Targeted Imaging in Helical cone-beam CT

描述（由申请人提供）：在该项目中，我们建议开发、优化和翻译螺旋锥束计算机断层扫描（CBCT）中的先进精确图像重建算法。近似重建算法已被用于临床螺旋CBCT，它们已被优化，并工作良好，为许多临床病例。然而，放射科医生每天也会遇到CBCT图像包含临床显著伪影，达到干扰准确诊断的水平。最近开发的精确算法有可能消除或减轻伪影，同时也减少辐射剂量。为了将这些算法转化为临床应用，必须克服许多技术障碍。在这一点上，我们认为解决这些技术问题实际上比追求进一步的纯理论算法开发更重要。解决这些具有临床意义的技术问题也将提供一个范例和路线图，如果引入重大的新硬件开发，未来的研究工作可以遵循这一范例和路线图。拟议的研究旨在填补重要的空白，将理论上精确的算法转化为提高图像质量，降低成像剂量，并通过解决这些问题实现螺旋CBCT的新成像功能。为了实现将新开发的精确算法转化为实用的螺旋CBCT的总体目标，我们设计了四个具体目标：（1）开发和实现适用于实用螺旋CBCT的重建算法;（2）开发和优化用于定量CT的精确重建算法;（3）使用数值和物理体模数据测试和评估算法;以及（4）在模型和人类观察者研究中测试和评估算法。前两个目标集中在（a）开发、优化和翻译精确算法，以显著减少螺旋CBCT当前临床应用中的图像伪影;（B）研究和利用数据冗余，以减少螺旋CBCT中的成像剂量和图像伪影。最后两个目标是为了在实际应用中对算法性能进行全面的评估。评价研究的一个主要动机是为目标1和2的任务提供指导。我们相信，该项目具有很高的科学和临床意义，因为它可以加快翻译的精确算法，以提高图像质量和降低成像剂量。它还可以为开发新的CBCT成像能力产生具有重要意义的新见解。该项目具有多方面的高创新性，可解决算法转化为临床应用的关键问题，并研究和使用数据冗余来提高图像质量和降低成像剂量。

DESCRIPTION (provided by applicant): In the project, we propose to develop, optimize, and translate advanced, exact algorithms for image reconstruction in helical cone-beam computed tomography (CBCT). Approximate reconstruction algorithms have been used for clinical helical CBCT, and they have been optimized, and work well, for many clinical cases. However, on a daily basis, radiologists also encounter CBCT images containing clinically significant artifacts, attaining levels that interfere with accurate diagnosis. The recently developed exact algorithms have the potential to eliminate or mitigate the artifacts while also reducing radiation dose. In order to translate these algorithms into clinic applications, many technical hurdles must be overcome. At this point, we believe that tackling these technical issues is actually more important than pursuing further purely theoretical algorithm development. Working through these technical problems of clinical significance will also provide a paradigm and roadmap that can be followed by research effort in the future if and when major new hardware developments are introduced. The proposed research is intended to fill in important gaps toward translating the theoretically exact algorithms to improving image quality, reducing imaging dose, and enabling new imaging capabilities in helical CBCT by tackling the issues. To achieve the overall objective of translating the newly developed exact algorithms to practical helical CBCT, we have designed four specific aims: (1) to develop and implement reconstruction algorithms tailored to practical helical CBCT; (2) to develop and optimize exact reconstruction algorithms for quantitative CT; (3) to test and evaluate the algorithms by using numerical and physical phantom data; and (4) to test and evaluate the algorithms in model- and human-observer studies. The first two aims focus on (a) the development, optimization, and translation of the exact algorithms that can significantly reduce the image artifacts in current clinical application of helical CBCT and (b) the investigation and exploitation of data redundancy for reducing the imaging dose and image artifacts in helical CBCT. The last two aims are designed for thorough evaluation of the algorithm performance in practical applications. A key motivation for the evaluation studies is to provide guidance for the tasks in Aims 1 and 2. We believe that the project is of high scientific and clinical significance in that it can expedite the translation of exact algorithms for improving image quality and reducing imaging dose. It can also produce new insights of significant implications for developing new CBCT imaging capability. The project has multiple facets of high innovation for addressing critical issues in the algorithms' translation to clinical applications and for investigating and using data redundancy for improving the image quality and lowering the imaging dose.