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

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Kernel-based modeling of pneumothorax deformation using intraoperative cone-beam CT images

Kernel-based modeling of pneumothorax deformation using intr...

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

作者： Maekawa, Hinako Nakao, Megumi Mineura, Katsutaka Chen-Yoshikawa, Toyofumi F. Matsuda, Tetsuya Kyoto Univ Grad Sch Informat Kyoto Japan Kyoto Univ Hosp Dept Thorac Surg Kyoto Japan Nagoya Univ Hosp Dept Thorac Surg Nagoya Aichi Japan

ISBN: (纸本)9781510640269

In this study, we introduce statistical modeling methods for pneumothorax deformation using paired cone-beam computed tomography (CT) images. We designed a deformable mesh registration framework for shape changes involving non-linear deformation and rotation of the lungs. The registered meshes with local correspondences are available for both surgical guidance in thoracoscopic surgery and building statistical deformation models with inter-patient variations. In addition, a kernel-based deformation learning framework is proposed to reconstruct intraoperative deflated states of the lung from the preoperative CT models. This paper reports the findings of pneumothorax deformation and evaluation results of the kernel-based deformation framework.

关键词： Pneumothorax deformation Registration Statistical modeling Cone-beam CT Thoracoscopic Surgery

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Multi-body statistical shape representation of anatomy for navigation in Robot-Assisted Laparoscopic Partial Nephrectomy

Multi-body statistical shape representation of anatomy for n...

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

作者： Kokko, Michael A. Seigne, John D. Van Citters, Douglas W. Halter, Ryan J. Dartmouth Coll Thayer Sch Engn 14 Engn Dr Hanover NH 03755 USA Dartmouth Coll Geisel Sch Med 1 Rope Ferry Rd Hanover NH 03755 USA Dartmouth Hitchcock Med Ctr Div Urol 1 Med Ctr Dr Lebanon NH 03756 USA

ISBN: (纸本)9781510640269

image guidance for abdominal procedures requires an anatomical model capable of representing significant displacement and deformation of relevant tissues in a computationally efficient manner. This work evaluates the suitability of statistical shape modeling to represent key structures in Robot Assisted Laparoscopic Partial Nephrectomy (RALPN) both individually, and also as a multi-body composite model. Tomography obtained from subjects in an ongoing RALPN study was used to produce surface model representations of the kidneys, abdominal aorta, and inferior vena cava. Each structure was resliced and remeshed in a standardized fashion to allow for extraction of the principal modes of variation. Reduced parameter representations of the example structures based on the strongest eigenmodes indicate that <5mm average RMSE modeling accuracy can be achieved with four parameters for the individual models and eight parameters for the four-body composite model. The magnitude of centroid displacements observed under the principal modes of variation is consistent with literature-reported values, suggesting that this approach may be suitable for image guidance in RALPN.

关键词： Statistical Shape modeling Partial Nephrectomy Anatomical modeling Tissue Displacement Tissue Deformation

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modeling the Surgical Exposure of Anatomy in Robot-Assisted Laparoscopic Partial Nephrectomy

Modeling the Surgical Exposure of Anatomy in Robot-Assisted ...

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

ISBN: (纸本)9781510633988

Although robotic instrumentation has revolutionized manipulation in oncologic laparoscopy, there remains a significant need for image guidance during the exposure portion of certain abdominal procedures. The high degree of mobility and potential for deformation associated with abdominal organs and related structures poses a significant challenge to implementing image based navigation for the initial phase of robot-assisted laparoscopic partial nephrectomy (RALPN). This work introduces two key elements of a RALPN exposure simulation framework: a model for laparoscopic exposure and a compact representation of anatomical geometry suitable for integration into a statistical estimation framework. Data to drive the exposure simulation were collected during a clinical RALPN case in which the robotic endoscope was tracked in six dimensions. An initial rigid registration was performed between a preoperative CT scan and the frame of the optical tracker, allowing the endoscope trajectory to be replayed over tomography to simulate anatomical observations with realistic kinematics. CT data from five study subjects were combined with four publicly available datasets to produce a mean kidney shape. This template kidney was fit back to each of the input models by optimally tuning a set of eight parameters, achieving an average RMSE of 2.18mm. These developments represent important steps toward a full, clinically-relevant framework for simulating organ exposure and testing navigation algorithms. In future work, a particle filter estimation scheme will be integrated into the simulation to incrementally optimize correspondences between parametric anatomical models and simulated or reconstructed endoscopic observations.

