Automated DECT Angiography Bone Removal

自动 DECT 血管造影去骨

• 批准号: 7611668
• 负责人: SANDY A. NAPEL
• 金额: $ 17.23万
• 依托单位: KITWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-15 至 2010-11-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7611668
• 关键词: Air; Algorithms; Anatomy; Angiography; Arts; Blood Vessels; Businesses; Clinical; Computer Assisted; Data; Databases; Detection; Development; Diagnosis; Disease; Doctor of Philosophy; Emergency Situation; Evaluation; Excision; Goals; Head and neck structure; Health; Healthcare; Image; Injury; Iodine; Knowledge; Lead; Manuals; Medical; Medical Imaging; Methods; Morphologic artifacts; Noise; Patients; Performance; Phase; Physician Executives; Pilot Projects; Plug-in; Principal Investigator; Radiology Specialty; Relative (related person); Research; Scanning; Slice; Small Business Innovation Research Grant; Solutions; System; Techniques; Technology; Tissues; Universities; Validation; Vascular Diseases; Visualization software; Work; X-Ray Computed Tomography; base; bone; calcification; design; experience; image processing; improved; innovation; medical schools; member; novel; prototype; public health relevance; research clinical testing; soft tissue; software development; treatment planning

DESCRIPTION (provided by applicant): This overall goal of this SBIR project is to develop a fully automated bone removal method for Dual Energy Computed Tomography (DECT) angiography scans. Dual energy scans offer the opportunity to better understand the material decomposition of anatomy, thus allowing for new methods to visualize and understand a wide range of diseases and conditions. In Phase I of this proposal we will develop and evaluate the main algorithmic components of our automated bone segmentation method, evaluate the potential impact on CTA workflow, and design a prototype user interface. We will also design, conduct, and analyze a preliminary evaluation of the automatically produced bone suppressed images with respect to manual segmentations. Algorithm development and evaluation will be performed using an existing database of dual energy clinical CT images, provided by GE Healthcare. In Phase II we will further improve the robustness of the method to include more diverse data from different dual-energy scanners and different anatomy, perform a larger clinical evaluation, and develop a commercial product. The ultimate goal of this work is to develop and sell this technology as an automated bone segmentation and removal product. This proposal is a partnership between Stanford University, which has extensive clinical expertise in developing computational aids for medical image interpretation, and Kitware, a small business with experience in medical visualization and software development. Currently, a fully robust and automated bone removal system does not exist, and the proposed novel solution has the potential to significantly improve current head and neck CTA interpretation making this a highly innovative and important project. The specific aims of the research are to: 1. Develop the key components of a fully automated dual-energy CTA bone segmentation and removal method consisting of: a. An algorithm component to perform the initial decomposition of anatomy (bone, vessels, air, soft tissue) based on dual-energy data. b. An algorithm component to recover vascular regions erroneously classified as bone by algorithm component (a). c. A final algorithm component to remove any non-vascular regions erroneously classified as vessels by the algorithm component (a) above, including the removal of partial volume bone fragments and high intensity fragments introduced by noise. 2. Develop and evaluate a prototype application incorporating these three algorithm components. The application will display the result of automated bone removal with a traditional 2D slice display and 3D MIP/volume renderings. 3. Perform a pilot study evaluating the accuracy of the automated bone removal relative to state of the art manual techniques while documenting the improvement in the workflow. PUBLIC HEALTH RELEVANCE: The goal of this project is to develop a fully automated bone removal method for Dual Energy Computed Tomography (DECT) angiography scans. The proposed DECT and algorithmic solution has the potential to significantly improve current head and neck CTA interpretation.

描述（由申请人提供）：该SBIR项目的总体目标是开发一种用于双能计算机断层扫描（DECT）血管造影扫描的全自动截骨方法。双能量扫描提供了更好地了解解剖结构的材料分解的机会，从而允许新的方法来可视化和了解各种疾病和病症。在本提案的第一阶段，我们将开发和评估我们的自动骨分割方法的主要算法组件，评估对CTA工作流程的潜在影响，并设计原型用户界面。我们还将设计、执行和分析自动生成的骨抑制图像与手动分割的初步评价。将使用GE Healthcare提供的现有双能量临床CT图像数据库进行算法开发和评价。在第二阶段，我们将进一步提高该方法的稳健性，以包括来自不同双能扫描仪和不同解剖结构的更多样化的数据，进行更大规模的临床评价，并开发商业产品。这项工作的最终目标是开发和销售这项技术作为自动化骨分割和去除产品。该提案是斯坦福大学和Kitware之间的合作，前者在开发用于医学图像解释的计算辅助工具方面具有广泛的临床专业知识，后者是一家在医学可视化和软件开发方面具有经验的小企业。目前，还不存在完全稳健和自动化的截骨系统，所提出的新解决方案有可能显著改善当前的头颈部CTA解释，使其成为一个高度创新和重要的项目。本研究的具体目的是：1.开发全自动双能量CTA骨分割和去除方法的关键组件，包括：a.基于双能量数据执行解剖结构（骨骼、血管、空气、软组织）初始分解的算法组件。B.算法组件，用于恢复被算法组件（a）错误地分类为骨骼的血管区域。C.最后的算法组件，用于去除由上述算法组件（a）错误地分类为血管的任何非血管区域，包括去除部分体积骨碎片和由噪声引入的高强度碎片。2.开发并评估一个包含这三个算法组件的原型应用程序。该应用程序将使用传统的2D切片显示和3D MIP/体积渲染显示自动截骨的结果。3.进行一项试点研究，评价自动截骨相对于最先进手动技术的准确性，同时记录工作流程的改进。公共卫生相关性：该项目的目标是开发一种用于双能量计算机断层扫描（DECT）血管造影扫描的全自动骨切除方法。所提出的DECT和算法解决方案有可能显著改善当前的头颈部CTA解释。