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共 277 条记录，以下是271-280 订阅

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Theoretically and Experimentally Driven Noise Model for X-ray Fluoroscopic image Synthesis

Theoretically and Experimentally Driven Noise Model for X-ra...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： J. F. Whitehead Y. Hu J.W. Kim Y.-B. Chang J. Manak Canon Medical Research USA Vernon Hills Illinois United States of America

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Fluoroscopic and digital angiography (DA) images are both acquired during x-ray image guided interventions. Fluoroscopic images are acquired at lower radiation dose and therefore have higher image noise. The purpose of this work is to develop a method for fluoroscopic image synthesis from DA images. The developed noise model leverages measured system parameters to determine a mapping from DA images to fluoroscopic images without requiring knowledge of the system parameters that the DA images were acquired with. The noise texture between simulated and real fluoroscopic images was found to be similar with a mean absolute percent difference between normalized noise power spectrums of 0.7±0.7%. The noise magnitude was also similar, with a mean absolute percent difference between noise-curves acquired at 70, 90, and 120 kVp of 4.2±6.4%, 3.9±4.6%, and 1.9±1.3%, respectively. This method facilitates fluoroscopic image synthesis from DA images for the training of deep learning algorithms or testing of denoising algorithms with a known reference.

关键词： Training Semiconductor device modeling Semiconductor device measurement image synthesis Semiconductor detectors Noise Noise reduction

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modeling the interaction between navigation probe and deformable brain tissue based on finite element analysis: preliminary study

Modeling the interaction between navigation probe and deform...

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IEEE-EMBS conference on Biomedical Engineering and Sciences

作者： Tan Chye Cheah Alwin Kumar Rathinam S. Anandan Shanmugam Vicknes Waran Department of Electrical and Electronic Engineering The University of Nottingham Malaysia Campus Biomodelling Unit Centre for Biomedical and Technology Integration (CBMTI) Pte Ltd University of Malaya Neurosurgical Unit Faculty of Medicine University of Malaya

ISBN: (纸本)9781467316644

image-guided surgery (IGS) plays an important role in clinical treatment and interventions. It is a surgical technique utilizing computerized equipment to identify the anatomical structures based on preoperative image data i.e. computed tomography (CT) and magnetic resonance imaging (MRI) and in finding the most suitable surgical path to reach these structures. In the medical discipline of neurosurgery where IGS is frequently used, it has been noted that the brain undergoes varying levels of deformation at different stages of the operative procedure. Current navigation software based on preoperative imaging information cannot accurately describe such deformations could subsequently lead to surgical localization error. This paper presents modeling of soft tissue deformation based on finite element analysis, which includes the geometry model, material properties used for the model, finite element mesh, and boundary constraint conditions. The navigation probe-tissue interaction is modeled by conducting stress-strain analysis on the brain model using a visco-elastic material model. The simulation closely depicts the actual scene;the results show that the highest deformation occurs at the probe tip when the probe goes in contact with the brain tissue. After the probe breaks the tissue's surface tension, the stress occurs at the surrounding region of the probe. This biomodeling analysis provides useful information concerning brain tissues deformation when under external stress which may help in building biomechanical deformation model to predict the intraoperative brain shift.

关键词： image-guide surgery Brain tissue deformation Finite element analysis Tool-tissue interaction Biomodel

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CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers

CathFlow: Self-Supervised Segmentation of Catheters in Inter...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Alex Ranne Liming Kuang Yordanka Velikova Nassir Navab Ferdinando Rodriguez Y Baena Hamlyn Centre for Robotic Surgery Imperial College London UK Chair for Computer Aided Medical Procedures and Augmented Reality (CAMP) Technical University of Munich Garching Germany Munich Center for Machine Learning Munich Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique, but exposes patients and surgeons to prolonged radiation. Alternatives such as ultrasound are difficult to interpret, are highly prone to artifacts and noise, and vary in quality, depending on the experience of the interventional radiologist and machine settings. In this work, we seek to address both problems by introducing a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images, without demanding any labeled data. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism, and is capable of learning feature changes across time and space. To facilitate training, we used synthetic ultrasound data based on physics-driven catheter insertion simulations, and translated the data into a unique CT-Ultrasound common domain, CACTUSS, to improve the segmentation performance. We generated ground truth segmentation masks by computing the optical flow between adjacent frames using FlowNet2, and performed thresholding to obtain a binary mask estimate. Finally, we validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms, thus demonstrating its potential for applications to clinical data in the future.

