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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31-40 订阅

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Generating patient-specific pulmonary vascular models for surgical planning

Generating patient-specific pulmonary vascular models for su...

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

作者： Murff, Daniel Co-Vu, Jennifer O'Dell, Walter G. Univ Florida Dept Biomed Engn Gainesville FL 32611 USA UF Congenital Heart Ctr Gainesville FL 32610 USA Univ Florida Shands Canc Ctr Gainesville FL 32601 USA

ISBN: (纸本)9781628415056

Each year in the U.S., 7.4 million surgical procedures involving the major vessels are performed. Many of our patients require multiple surgeries, and many of the procedures include "surgical exploration". procedures of this kind come with a significant amount of risk, carrying up to a 17.4% predicted mortality rate. This is especially concerning for our target population of pediatric patients with congenital abnormalities of the heart and major pulmonary vessels. This paper offers a novel approach to surgical planning which includes studying virtual and physical models of pulmonary vasculature of an individual patient before operation obtained from conventional 3D X-ray computed tomography (CT) scans of the chest. These models would provide clinicians with a non-invasive, intricately detailed representation of patient anatomy, and could reduce the need for invasive planning procedures such as exploratory surgery. Researchers involved in the AirPROM project have already demonstrated the utility of virtual and physical models in treatment planning of the airways of the chest. Clinicians have acknowledged the potential benefit from such a technology. A method for creating patient-derived physical models is demonstrated on pulmonary vasculature extracted from a CT scan with contrast of an adult human. Using a modified version of the NIH imageJ program, a series of image-processing functions are used to extract and mathematically reconstruct the vasculature tree structures of interest. An auto-generated STL file is sent to a 3D printer to create a physical model of the major pulmonary vasculature generated from 3D CT scans of patients.

关键词： 3D modeling Computed tomography 3D printing Surgery 3D image processing Heart Chest Mathematical modeling Tissues X-ray computed tomography

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A patient specific 4D MRI liver motion model based on sparse imaging and registration

A patient specific 4D MRI liver motion model based on sparse...

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

作者： Noorda, Yolanda H. Bartels, Lambertus W. van Stralen, Marijn Pluim, Josien P. W. Univ Med Ctr Utrecht Image Sci Inst Utrecht Netherlands

ISBN: (纸本)9780819494450

Introduction image-guided minimally invasive procedures are becoming increasingly popular. Currently, High-Intensity Focused Ultrasound (HIFU) treatment of lesions in mobile organs, such as the liver, is in development. A requirement for such treatment is automatic motion tracking, such that the position of the lesion can be followed in real time. We propose a 4D liver motion model, which can be used during planning of this procedure. During treatment, the model can serve as a motion predictor. In a similar fashion, this model could be used for radiotherapy treatment of the liver. Method The model is built by acquiring 2D dynamic sagittal MRI data at six locations in the liver. By registering these dynamics to a 3D MRI liver image, 2D deformation fields are obtained at every location. The 2D fields are ordered according to the position of the liver at that specific time point, such that liver motion during an average breathing period can be simulated. This way, a sparse deformation field is created over time. This deformation field is finally interpolated over the entire volume, yielding a 4D motion model. Results The accuracy of the model is evaluated by comparing unseen slices to the slice predicted by the model at that specific location and phase in the breathing cycle. The mean Dice coefficient of the liver regions was 0.90. The mean misalignment of the vessels was 1.9 mm. Conclusion The model is able to predict patient specific deformations of the liver and can predict regular motion accurately.

关键词： motion modeling registration MRI

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Collision detection and modeling of rigid and deformable objects in laparoscopic simulator

Collision detection and modeling of rigid and deformable obj...