关键词： Surgical Navigation robotic Surgery Partial Nephrectomy Surgical Exposure Anatomical modeling

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The image-to-Physical Liver Registration Sparse Data Challenge: Characterizing Inverse Biomechanical Model Resolution

The Image-to-Physical Liver Registration Sparse Data Challen...

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

作者： Heiselman, Jon S. Miga, Michael, I Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Vanderbilt Inst Surg & Engn 221 Kirkland Hall Nashville TN 37235 USA

ISBN: (纸本)9781510633988

image-guided liver surgery relies on intraoperatively acquired data to create an accurate alignment between image space and the physical patient anatomy. Often, sparse data of the anterior liver surface can be collected for these registrations. However, achieving accurate registration to sparse surface data when soft tissue deformation is present remains a challenging open problem. While many approaches have been developed, a common standard for comparing algorithm performance has yet to be adopted. The image-to-physical liver registration sparse data challenge offers a publicly available dataset of realistic sparse data patterns collected on a deforming liver phantom for the purpose of evaluating and comparing potential registration approaches. Additionally, the challenge is designed to allow testing and characterization of these methods as a general utility for the registration community. Using this challenge environment, an inverse biomechanical method for deformable registration to sparse data was investigated with respect to how whole-organ target registration error (TRE) is impacted by a model parameter that controls the spatial reconstructive resolution of mechanical loads applied to the organ. For this analysis, this resolution parameter was varied across a wide range of values and TRE was calculated from the challenge dataset. An optimal parameter value for model resolution was found and average TRE across the 112 sparse data challenge cases was reduced to 3.08 +/- 0.85 mm, an approximate 32% improvement over previously reported results. The value of the data offered by the sparse data challenge is evident. This work was performed entirely using information automatically generated by the challenge submission and processing site.

关键词： Liver image registration Data modeling Natural surfaces Surgery Data acquisition Reconstruction algorithms Algorithm development

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Optimal boundary conditions for model based brain shift simulation in deep brain stimulation surgery

Optimal boundary conditions for model based brain shift simu...

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

作者： Li, Chen Fan, Xiaoyao Aronson, Joshua Paulsen, Keith D. Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Dartmouth Coll Geisel Sch Med Hanover NH 03755 USA Norris Cotton Canc Ctr Lebanon NH 03756 USA Dartmouth Hitchcock Med Ctr Lebanon NH 03756 USA

ISBN: (纸本)9781510640269

Brain shift is a confounder to the accuracy of electrode lead placement during deep brain stimulation (DBS) surgery. Model based image updating method can compensate for brain shift with high efficiency and accuracy. A key element to achieving clinically accepted accuracy using our biomechanical brain model is designating rigorous boundary conditions (BCs) that define general physics of the model. In this retrospective study, we searched for a set of six optimal BCs such as gravitational direction and level of CSF for our model to simulate accurate brain shift in DBS lead placement surgery. Specifically, we conducted 9072 trials of brain shift simulation with varying boundary conditions and deep brain sparse data for three training cases and applied these parameters to three testing cases for evaluation. The optimal set of parameters was determined based on lowest target registration error (TRE) evaluated at five deep brain landmarks near the subthalamus area. We show that simulations with optimal BCs compensated 61.28% and 50.06% of brain shift on average in two of the three testing cases where large brain deformation occurred and 26.5% in one testing case of small brain shift. In comparison, optimal BCs delivered consistent and accurate prediction of brain shift at all deep brain landmarks in both training and testing cases whereas default sets of BCs produced similar results at some landmarks but underperformed for the rest. With only deep brain sparse data and a set of optimal BCs, our biomechanical brain model can achieve significant brain shift compensation in DBS cases and Its clinical utility will be examined in surgical cases in future OR.

关键词： DBS brain shift modeling finite element method image guided surgery

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Spherical Harmonics for modeling Shape Transformations of Breasts following Breast Surgery

Spherical Harmonics for Modeling Shape Transformations of Br...