关键词： image segmentation Ultrasonic imaging Motion segmentation Computational modeling Transformers imaging phantoms Feature extraction Data models Catheters Optical flow

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System Design of an Automated Drilling Device for Neurosurgical Applications

System Design of an Automated Drilling Device for Neurosurgi...

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Asia-Pacific conference on Intelligent Robot Systems (ACIRS)

作者： Sriram Marisetty Pavan Kumar Pothula Pon Deepika C K Vinay Vikas Vazhayil Madhav Rao Surgical and Assistive Robotics Lab IIIT Bangalore Department of Neurosurgery NIMHANS

ISBN: (数字)9781728198187

ISBN: (纸本)9781728198194

Mechanical drilling and perforation of bone is one of the most standard practices followed in neurosurgery, but is a highly specialised task performed by trained neurosurgeons. Craniotomy refers to removing part of the skull bone to access the underlying tissues, which is currently performed manually involving a series of precautionary procedures. The non-planar surface of the brain makes the manual drilling process over a series of marked points less accurate. guided robotic drilling is one such alternative towards the contemporary craniotomy method that leads to increased accuracy, thereby minimizing permanent secondary damages to the patient under treatment. The paper proposes a design towards achieving the first step in building an autonomous neurosurgical tool for craniotomy practices over a non-planar surface. The proposed device caters towards reaching to a specified target location in the surgical space, and orient the drilling head system perpendicular to the specified point on the non-planar surface to perform needle insertion, or deep drilling. In addition, the proposed tool follows the guided path in a perpendicular orientation along the series of drilling points on a non-planar skull surface, where the skull surface is derived from IR imaging of the patient in the operative field. The real-time 3D model of the patient's head is reconstructed post multiple scanning from the handheld portable IR camera. The developed autonomous drilling system is driven by the path planned and annotated by the neurosurgeon on the 3D model generated from the handheld IR camera in the intraoperative space. The autonomous drill positioning system is validated experimentally on a 3D printed human head, and the system placement, along with the orientation errors and accuracy in guided path showed promising results.

关键词： Three-dimensional displays Head Neurosurgery Solid modeling Bones image reconstruction

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Knowledge-guided Surgery: Augmented Reality Based Registration and Projection for Lesion Tissue Information

Knowledge-Guided Surgery: Augmented Reality Based Registrati...

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Jianren Chen Teng Liu Xiaoxue Sun Siquan Liang Haitao Li Yanding Qin Hongpeng Wang Jianda Han College of Artificial Intelligence Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China College of Computer Science Nankai University Tianjin China Tianjin Huanhu Hospital Tianjin China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

To address the challenges of providing real-time guidance during surgical procedures and fully utilizing preoperative lesion data information intraoperatively, we propose an augmented reality-based, knowledge-guided method for surgical procedures. Our approach includes designing semantic segmentation algorithms for medical images to achieve accurate segmentation of lesion tissues. Using the segmentation results, we locate tissues and organs, perform surgical path planning after 3D reconstruction, and ultimately generate a virtual knowledge model that includes semantic information about lesions and preoperative planning methods. To align the virtual surgical knowledge model with the patient's lesion site in the real surgical environment, we establish a portable high-precision augmented reality display system with external depth cameras. We use joint calibration, point cloud alignment, and wireless communication transmission to improve the augmented reality device's scene perception capability and achieve the precise integration of virtual and real objects in holographic projection. The experiment results show that the surgical guidance method proposed in this article can provide rich surgical information and has high registration accuracy, which is helpful for doctors to perform surgical operations.

