Infarct Tissue Using a Composite Material Model

Determining In-Silico Left Ventricular Contraction Force of ...

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

作者： Dempsey, Sergio C. H. Samani, Abbas Western Univ Med Phys Program London ON Canada Western Univ Dept Elect & Comp Engn London ON Canada Western Univ Dept Med Biophys London ON Canada Western Univ Robarts Res Inst Imaging Res London ON Canada

ISBN: (数字)9781510616424

ISBN: (纸本)9781510616424

A computational method is presented in this paper for determining the severity of myocardial infarction of the left ventricle (LV) using its image data. In-silico generated displacement fields for a healthy and damaged LV are used to mimic imaging modalities by adding appropriate levels of noise. To reconstruct the contraction force from the displacement field, a composite material model of the LV is optimized using genetic algorithms and a neural network to return the contraction force and distribution of forces for infarct tissue. The healthy LV contraction force was accurately returned within 1% for all displacement field tests indicating that all imaging methods could be used to measure healthy patient LV displacement fields for the purpose of contraction force reconstruction. With the damaged LV, contraction forces of the healthy region, as well as infarct border and infarct regions were considered. The optimization model found the contraction force distribution within 2% for the healthy region, while for the border zone and infarct regions the average contraction force reconstruction errors were 8.4 kPa and 5.1 kPa, respectively. These errors are reasonably small while no significant SNR dependence was observed. The inverse problem algorithm provided good estimates regardless of the SNR, however, further training of the neural network system is required to improve the robustness of the inversion framework with low contraction forces, since the accuracy of the optimization limited the SNR response.

关键词： Left Ventricle Myocardial Infarction Contraction Force Reconstruction Composite Material modeling Finite Element Modellin Optimization

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