Automated DECT Angiography Bone Removal

描述（由申请人提供）：这个SBIR项目的总体目标是为双能量计算机断层扫描（DECT）血管造影扫描开发一种全自动骨移除方法。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广泛的疾病和条件。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，并设计一个原型用户界面。我们还将设计、执行和分析自动生成的骨抑制图像与人工分割的初步评估。算法开发和评估将使用GE医疗集团提供的现有双能量临床CT图像数据库进行。在II期，我们将进一步提高该方法的稳健性，包括来自不同双能扫描仪和不同解剖结构的更多样化数据，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开发和销售这种技术作为一种自动骨分割和去除产品。该提案是斯坦福大学（Stanford University）和Kitware之间的合作伙伴关系，前者在开发用于医学图像解释的计算辅助设备方面拥有广泛的临床专业知识，后者是一家拥有医疗可视化和软件开发经验的小型企业。目前，还不存在完全健壮和自动化的骨去除系统，所提出的新解决方案有可能显着改善当前头颈部CTA解释，使其成为一个高度创新和重要的项目。研究的具体目的是：1。开发全自动双能CTA骨分割去除方法的关键组件，包括：a.基于双能数据对解剖（骨、血管、空气、软组织）进行初始分解的算法组件。b.用于恢复被算法组件(a)错误分类为骨骼的血管区域的算法组件。c.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的最终算法组件，包括去除部分体积骨碎片和由噪声引入的高强度碎片。2. 开发和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/体效果图显示自动骨移除的结果。3. 进行一项试点研究，评估相对于最先进的手工技术的自动骨移除的准确性，同时记录工作流程的改进。公共卫生相关性：该项目的目标是为双能量计算机断层扫描（DECT）血管造影扫描开发一种全自动去骨方法。所提出的DECT和算法解决方案具有显著改善当前头颈部CTA解释的潜力。

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.