4 research outputs found

    Advanced Endoscopic Navigation:Surgical Big Data,Methodology,and Applications

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    随着科学技术的飞速发展,健康与环境问题日益成为人类面临的最重大问题之一。信息科学、计算机技术、电子工程与生物医学工程等学科的综合应用交叉前沿课题,研究现代工程技术方法,探索肿瘤癌症等疾病早期诊断、治疗和康复手段。本论文综述了计算机辅助微创外科手术导航、多模态医疗大数据、方法论及其临床应用:从引入微创外科手术导航概念出发,介绍了医疗大数据的术前与术中多模态医学成像方法、阐述了先进微创外科手术导航的核心流程包括计算解剖模型、术中实时导航方案、三维可视化方法及交互式软件技术,归纳了各类微创外科手术方法的临床应用。同时,重点讨论了全球各种手术导航技术在临床应用中的优缺点,分析了目前手术导航领域内的最新技术方法。在此基础上,提出了微创外科手术方法正向数字化、个性化、精准化、诊疗一体化、机器人化以及高度智能化的发展趋势。【Abstract】Interventional endoscopy (e.g., bronchoscopy, colonoscopy, laparoscopy, cystoscopy) is a widely performed procedure that involves either diagnosis of suspicious lesions or guidance for minimally invasive surgery in a variety of organs within the body cavity. Endoscopy may also be used to guide the introduction of certain items (e.g., stents) into the body. Endoscopic navigation systems seek to integrate big data with multimodal information (e.g., computed tomography, magnetic resonance images, endoscopic video sequences, ultrasound images, external trackers) relative to the patient's anatomy, control the movement of medical endoscopes and surgical tools, and guide the surgeon's actions during endoscopic interventions. Nevertheless, it remains challenging to realize the next generation of context-aware navigated endoscopy. This review presents a broad survey of various aspects of endoscopic navigation, particularly with respect to the development of endoscopic navigation techniques. First, we investigate big data with multimodal information involved in endoscopic navigation. Next, we focus on numerous methodologies used for endoscopic navigation. We then review different endoscopic procedures in clinical applications. Finally, we discuss novel techniques and promising directions for the development of endoscopic navigation.X.L. acknowledges funding from the Fundamental Research Funds for the Central Universities. T.M.P. acknowledges funding from the Canadian Foundation for Innovation, the Canadian Institutes for Health Research, the National Sciences and Engineering Research Council of Canada, and a grant from Intuitive Surgical Inc

    Advances in Biomedical Applications and Assessment of Ultrasound Nonrigid Image Registration.

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    Image volume based registration (IVBaR) is the process of determining a one-to-one transformation between points in two images that relates the information in one image to that in the other image quantitatively. IVBaR is done primarily to spatially align the two images in the same coordinate system in order to allow better comparison and visualization of changes. The potential use of IVBaR has been explored in three different contexts. In a preliminary study on identification of biometric from internal finger structure, semi-automated IVBaR-based study provided a sensitivity and specificity of 0.93 and 1.00 respectively. Visual matching of all image pairs by four readers yielded 96% successful match. IVBaR could potentially be useful for routine breast cancer screening and diagnosis. Nearly whole breast ultrasound (US) scanning with mammographic-style compression and successful IVBaR were achieved. The image volume was registered off-line with a mutual information cost function and global interpolation based on the non-rigid thin-plate spline deformation. This Institutional Review Board approved study was conducted on 10 patients undergoing chemotherapy and 14 patients with a suspicious/unknown mass scheduled to undergo biopsy. IVBaR was successful with mean registration error (MRE) of 5.2±2 mm in 12 of 17 ABU image pairs collected before, during or after 115±14 days of chemotherapy. Semi-automated tumor volume estimation was performed on registered image volumes giving 86±8% mean accuracy compared with a radiologist hand-segmented tumor volume on 7 cases with correlation coefficient of 0.99 (p<0.001). In a reader study by 3 radiologists assigned to mark the tumor boundary, significant reduction in time taken (p<0.03) was seen due to IVBaR in 6 cases. Three new methods were developed for independent validation of IVBaR based on Doppler US signals. Non-rigid registration tools were also applied in the field of interventional guidance of medical tools used in minimally invasive surgery. The mean positional error in a CT scanner environment improved from 3.9±1.5 mm to 1.0±0.3 mm (p<0.0002). These results show that 3D image volumes and data can be spatially aligned using non-rigid registration for comparison as well as quantification of changes.Ph.D.Applied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64802/1/gnarayan_1.pd
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