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项目的总体目标是为双能计算机断层扫描（DECT）血管造影扫描开发一种全自动的骨骼去除方法。双能扫描提供了更好地了解解剖结构的材料分解的机会，从而使新方法可视化和了解广泛的疾病和状况。在本提案的第一阶段，我们将开发和评估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，进行和分析对手动分割自动产生的骨骼抑制图像的初步评估。 GE Healthcare提供的现有双能临床CT图像的数据库将进行算法开发和评估。在第二阶段，我们将进一步提高该方法的鲁棒性，以包括来自不同双能扫描仪和不同解剖结构，进行更大的临床评估并开发商业产品的更多样化的数据。这项工作的最终目标是开发和销售这项技术作为自动骨细分和去除产品。该建议是斯坦福大学之间的合作伙伴关系，该大学在开发用于医学图像解释的计算辅助工具方面拥有广泛的临床专业知识，以及具有医疗可视化和软件开发经验的小型企业Kitware。当前，尚不存在完全健壮和自动化的骨骼清除系统，拟议的新颖解决方案有可能显着改善当前的头部和颈部CTA解释，从而使这是一个高度创新且重要的项目。该研究的具体目的是：1。开发全自动双能CTA骨分割的关键组成部分和由：a组成的去除方法。一种基于双能量数据的解剖结构（骨，容器，空气，软组织）的初始解剖分解的算法。 b。通过算法分量（a）错误地分类为骨骼的血管区域的算法组件。 c。最终的算法分量可去除上面的算法（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噪声引入的高强度片段。 2。开发和评估包含这三种算法组件的原型应用程序。该应用程序将显示传统的2D切片显示器和3D MIP/卷渲染的自动骨去除结果。 3.进行一项试验研究，以评估自动骨去除相对于艺术手动技术的状态的准确性，同时记录工作流程的改善。公共卫生相关性：该项目的目的是开发一种全自动的骨头去除方法，用于双能计算机断层扫描（DECT）血管造影扫描。提出的DECT和算法解决方案具有显着改善当前头颈CTA解释的潜力。