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

共 935 条记录，以下是71-80 订阅

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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

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)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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Augmented reality visualization of hyperspectral imaging classifications for image-guided brain tumor phantom resection

Augmented reality visualization of hyperspectral imaging cla...

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

作者： Huang, James Halicek, Martin Shahedi, Maysam Fei, Baowei Univ Texas Dallas Dept Bioengn Richardson TX 75083 USA Univ Texas Southwestern Med Ctr Dallas Adv Imaging Res Ctr Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dallas Dept Radiol Dallas TX 75390 USA

ISBN: (纸本)9781510633988

Wearable augmented reality (AR) is an emerging technology with enormous potential for use in the medical field, from training and procedure simulations to image-guided surgery. medical AR seeks to enable surgeons to see tissue segmentations in real time. With the objective of achieving real-time guidance, the emphasis on speed produces the need for a fast method for imaging and classification. Hyperspectral imaging (HSI) is a non-contact, optical imaging modality that rapidly acquires hundreds of images of tissue at different wavelengths, which can be used to generate spectral data of the tissue. Combining HSI infoimation and machine-learning algorithms allows for effective tissue classification. In this paper, we constructed a brain tissue phantom with porcine blood, yellow-dyed gelatin, and colorless gelatin to represent blood vessels, tumor, and noimal brain tissue, respectively. Using a segmentation algorithm, hundreds of hyperspectral images were compiled to classify each of the pixels. Three segmentation labels were generated from the data, each with a different type of tissue. Our system virtually superimposes the HSI channels and segmentation labels of a brain tumor phantom onto the real scene using the HoloLens AR headset. The user can manipulate and interact with the segmentation labels and HSI channels by repositioning, rotating, changing visibility, and switching between them. All actions can be performed through either hand or voice controls. This creates a convenient and multifaceted visualization of brain tissue in real time with minimal user restrictions. We demonstrate the feasibility of a fast and practical HIS-AR technique for potential use of image-guided brain surgery.

关键词： Augmented reality hyperspectral imaging image-guided surgery brain tumor resection

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SimITK: Model Driven Engineering for medical imaging

SimITK: Model Driven Engineering for Medical Imaging

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

作者： Trezise, Melissa Gobbi, David Cordy, James Abolmaesumi, Purang Mousavi, Parvin Queens Univ Sch Comp Kingston ON Canada Univ Calgary Calgary AB Canada Univ British Columbia Dept Elect Comp Engn Vancouver BC Canada

ISBN: (纸本)9780819498298

The Insight Segmentation and Registration Toolkit (ITK) is a highly utilized open source medical imaging library providing chiefly the functionality to register, segment, and filter medical images. Although extremely powerful, ITK has a steep learning curve for users with little or no background in programming. It was for this reason that SimITK was developed. SimITK wraps ITK into the model driven engineering environment Simulink, a part of the Matlab development suite. The first released version of SimITK was a proof of concept, and demonstrated that ITK could be wrapped successfully in Simulink. In this paper a new version of SimITK is presented where ITK classes are wrapped using a fully automated process. In addition, SimITK is transitioned to successfully support ITK version 4, in order to remain current with the ITK project. SimITK includes thirty-seven image filters, twelve optimizers, and nineteen transform classes from ITK version 4 which are successfully wrapped and tested, and can be quickly and easily combined to perform medical imaging tasks. These classes were chosen to represent a broad range of usability, and to allow for greater flexibility when creating registration pipelines. SimITK has the potential to reduce the learning curve for ITK and allow the user to focus on developing workflows and algorithms. A release of SimITK along with tutorials and videos is available at ***.

关键词： SimITK ITK Simulink medical imaging Registration Model Driven Engineering

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Enhancing MR-guided Laser Interstitial Thermal Therapy Planning using U-Net: A Data-Driven Approach for Predicting MR Thermometry images

Enhancing MR-guided Laser Interstitial Thermal Therapy Plann...

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

作者： Sadatamin, Saba Ketabi, Sara Donszelmann-Lund, Elise Abtahi, Saba Chaban, Yuri Robbins, Steven Tyc, Richard Khalvati, Farzad Waspe, Adam C. Kahrs, Lueder A. Drake, James M. Hosp Sick Children Posluns Ctr Image Guided Innovat & Therapeut Inte Toronto ON Canada Univ Toronto Inst Biomed Engn Toronto ON Canada Univ Toronto Dept Comp Sci Toronto ON Canada Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Univ Toronto Dept Med Imaging Toronto ON Canada Monteris Med Winnipeg MB Canada

ISBN: (纸本)9781510671614;9781510671607

Epilepsy, a common neurological disorder causing recurring seizures. Magnetic Resonance-guided Laser Interstitial Thermal Therapy (MRgLITT) is a promising minimally-invasive technique to ablate the target especially for drug resistance epilepsy. MRgLITT employs a laser fiber to ablate brain tissue through heat deposition, offering real-time monitoring through Magnetic Resonance (MR) thermometry images and precise treatment planning using MRI planning images. In this study, we developed an AI-based approach utilizing a U-Net model, a convolutional neural network architecture widely used for image to image translation, to predict MR thermometry images from anatomical MRI planning images from a dataset of 81 patients with mesial temporal lobe epilepsy. The model's performance was evaluated on a test dataset using the structural similarity index (SSIM) and root mean squared error (RMSE).

