Vessel Segmentation/Registration from Ultrasound Images

超声图像的血管分割/注册

• 批准号: 6641019
• 负责人: SAYAN PATHAK
• 金额: $ 9.96万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-04 至 2004-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6641019
• 关键词: abdomen; aorta aneurysm; artificial intelligence; bioimaging /biomedical imaging; computer program /software; computer system design /evaluation; gastrointestinal circulation disorder; gastrointestinal imaging /visualization; human data; mathematics; three dimensional imaging /topography

DESCRIPTION (provided by applicant): Ultrasound is widely used for imaging of blood vessels as it is non-invasive, real-time, and relatively inexpensive. This proposal focuses on segmentation of abdominal aortic aneurysms (AAA) from ultrasound images with extension to other vascular imaging applications in the long term. Reliable quantitative evaluation of AAAs plays a pivotal role in diagnoses and frequent follow-up studies needed to avoid life-threatening rupture. These studies require vessel segmentation (for size analysis) and registration between serial studies (for monitoring the progression of the disease before and/or after vascular repair). AAA evaluation is routinely carried out for both high-risk patient populations and those treated with endovascular repair. Currently, AAA management is primarily based on measurements from two-dimensional (2-D) slices in CT scans. AAA monitoring and follow-up could be improved by 1) measurement from 3-D reconstructions, and 2) use of ultrasound imaging to minimize radiation exposure and reduce costs. 3-D ultrasound reconstructions provide accuracy comparable to that of CT. However, large inter-observer variability and long processing times preclude routine clinical use of 3-D image information. This research aims to develop software solutions for improved ultrasound-based AAA monitoring and other vascular diseases (in the long term). The tools used will be based on advanced image segmentation and registration algorithms involving curvature-driven image processing techniques and deformable models. The goal of the Phase I study is to establish feasibility of the proposed methods by demonstrating an improvement in the repeatability and accuracy of measurements and reduction in delineation time.

描述（由申请人提供）： 超声广泛用于对血管的成像，因为它是无创，实时且相对便宜的。该提案着重于从长远来看，从具有扩展到其他血管成像应用的超声图像的腹主动脉瘤（AAA）分割。对AAA的可靠定量评估在诊断和频繁的随访研究中起着关键作用，以避免威胁生命的破裂。这些研究需要血管分割（用于尺寸分析）和串行研究之间的注册（用于监测疾病的进展和/或血管修复后的进展）。对于高危患者和接受血管内修复治疗的患者，常规进行了AAA评估。当前，AAA管理主要基于CT扫描中二维（2-D）切片的测量值。 AAA监测和随访可以通过1）测量3-D重建，以及2）使用超声成像以最大程度地减少辐射暴露并降低成本。 3-D超声重建提供了与CT相当的精度。但是，大量观察者的变异性和较长的处理时间排除了3D图像信息的常规临床使用。这项研究旨在开发用于改进基于超声的AAA监测和其他血管疾病的软件解决方案（从长远来看）。所使用的工具将基于涉及曲率驱动的图像处理技术和可变形模型的高级图像分割和注册算法。 I阶段研究的目的是通过证明测量值的重复性和准确性以及描述时间的减少的重复性和准确性来确定所提出的方法的可行性。