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

作者： Dy, Mary-Clare Tagawa, Kazuyoshi Tanaka, Hiromi T. Komori, Masaru Ritsumeikan Univ Kusatsu Shiga Japan Shiga Univ Med Sci Otsu Shiga 52021 Japan

ISBN: (纸本)9781628415056

Laparoscopic simulators are viable alternatives for surgical training and rehearsal. Haptic devices can also be incorporated with virtual reality simulators to provide additional cues to the users. However, to provide realistic feedback, the haptic device must be updated by 1 kHz. On the other hand, realistic visual cues, that is, the collision detection and deformation between interacting objects must be rendered at least 30 fps. Our current laparoscopic simulator detects the collision between a point on the tool tip, and on the organ surfaces, in which haptic devices are attached on actual tool tips for realistic tool manipulation. The triangular-mesh organ model is rendered using a mass spring deformation model, or finite element method-based models. In this paper, we investigated multi-point-based collision detection on the rigid tool rods. Based on the preliminary results, we propose a method to improve the collision detection scheme, and speed up the organ deformation reaction. We discuss our proposal for an efficient method to compute simultaneous multiple collision between rigid (laparoscopic tools) and deformable (organs) objects, and perform the subsequent collision response, with haptic feedback, in real-time.

关键词： Virtual reality Surgical simulation Minimally invasive surgery modeling Collision detection Deformable objects Haptic devices

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Optimal atlas construction through hierarchical image registration

Optimal atlas construction through hierarchical image regist...

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

作者： Grevera, George J. Udupa, Jayaram K. Odhner, Dewey Torigian, Drew A. St Josephs Univ Dept Comp Sci 5600 City Ave Philadelphia PA 19131 USA Hosp Univ Penn Dept Radiol Med Image Proc Grp MIPG 3400 Spruce St Philadelphia PA 19104 USA

ISBN: (纸本)9781510600218

Atlases (digital or otherwise) are common in medicine. However, there is no standard framework for creating them from medical images. One traditional approach is to pick a representative subject and then proceed to label structures/regions of interest in this image. Another is to create a "mean" or average subject. Atlases may also contain more than a single representative (e.g., the Visible Human contains both a male and a female data set). Other criteria besides gender may be used as well, and the atlas may contain many examples for a given criterion. In this work, we propose that atlases be created in an optimal manner using a well-established graph theoretic approach using a min spanning tree (or more generally, a collection of them). The resulting atlases may contain many examples for a given criterion. In fact, our framework allows for the addition of new subjects to the atlas to allow it to evolve over time. Furthermore, one can apply segmentation methods to the graph (e.g., graph-cut, fuzzy connectedness, or cluster analysis) which allow it to be separated into "sub-atlases" as it evolves. We demonstrate our method by applying it to 50 3D CT data sets of the chest region, and by comparing it to a number of traditional methods using measures such as Mean Squared Difference, Mattes Mutual Information, and Correlation, and for rigid registration. Our results demonstrate that optimal atlases can be constructed in this manner and outperform other methods of construction using freely available software.

关键词： digital atlas modeling anatomical models

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Dimensional accuracy of 3D printed vertebra

Dimensional accuracy of 3D printed vertebra

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

作者： Ogden, Kent Ordway, Nathaniel Diallo, Dalanda Tillapaugh-Fay, Gwen Aslan, Can SUNY Upstate Med Univ Dept Radiol 900 S Crouse Ave Syracuse NY 13210 USA SUNY Upstate Med Univ Dept Orthoped Surg Syracuse NY 13210 USA Syracuse Univ Dept Biomed & Chem Engn Syracuse NY 13210 USA

ISBN: (纸本)9780819498298

3D printer applications in the biomedical sciences and medical imaging are expanding and will have an increasing impact on the practice of medicine. Orthopedic and reconstructive surgery has been an obvious area for development of 3D printer applications as the segmentation of bony anatomy to generate printable models is relatively straightforward. There are important issues that should be addressed when using 3D printed models for applications that may affect patient care;in particular the dimensional accuracy of the printed parts needs to be high to avoid poor decisions being made prior to surgery or therapeutic procedures. In this work, the dimensional accuracy of 3D printed vertebral bodies derived from CT data for a cadaver spine is compared with direct measurements on the ex-vivo vertebra and with measurements made on the 3D rendered vertebra using commercial 3D image processing software. The vertebra was printed on a consumer grade 3D printer using an additive print process using PLA (polylactic acid) filament. Measurements were made for 15 different anatomic features of the vertebral body, including vertebral body height, endplate width and depth, pedicle height and width, and spinal canal width and depth, among others. It is shown that for the segmentation and printing process used, the results of measurements made on the 3D printed vertebral body are substantially the same as those produced by direct measurement on the vertebra and measurements made on the 3D rendered vertebra.

