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）： 超声波广泛用于血管成像，因为它是非侵入性的、实时的且相对便宜。该提案的重点是从超声图像中分割腹主动脉瘤（AAA），并从长远来看扩展到其他血管成像应用。 AAA 的可靠定量评估在诊断和避免危及生命的破裂所需的频繁随访研究中发挥着关键作用。这些研究需要血管分割（用于尺寸分析）和系列研究之间的登记（用于监测血管修复之前和/或之后疾病的进展）。 AAA 评估通常对高危患者群体和接受血管内修复治疗的患者进行。目前，AAA 管理主要基于 CT 扫描中二维 (2-D) 切片的测量。 AAA 监测和随访可以通过 1) 3-D 重建测量和 2) 使用超声成像来最大程度地减少辐射暴露并降低成本来改进。 3-D 超声重建的准确性可与 CT 相媲美。然而，观察者之间的巨大差异和较长的处理时间阻碍了 3D 图像信息的常规临床使用。本研究旨在开发软件解决方案，以改进基于超声的 AAA 监测和其他血管疾病（从长远来看）。所使用的工具将基于先进的图像分割和配准算法，涉及曲率驱动图像处理技术和可变形模型。第一阶段研究的目标是通过证明测量的可重复性和准确性的改进以及描绘时间的减少来确定所提出的方法的可行性。