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去骨方法。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状况。在该提案的第一阶段，我们将开发和评估自动骨骼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、进行和分析对自动生成的骨抑制图像相对于手动分割的初步评估。算法开发和评估将使用 GE Healthcare 提供的现有双能临床 CT 图像数据库进行。在第二阶段，我们将进一步提高该方法的稳健性，以包含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估，并开发商业产品。这项工作的最终目标是将这项技术作为自动骨分割和去除产品进行开发和销售。该提案是斯坦福大学和 Kitware 之间的合作伙伴关系。斯坦福大学在开发医学图像解释计算辅助工具方面拥有丰富的临床专业知识，Kitware 是一家在医学可视化和软件开发方面拥有丰富经验的小型企业。目前，还不存在完全强大的自动化去骨系统，而所提出的新颖解决方案有可能显着改善当前的头颈 CTA 解释，使其成为一个高度创新且重要的项目。该研究的具体目标是： 1. 开发全自动双能 CTA 骨分割和去除方法的关键组件，包括：一种算法组件，用于基于双能数据执行解剖结构（骨骼、血管、空气、软组织）的初始分解。 b.用于恢复被算法组件(a)错误地分类为骨骼的血管区域的算法组件。 c.最终算法组件用于去除被上述算法组件（a）错误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. 开发并评估包含这三个算法组件的原型应用程序。该应用程序将通过传统的 2D 切片显示和 3D MIP/体积渲染来显示自动去骨的结果。 3. 进行试点研究，评估自动去骨相对于最先进的手动技术的准确性，同时记录工作流程的改进。公共健康相关性：该项目的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去骨方法。所提出的 DECT 和算法解决方案有可能显着改善当前头颈部 CTA 解释。