关键词： Deep learning Artificial intelligence Magnetic resonance guided laser interstitial thermal therapy (MRgLITT) magnetic resonance imaging Thermometry Data-driven planning tool

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Surface-based registration of liver in ultrasound and CT

Surface-based registration of liver in ultrasound and CT

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

作者： Dehghan, Ehsan Lu, Kongkuo Yan, Pingkun Tahmasebi, Amir Xu, Sheng Wood, Bradford J. Abi-Jaoudeh, Nadine Venkatesan, Aradhana Kruecker, Jochen Philips Res North Amer Briarcliff Manor NY 10510 USA NIH Dept Radiol Bethesda MD 20892 USA NIH Imaging Sci Clin Ctr Bethesda MD 20892 USA

ISBN: (纸本)9781628415056

Ultrasound imaging is an attractive modality for real-time image-guided interventions. Fusion of US imaging with a diagnostic imaging modality such as CT shows great potential in minimally invasive applications such as liver biopsy and ablation. However, significantly different representation of liver in US and CT turns this image fusion into a challenging task, in particular if some of the CT scans may be obtained without contrast agents. The liver surface, including the diaphragm immediately adjacent to it, typically appears as a hyper-echoic region in the ultrasound image if the proper imaging window and depth setting are used. The liver surface is also well visualized in both contrast and non-contrast CT scans, thus making the diaphragm or liver surface one of the few attractive common features for registration of US and non-contrast CT. We propose a fusion method based on point-to-volume registration of liver surface segmented in CT to a processed electromagnetically (EM) tracked US volume. In this approach, first, the US image is pre-processed in order to enhance the liver surface features. In addition, non-imaging information from the EM-tracking system is used to initialize and constrain the registration process. We tested our algorithm in comparison with a manually corrected vessel-based registration method using 8 pairs of tracked US and contrast CT volumes. The registration method was able to achieve an average deviation of 12.8mm from the ground truth measured as the root mean square Euclidean distance for control points distributed throughout the US volume. Our results show that if the US image acquisition is optimized for imaging of the diaphragm, high registration success rates are achievable.

关键词： Multi-modality fusion image registration liver-diaphragm surface image-guided intervention ultrasound computed tomography electromagnetic tracking

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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

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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Novel method to improve feature extraction in MR for model-based image updating in image-guided neurosurgery

Novel method to improve feature extraction in MR for model-b...

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

作者： Chen, Kristen Li, Chengpei Fan, Xiaoyao Davis, Scott Evans, Linton Paulsen, Keith Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Dartmouth Coll Geisel Sch Med Hanover NH 03755 USA Norris Canc Ctr Lebanon NH 03756 USA Dartmouth Hitchcock Med Ctr Lebanon NH 03756 USA

ISBN: (纸本)9781510671614;9781510671607

In image-guided neurosurgery, preoperative magnetic resonance (pMR) images are rigidly registered with the patient's head in the operating room. image-guided systems incorporate this spatial information to provide real-time information on where surgical instruments are located with respect to preoperative imaging. The accuracy of these systems rely on the rigid relationship between the patient's brain and the preoperative scan, which typically does not hold true due to intraoperative brain shift. To account for this brain shift, we previously developed an image-guidance updating framework that incorporates brain shift information acquired from registering intraoperative stereovision (iSV) surface with the pMR surface to create an updated magnetic resonance image (uMR). To register the iSV surface and the pMR surface, the two surfaces must have some matching features that can be used for registration. However, for some cases, the matching features could fall outside of the segmented brain volume causing a lack of matching features for registration between iSV and pMR surfaces. To capture features falling outside of the brain volume, we have developed a method to improve feature extraction, which involves performing a selective dilation in the region of the stereovision surface. The goal of this method is to capture features that fall outside of the brain volume without capturing too much noise. With further testing, this method has potential in supplementing brain segmentation to improve image registration between iSV and pMR surfaces within the image-guidance updating framework.

关键词： Brain image segmentation Feature extraction Neuroimaging image registration Brain mapping Volume rendering Magnetic resonance imaging Neurosurgery 3D modeling

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A MR-TRUS Registration Method for Ultrasound-guided Prostate interventions

A MR-TRUS Registration Method for Ultrasound-Guided Prostate...