关键词： Point cloud compression Solid modeling Accuracy Navigation Surgery Cameras Calibration Lesions Augmented reality Biomedical imaging

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3D Reconstruction of Cerebral Aneurysm Geometry from 2D Angiographic imaging using Deep Learning

3D Reconstruction of Cerebral Aneurysm Geometry from 2D Angi...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： R. E. White M. Mattei Di Eugenio R. White V.K. Chivukula Dept. of Biomedical Engineering and Sciences Florida Institute of Technology Melbourne Florida United States of America Dept. of Mathematics and Systems Engineering Florida Institute of Technology Melbourne Florida United States of America

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

The sudden rupture of a cerebral aneurysm can result in severe disability or death and is treatable with neurosurgical clipping or minimally invasive endovascular procedures (coiling or flow diverters) guided by angiography. However, the need for retreatment is relatively common. Clinicians rely on experience and 2D image techniques to lead and guide endovascular procedures. Cerebral vasculature is highly three-dimensional, with complex 3D curvature and tortuosity. Current imaging techniques are used during procedures to ‘flatten’ the complex vasculature. In this study, we developed a method for using 3 uniquely oriented angiographic images to reconstruct the 3D aneurysm *** used open-source datasets of 149 aneurysm geometries consisting of patient-specific cerebral aneurysms and surrounding vasculature with a range of complex morphologies and sizes. After leaving 30 for unseen testing, we augmented the remaining dataset by orientation and scaling, yielding a dataset of over 24,000 unique geometries. We restructured the irregular point clouds into a regular grid. This 3D data functioned as the output to the neural network. Simulated angiographic images were obtained by using 2D projections of the gridded data in the XY, YZ, and XZ planes. These three projections were used as the input to the *** developed a 3D convolutional neural network with a customized dice loss function to account for the surrounding padding. Initial results are promising, showing that complex vasculature can be identified in 3D; however, DARNN has trouble distinguishing multiple outlet vessels in a small region. Predictions are made in 0.1 s.

关键词： Geometry Point cloud compression Solid modeling Three-dimensional displays Semiconductor detectors Neural networks Morphology

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Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation

Curvilinear Structure Tracking by Low Rank Tensor Approximat...

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IEEE conference on Computer Vision and Pattern Recognition

作者： Erkang Cheng Yu Pang Ying Zhu Jingyi Yu Haibin Ling Department of Computer and Information Sciences Temple University Siemens Corporate Technology Department of Computer and Information Science University of Delaware

ISBN: (纸本)9781479951192

Robust tracking of deformable object like catheter or vascular structures in X-ray images is an important technique used in image guided medical interventions for effective motion compensation and dynamic multi-modality image fusion. Tracking of such anatomical structures and devices is very challenging due to large degrees of appearance changes, low visibility of X-ray images and the deformable nature of the underlying motion field as a result of complex 3D anatomical movements projected into 2D images. To address these issues, we propose a new deformable tracking method using the tensor-based algorithm with model propagation. Specifically, the deformable tracking is formulated as a multi-dimensional assignment problem which is solved by rank-1 l_1 tensor approximation. The model prior is propagated in the course of deformable tracking. Both the higher order information and the model prior provide powerful discriminative cues for reducing ambiguity arising from the complex background, and consequently improve the tracking robustness. To validate the proposed approach, we applied it to catheter and vascular structures tracking and tested on X-ray fluoroscopic sequences obtained from 17 clinical cases. The results show, both quantitatively and qualitatively, that our approach achieves a mean tracking error of 1.4 pixels for vascular structure and 1.3 pixels for catheter tracking.

关键词： Tensile stress Computational modeling Approximation methods X-ray imaging Trajectory Target tracking

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