关键词： 3D printing orthopedic modeling dimensional accuracy vertebral modeling

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Diaphragm motion characterization using chest motion data for biomechanics-based lung tumor tracking during EBRT

Diaphragm motion characterization using chest motion data fo...

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conference on spie 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 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 Biomed Engn Grad Program London ON Canada

ISBN: (纸本)9781510600218

Despite recent advances in image-guided interventions, lung cancer External Beam Radiation Therapy (EBRT) is still very challenging due to respiration induced tumor motion. Among various proposed methods of tumor motion compensation, real-time tumor tracking is known to be one of the most effective solutions as it allows for maximum normal tissue sparing, less overall radiation exposure and a shorter treatment session. As such, we propose a biomechanics-based real-time tumor tracking method for effective lung cancer radiotherapy. In the proposed algorithm, the required boundary conditions for the lung Finite Element model, including diaphragm motion, are obtained using the chest surface motion as a surrogate signal. The primary objective of this paper is to demonstrate the feasibility of developing a function which is capable of inputting the chest surface motion data and outputting the diaphragm motion in real-time. For this purpose, after quantifying the diaphragm motion with a Principal Component Analysis (PCA) model, correlation coefficient between the model parameters of diaphragm motion and chest motion data was obtained through Partial Least Squares Regression (PLSR). Preliminary results obtained in this study indicate that the PCA coefficients representing the diaphragm motion can be obtained through chest surface motion tracking with high accuracy.

关键词： lung cancer EBRT tumor motion tracking biomechanical modeling PCA extrinsic marker

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image Reconstruction and Tissue Separation modeling with XFEM for Surgical Visualization and Guidance

Image Reconstruction and Tissue Separation Modeling with XFE...

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

作者： Pereira, Kyvia Ringel, Morgan Miga, Michael I. Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Inst Surg & Engn Nashville TN 37232 USA Vanderbilt Univ Dept Radiol & Radiol Sci Nashville TN USA Vanderbilt Univ Dept Neurol Surg Med Ctr Nashville TN USA Vanderbilt Univ Dept Otolaryngol Head & Neck Surg Med Ctr Nashville TN USA

ISBN: (纸本)9781510671614;9781510671607

Surgical procedures require precise target localization to ensure optimal outcomes and minimize patient risks. This paper presents an approach that combines eXtended Finite Element Method (XFEM) for retraction modeling with medical image updating to improve target visualization and localization accuracy in surgical guidance. XFEM is employed to simulate tissue retraction, capturing the complex mechanical behavior of tissue separation during surgery. By incorporating XFEM-derived displacement fields, a strategy for updating preoperative medical images is introduced, enabling adjustments to better visualize the tissue deformation state. XFEM's ability to model discontinuities in mechanical behavior provides a realistic representation of tissue retraction. To validate the effectiveness of the approach, experiments were conducted on tissue phantom samples. The average displacement error between the ground-truth measurements and the reconstruction using the proposed method ranged from 1.5 to 2.1 mm, with an overall average error of 1.8 +/- 1.3 mm across different phantom samples. This demonstrates improvements in target localization compared to traditional methods that do not account for tissue retraction. The integration of XFEM-based retraction modeling and displacement field-driven image updating offers an interesting tool for improving surgical guidance systems, ultimately leading to more precise and successful interventions.

关键词： eXtended Finite Element Method (XFEM) tissue retraction modeling medical image updating

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Computational modeling and Analysis for Left Ventricle Motion using CT/Echo image fusion

Computational Modeling and Analysis for Left Ventricle Motio...

