Accelerated statistical image reconstruction methods for X-ray CT

描述(申请人提供)：X射线CT的加速统计图像重建方法为众多医学应用提供了有价值的成像信息。CT扫描使用量的增加显著影响了人群的辐射剂量。减少X射线CT剂量将需要在数据采集、图像重建、后处理、采集协议的改变和消除不必要的扫描方面取得进展。统计图像重建(SIR)方法已经在核医学成像中被常规使用了十多年，并开始应用于低剂量的临床X射线CT扫描。最适合于亚mSv CT扫描的SIR方法使用精确的模型，用于X射线CT系统的物理和统计；这些方法需要非常长的计算时间，阻碍了它们的常规临床应用。该项目的主要目标是开发更快的SIR算法，使SIR方法能够常规用于所有类型的CT扫描，特别是亚mSv扫描。所开发的方法将适用于广泛的图像获取几何形状和统计成本函数，因此将补充其他研究人员在这些方面的进展。要显著加快SIR，需要的不仅仅是等待计算机技术的进步。时钟频率不再增加，CT技术的进步(例如，双能量、双源和更宽的锥角)继续增加CT数据量。传统的SIR算法是从数学上设计的，很少考虑现代计算体系结构。在这个项目中，成像科学家和计算机科学家将合作开发、实施和评估SIR算法，这些算法是为提供多个级别(指令级、核心级和节点级)并行性的现代多核计算系统量身定做的。其目标是使常规螺旋胸部CT扫描的SIR计算时间低于5分钟，剂量低于mSv，以实现SIR方法的普遍使用。开发的方法将使所有解剖成像部位、临床应用和患者群体受益。该项目的调查将集中在密歇根大学以通常40-80 mA(取决于患者大小)管电流的80%和20%采集的大量患者肺结节C扫描。80%的剂量扫描通常在1-1.5mSv范围内，而20%的剂量扫描在0.25-0.4mSv范围内。此集合中存档的正弦图为研究亚mSv胸部CT以及与常规剂量胸部CT扫描进行比较提供了独特而有价值的资源。数值观察者研究和放射科观察者研究都将评估在亚mSv剂量下使用先进的SIR方法对肺结节的检测性能和形态特征。考虑到USPSTF最近的建议草案[1，2]，这些肺部研究尤其及时，这可能会导致对有肺癌风险的患者大量增加年度CT检查的使用。

DESCRIPTION (provided by applicant): Accelerated statistical image reconstruction methods for X-ray CT X-ray CT provides valuable imaging information for numerous medical applications. Increasing use of CT scans con- tributes significantly to population radiation dose. Decreasing X-ray CT dose will require advances in data acquisition, image reconstruction, post processing, changes in acquisition protocols, and elimination of unneeded scans. Statistical image re- construction (SIR) methods have been used routinely in nuclear medicine imaging for over a decade and have begun to be applied to reduced dose clinical X-ray CT scans. The types of SIR methods that are likely to be most suitable for sub-mSv CT scans use accurate models for the physics and statistics of X-ray CT systems; these methods have required very long computation times that impede their routine clinical use. The primary goal of this project is to develop significantly faster SIR algorithms that will enable routine use of SIR methods for all types of CT scans, particularly sub-mSv scans. The methods developed will be applicable to a wide variety of image acquisition geometries and statistical cost functions, and thus will complement advances in these components by other researchers. Achieving significant acceleration of SIR requires much more than simply waiting for advances in computer technology. Clock frequencies are no longer increasing, and advances in CT technology (e.g., dual energy, dual sources, and wider cone angles) continue to increase CT data sizes. Traditional SIR algorithms have been designed mathematically with little consideration of modern computing architectures. In this project, imaging scientists and computer scientists will collaborate to develop, implement, and evaluate SIR algorithms that are tailored to modern many-core computing systems that provide parallelism at multiple scales (instruction level, core level, and node level). The goal is to achieve SIR compute times of less than 5 minutes for routine helical chest CT scans at sub-mSv doses, to enable universal use of SIR methods. The methods developed will benefit all anatomic imaging sites, clinical applications, and patient populations. This project's investigation will focus on a large collection of patient lung nodule C scans that were acquired at the University of Michigan at both 80% and 20% of the usual 40-80 mA (depending on patient size) tube currents for such scans. The 80% dose scans are typically in the 1-1.5 mSv range whereas the 20% dose scans are in the 0.25-0.4 mSv range. The archived sinograms in this collection provide a unique and valuable resource for investigating sub-mSv chest CT and for comparing to regular dose chest CT scans. Both numerical observer studies and radiologist observer studies will evaluate detection performance and morphological characterization of lung nodules with advanced SIR methods at sub-mSv doses. These lung studies are particularly timely in light of the recent USPSTF draft recommendation [1, 2] that is likely to lead to substantial increased use of annual CT exams for patients at risk of lung cancer.