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

作者： Sampathkumar, U. Nowroozilarki, Z. Reece, G. P. Hanson, S. E. Merchant, F. A. Univ Houston Dept Comp Sci Houston TX 77204 USA Univ Houston Dept Engn Technol Houston TX USA MD Anderson Canc Ctr Dept Plast Surg Houston TX USA

ISBN: (纸本)9781510633988

Breast shape aesthetic is most desired outcome of cosmetic and reconstructive breast surgery. Post-operative (post-op) patient satisfaction largely depends on patient expectations, hence appropriately communicating information about surgical options to patients and moderating patient expectations is critical in surgical planning. Breast modeling and computational simulation can help mitigate this challenge and provide an effective tool for presenting potential surgical outcomes and eliciting patient preferences. Most available computational models lack the ability to provide realistic estimation of breast shape changes. We have previously developed a Fourier spherical harmonics (SPHARM) based computational approach to model breast shape'. SPHARM modeling results in 1320 coefficients that are effective descriptors of the 3D breast shape. In this study, we develop a framework to transform the SPHARM coefficients of the pre-operative (pre-op) breast to generate an estimation of the post-op breast shape for cosmetic (e.g. implant-based augmentation) and reconstructive (e.g. implant, autologous tissue, etc.) surgery procedures. Least squares optimization was used to realize the transformation between the pre- and post-op SPHARM coefficients. We demonstrate the feasibility of our approach using data from patients who have undergone bilateral implant-based breast reconstruction as part of cancer treatment. We trained a random forest regression function using SPHARM coefficients and their corresponding shape transformation vectors for 21 pre op breasts and validated the regressor using a test dataset of 41 breasts. Our preliminary results demonstrate feasibility of the proposed data-driven approach to model transformation of the pre-op breast to its post-op form for a given surgical procedure.

关键词： Breast shape transformation modeling SPHARM Spherical harmonics Random forest regression

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Feasibility of 3D Motion-Compensated Needle Guidance for TIPS procedures

Feasibility of 3D Motion-Compensated Needle Guidance for TIP...

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

作者： Wagner, Martin G. Periyasamy, Sarvesh Speidel, Michael A. Laeseke, Paul F. Univ Wisconsin Dept Med Phys 1111 Highland Ave Madison WI 53705 USA Univ Wisconsin Dept Radiol 600 Highland Ave Madison WI 53792 USA Univ Wisconsin Dept Med 1685 Highland Ave Madison WI 53705 USA

ISBN: (纸本)9781510633988

X-ray fluoroscopy is commonly used to guide needles during transjugular intrahepatic portosystemic shunt (TIPS) procedures. Respiratory motion and the 2D nature of the x-ray projections, however, make it difficult to accurately guide the needle from a hepatic vein, through the liver parenchyma, and into the portal vein, which is not visible on x-ray images in the absence of continuous contrast-enhancement. Due to these challenges, multiple needle passes are often required, which increase the risk for perforation of the liver capsule and hemorrhage. To overcome these challenges, we propose a motion-compensated 3D needle guidance system, which generates a respiratory motion model of the portal venous system using a 3D DSA and a contrast enhanced 2D x-ray sequence acquired under free breathing conditions. The respiratory motion is tracked during the needle pass based on brightness variations above and below the diaphragm in the 2D images, which allows for creation of a motion-compensated surface model of the target vasculature. Additionally, a 3D needle reconstruction algorithm from two 2D x-ray images is presented, which allows for motion-compensated 3D device imaging. A preliminary pig study was performed to evaluate the feasibility of the proposed techniques. The biplane needle reconstruction was compared to conventional cone beam CT acquisitions, where a root mean squared distance of 0.98 mm and a tip localization error of 1.22 mm were measured. The maximum error of the estimated vascular motion per frame in the two pig studies was 0.63 mm and 1.63 mm respectively. If successfully translated to clinical TIPS procedures, the proposed needle guidance could result in fewer unnecessary needle passes and therefore shorter procedure times and lower risk to the patient.

关键词： Veins 3D modeling X-rays image segmentation 3D image processing Motion models X-ray imaging 3D acquisition Liver Reconstruction algorithms

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Segmentation and Removal of Surgical Instruments for Background Scene Visualization from Endoscopic/Laparoscopic Video

Segmentation and Removal of Surgical Instruments for Backgro...

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

作者： Hasan, S. M. Kamrul Simon, Richard A. Linte, Cristian A. RIT Biomed Modeling Visualizat & Image guided Nav BiM Rochester NY 14623 USA Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Biomed Engn Rochester NY 14623 USA