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

作者： Kim, Ji-Yeon Kang, Nahyup Lee, Hyoung-Euk Kim, James D. K. Samsung Adv Inst Technol Adv Media Lab Yongin 446712 Gyeonggi Do South Korea

ISBN: (纸本)9780819498298

In order to diagnose heart disease such as myocardial infarction, 2D strain through the speckle tracking echocardiography (STE) or the tagged MRI is often used. However out-of-plane strain measurement using STE or tagged MRI is inaccurate. Therefore, strain for whole organ which are analyzed by simulation of 3D cardiac model can be applied in clinical diagnosis. To simulate cardiac contraction in a cycle, cardiac physical properties should be reflected in cardiac model. The myocardial wall in left ventricle is represented as a transversely orthotropic hyperelastic material, with the fiber orientation varying sequentially from the epicardial surface, through about 0 degrees at the midwall, to the endocardial surface. A time-varying elastance model is simulated to contract myocardial fiber, and physiological intraventricular systolic pressure curves are employed for the cardiac dynamics simulation in a cycle. And an exact description of the cardiac motion should be acquired in order that essential boundary conditions for cardiac simulation are obtained effectively. Real time cardiac motion can be acquired by using echocardiography and exact cardiac geometrical 3D model can be reconstructed using 3D CT data. In this research, image fusion technology from CT and echocardiography is employed in order to consider patient-specific left ventricle movement. Finally, longitudinal strain from speckle tracking echocardiography which is known to fit actual left ventricle deformation relatively well is used to verify these results.

关键词： Cardiac left ventricle finite element method echocardiographic image image fusion

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Inverse Biomechanical modeling of the Tongue via Machine Learning and Synthetic Training Data

Inverse Biomechanical Modeling of the Tongue via Machine Lea...

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

作者： Tolpadi, Aniket A. Stone, Maureen L. Carass, Aaron Prince, Jerry L. Gomez, Arnold D. Rice Univ Dept Bioengn Houston TX 77005 USA Univ Maryland Dept Orthodont Dept Neural & Pain Sci Dent Sch Baltimore MD 21201 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

ISBN: (数字)9781510616424

ISBN: (纸本)9781510616424

The tongue's deformation during speech can be measured using tagged magnetic resonance imaging, but there is no current method to directly measure the pattern of muscles that activate to produce a given motion. In this paper, the activation pattern of the tongue's muscles is estimated by solving an inverse problem using a random forest. Examples describing different activation patterns and the resulting deformations are generated using a finite-element model of the tongue. These examples form training data for a random forest comprising 30 decision trees to estimate contractions in 262 contractile elements. The method was evaluated on data from tagged magnetic resonance data from actual speech and on simulated data mimicking flaps that might have resulted from glossectomy surgery. The estimation accuracy was modest (5.6% error), but it surpassed a semi-manual approach (8.1% error). The results suggest that a machine learning approach to contraction pattern estimation in the tongue is feasible, even in the presence of flaps.

关键词： Machine-Learning Model Inversion Biomechanics Random Forest

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Statistical Analysis of Surrogate Signals to Incorporate Respiratory Motion Variability into Radiotherapy Treatment Planning

Statistical Analysis of Surrogate Signals to Incorporate Res...

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

作者： Wilms, Matthias Ehrhardt, Jan Werner, Rene Marx, Mirko Handels, Heinz Med Univ Lubeck Inst Med Informat D-23538 Lubeck Germany Univ Med Ctr Hamburg Eppendorf Dept Computat Neurosci Hamburg Germany

ISBN: (纸本)9780819498298

Respiratory motion and its variability lead to location uncertainties in radiation therapy (RT) of thoracic and abdominal tumors. Current approaches for motion compensation in RT are usually driven by respiratory surrogate signals, e.g., spirometry. In this contribution, we present an approach for statistical analysis, modeling and subsequent simulation of surrogate signals on a cycle-by-cycle basis. The simulated signals represent typical patient-specific variations of, e.g., breathing amplitude and cycle period. For the underlying statistical analysis, all breathing cycles of an observed signal are consistently parameterized using approximating B-spline curves. Statistics on breathing cycles are then performed by using the parameters of the B-spline approximations. Assuming that these parameters follow a multivariate Gaussian distribution, realistic time-continuous surrogate signals of arbitrary length can be generated and used to simulate the internal motion of tumors and organs based on a patient-specific diffeomorphic correspondence model. As an example, we show how this approach can be employed in RT treatment planning to calculate tumor appearance probabilities and to statistically assess the impact of respiratory motion and its variability on planned dose distributions.

关键词： Statistical analysis Motion models Tumors Monte Carlo methods 4D CT imaging Data modeling Statistical modeling Radiotherapy Spirometry Motion measurement

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