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

作者： Yang, Xiaofeng Rossi, Peter Mao, Hui Jani, Ashesh B. Ogunleye, Tomi Curran, Walter J. Liu, Tian Emory Univ Dept Radiat Oncol Atlanta GA 30322 USA Emory Univ Winship Canc Inst Atlanta GA 30322 USA Emory Univ Dept Radiol & Imaging Sci Atlanta GA 30322 USA

ISBN: (纸本)9781628415056

In this paper, we report a MR-TRUS prostate registration method that uses a subject-specific prostate strain model to improve MR-targeted, US-guided prostate interventions (e.g., biopsy and radiotherapy). The proposed algorithm combines a subject-specific prostate biomechanical model with a B-spline transformation to register the prostate gland of the MRI to the TRUS images. The prostate biomechanical model was obtained through US elastography and a 3D strain map of the prostate was generated. The B-spline transformation was calculated by minimizing Euclidean distance between the normalized attribute vectors of landmarks on MR and TRUS prostate surfaces. This prostate tissue gradient map was used to constrain the B-spline-based transformation to predict and compensate for the internal prostate-gland deformation. This method was validated with a prostatephantom experiment and a pilot study of 5 prostate-cancer patients. For the phantom study, the mean target registration error (TRE) was 1.3 mm. MR-TRUS registration was also successfully performed for 5 patients with a mean TRE less than 2 mm. The proposed registration method may provide an accurate and robust means of estimating internal prostate-gland deformation, and could be valuable for prostate-cancer diagnosis and treatment.

关键词： MRI-TRUS registration image-guided intervention prostate cancer

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Quantitative analysis of 4D MR volume reconstruction methods from dynamic slice acquisitions

Quantitative analysis of 4D MR volume reconstruction methods...

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

作者： Romaguera, Liset Vazquez Plantefeve, Rosalie Kadoury, Samuel Ecole Polytech Montreal Montreal PQ Canada Ctr Hosp Univ Montreal Ctr Rech Montreal PQ Canada

ISBN: (纸本)9781510625501

Respiratory motion models aim at improving the quality of free breathing image acquisition protocols and yield increased targeting accuracy during image guided interventions. Respiratory motion can deviate pre-defined targets and trajectories determined preoperatively during treatment procedures. In this context, motion models offer a mean to estimate spatio-temporal displacements of the organ and correct the target position in real time during an intervention. To construct a motion model, data of the entire organ of interest must be acquired. However, existing techniques for 3D dynamic imaging have poor spatial and temporal resolution. Therefore, to capture the organ's temporal behavior, series of dynamic 2D slices covering the entire organ are typically acquired. Then, these slices are reordered retrospectively according to their motion phase within the respiratory cycle and stacked to form 3D dynamic volumes known as 4D images (3D + t). On the other hand, while numerous metrics were proposed to assess the spatial quality of the reordering, little attention has been paid to metrics that assess the coherent temporal behavior of the reconstructed dynamic volumes. This work proposes a method combining image-based matching approach with manifold alignment and compares it with two state of the art slice reordering methods. Methods were evaluated on a dataset of 7 volunteers using new metrics to assess the spatial quality and the temporal behavior, with the proposed method outperforming in terms of both spatial and temporal quality.

关键词： 4D imaging slice reordering manifold learning liver MRI

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Infrared image-guidance for intraoperative assessment of limb length discrepancy during total hip arthroplasty procedures

Infrared image-guidance for intraoperative assessment of lim...

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

作者： Yan, Jerry Chaurasia, Akash Takasuka, Hannah Jithendra, Aditi State, Claire McCarren, Katie Li, Robert Bender, Evan Hill, Matthew Benassi, Thomas Oni, Julius Manbachi, Amir Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Johns Hopkins Bayview Med Ctr Dept Orthopaed Surg Baltimore MD 21224 USA Johns Hopkins Univ Dept Neurosurg Baltimore MD USA

ISBN: (纸本)9781510633988

During total hip arthroplasty (THA) procedures, orthopedic surgeons rely on visual and tactile cues to minimize the risk of a limb length discrepancy (LLD) arising from improper implant placement and selection. This paper outlines a novel application of a commercial infrared (IR) motion controller, Leap Motion, to function as an optical tracker of LLD. Additionally, a custom benchtop testing model was used to evaluate accuracy and precision of this commercial IR motion controller against a custom-built IR stereo vision camera. This paper includes a discussion of the algorithms and infrared technology used to image and localize points. The novel design discussed in this paper has clinical applications for a custom-built IR stereo vision setup that will be positioned above the incision site during a THA procedure. Accompanying this solution concept is a fiducial with IR-visible landmarks for quick and accurate point selection and a software algorithm that measures changes between pre- and post-operative leg length.

关键词： image-guided surgery orthopedic surgery intraoperative infrared imaging stereo vision total hip arthroplasty leg length leg length discrepancy

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