ISBN: (纸本)9781510640269

Surgical tool segmentation is becoming imperative to provide detailed information during intra-operative execution. These tools can obscure surgeons' dexterity control due to narrow working space and visual field-of-view, which increases the risk of complications resulting from tissue injuries (e.g. tissue scars and tears). This paper demonstrates a novel application of segmenting and removing surgical instruments from laparoscopic/endoscopic video using digital inpainting algorithms. To segment the surgical instruments, we use a modified U-Net architecture (U-NetPlus) composed of a pre-trained VGG11 or VGG16 encoder and redesigned decoder. The decoder is modified by replacing the transposed convolution operation with an up-sampling operation based on nearest-neighbor (NN) interpolation. This modification removes the artifacts generated by the transposed convolution, and, furthermore, these new interpolation weights require no learning for the upsampling operation. The tool removal algorithms use the tool segmentation mask and either the instrument-free reference frames or previous instrument-containing frames to fill-in (i.e., inpaint) the instrument segmentation mask with the background tissue underneath. We have demonstrated the performance of the proposed surgical tool segmentation/removal algorithms on a robotic instrument dataset from the MICCAI 2015 EndoVis Challenge. We also showed successful performance of the tool removal algorithm from synthetically generated surgical instruments-containing videos obtained by embedding a moving surgical tool into surgical tool-free videos. Our application successfully segments and removes the surgical tool to unveil the background tissue view otherwise obstructed by the tool, producing visually comparable results to the ground truth.

关键词： Surgical tool segmentation tool removal video inpainting non-parametric optical flow affine parametric motion Poisson blending

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A Quantitative Prediction of the Post-operative Lobectomy Lung Physiology using a GPU-based Linear Elastic Lung Biomechanics Model and a Constrained Generative Adversarial Learning Approach

A Quantitative Prediction of the Post-operative Lobectomy Lu...

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

作者： Santhanam, Anand Stiehl, Brad Lauria, Michael Barjaktarevic, Igor Goldin, Jonathan Yanagawa, Jane Low, Daniel Univ Calif Los Angeles Dept Radiat Oncol Los Angeles CA 90024 USA Univ Calif Los Angeles Div Pulm & Crit Care Los Angeles CA USA Univ Calif Los Angeles Div Radiol Los Angeles CA USA Univ Calif Los Angeles Div Thorac Surg Los Angeles CA USA

ISBN: (纸本)9781510640269

Lobectomy is a common and effective procedure for treating early-stage lung cancers. However, for patients with compromised pulmonary function (e.g. COPD) lobectomy can lead to major postoperative pulmonary complications. A technique for quantitatively predicting postoperative pulmonary function is needed to assist surgeons in assessing candidate's suitability for lobectomy. We present a framework for quantitatively predicting the postoperative lung physiology and function using a combination of lung biomechanical modeling and machine learning strategies. A set of 10 patients undergoing lobectomy was used for this purpose. The image input consists of pre- and post-operative breath hold CTs. An automated lobe segmentation algorithm and lobectomy simulation framework was developed using a Constrained Adversarial Generative Networks approach. Using the segmented lobes, a patient-specific GPU-based linear elastic biomechanical and airflow model and surgery simulation was then assembled that quantitatively predicted the lung deformation during the forced expiration maneuver. The lobe in context was then removed by simulating a volume reduction and computing the elastic stress on the surrounding residual lobes and the chest wall. Using the deformed lung anatomy that represents the post-operative lung geometry, the forced expiratory volume in 1 second (FEV1) (the amount of air exhaled by a patient in 1 second starting from maximum inhalation), and forced vital capacity (FVC) (the amount of air exhaled by force from maximum inhalation), were then modeled. Our results demonstrated that the proposed approach quantitatively predicted the postoperative lobe-wise lung function at the FEV1 and FEV/FVC.

关键词： Lobectomy Lung Dynamics Lung Segmentation Biomechanical modeling Machine Learning

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Medical Imaging 2021: image-guided procedures, robotic interventions, and modeling

Medical Imaging 2021: Image-Guided Procedures, Robotic Inter...

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Medical Imaging 2021: image-guided procedures, robotic interventions, and modeling

ISBN: (纸本)9781510640252

The proceedings contain 87 papers. The topics discussed include: ultrasound multi-needle detection using deep attention U-Net with TV regularizations;segmentation and removal of surgical instruments for background scene visualization from endoscopic / laparoscopic video;simultaneous localization and mapping for laparoscopic liver navigation - a comparative evaluation study;context encoder guided self-supervised Siamese depth estimation based on stereo laparoscopic images;stereovision surface stitching for image updating in open spine surgery;single-shot three-dimensional reconstruction for colonoscopic image analysis;joint deep learning framework for image registration and segmentation of late gadolinium enhanced MRI and cine cardiac MRI;and quantitative assessment of proton beam dose and myocardial lesion formation detected by high-resolution, ex vivo delayed contrast-enhanced MRI for treatment of ventricular tachycardia.

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