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Ultrasound imaging system combined with multi-modality image analysis algorithms to monitor changes in anatomical structures.

机译:超声成像系统与多模态图像分析算法相结合,可监控解剖结构的变化。

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摘要

This dissertation concerns the development and validation of an ultrasound imaging system and novel image analysis algorithms applicable to multiple imaging modalities. The ultrasound imaging system will include a framework for 3D volume reconstruction of freehand ultrasound: a mechanism to register the 3D volumes across time and subjects, as well as with other imaging modalities, and a playback mechanism to view image slices concurrently from different acquisitions that correspond to the same anatomical region. The novel image analysis algorithms include a noise reduction method that clusters pixels into homogenous patches using a directed graph of edges between neighboring pixels, a segmentation method that creates a hierarchical graph structure using statistical analysis and a voting system to determine the similarity between homogeneous patches given their neighborhood, and finally, a hybrid atlas-based registration method that makes use of intensity corrections induced at anatomical landmarks to regulate deformable registration. The combination of the ultrasound imaging system and the image analysis algorithms will provide the ability to monitor nerve regeneration in patients undergoing regenerative, repair or transplant strategies in a sequential, non-invasive manner, including visualization of registered real-time and pre-acquired data, thus enabling preventive and therapeutic strategies for nerve regeneration in Composite Tissue Allotransplantation (CTA). The registration algorithm is also applied to MR images of the brain to obtain reliable and efficient segmentation of the hippocampus, which is a prominent structure in the study of diseases of the elderly such as vascular dementia, Alzheimer's, and late life depression. Experimental results on 2D and 3D images, including simulated and real images, with illustrations visualizing the intermediate outcomes and the final results are presented.
机译:本论文涉及超声成像系统的开发和验证,以及适用于多种成像方式的新型图像分析算法。超声成像系统将包括用于手绘超声3D体积重建的框架:一种用于记录时间和对象以及其他成像方式的3D体积的机制,以及一种回放机制,用于同时查看对应于不同采集的图像切片到相同的解剖区域。新颖的图像分析算法包括使用相邻像素之间的边缘有向图将像素聚类为同质斑块的降噪方法,使用统计分析创建分层图结构的分割方法和投票系统以确定给定同质斑块之间的相似性他们的邻居,最后是一种基于地图集的混合配准方法,该方法利用在解剖界标处引起的强度校正来调节可变形配准。超声成像系统和图像分析算法的结合将能够以连续,无创的方式监测经历再生,修复或移植策略的患者的神经再生,包括可视化已记录的实时数据和预先获取的数据,从而为复合组织同种异体移植(CTA)中的神经再生提供了预防和治疗策略。该配准算法还应用于大脑的MR图像,以获取可靠,有效的海马区隔,这是研究老年人疾病(如血管性痴呆,阿尔茨海默氏病和晚期抑郁症)的重要结构。展示了2D和3D图像(包括模拟图像和真实图像)的实验结果,并以插图形式显示了中间结果和最终结果。

著录项

  • 作者

    Revanna Shivaprabhu, Vikas.;

  • 作者单位

    University of Pittsburgh.;

  • 授予单位 University of Pittsburgh.;
  • 学科 Biomedical engineering.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 146 p.
  • 总页数 146
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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