Quantitative diagnostic dual energy CT with atlas-based prior knowledge

近十年来，双能CT （DECT）在临床常规检查中得到了广泛应用。所有主要的CT制造商都提供不同的硬件和软件概念，DECT的应用是相似的：获得的DECT数据用于进行双或多材料分解，例如从软组织中分离碘和骨骼，或定量造影剂和脂肪，分类或表征组织，或增加对比度（CNR最大化），或抑制对比度（伪影减少）。应用程序被设计用于特定的器官，用户需要注意调用正确的应用程序，并只在适当的器官或解剖区域解释其输出。例如，解释肾结石在肾脏以外器官的应用会产生错误的分类。为了获得定量结果，应用程序需要设置特定于患者的参数。为了校准这些，用户被要求将roi放置在预定义的解剖区域。由于这很耗时，用户往往倾向于使用默认设置，而不是优化它们。在这里，我们希望开发一个DECT图谱，利用其解剖（和功能）信息进行上下文敏感的DECT成像和材料分解，并能够在不需要用户交互的情况下自动校准开放参数。为了提高量化，初始图像不需要单独重建，而是在转换到图像域之前进行基于原始数据的分解。应开发一个专用的用户界面，以提供新的上下文敏感的DECT信息，例如，将每个器官自动分解为不同但合理的基础材料，并以方便的方式显示给读者。同样，用户放置的ROI应触发对ROI内容的上下文敏感的统计评估，并将其提供给用户。这将有助于量化肿瘤或病变中的碘摄取，将其与脂肪或钙成分分离，估计其血液供应等。由于DECT数据仅显示给定实例的造影剂摄取，并且由于这种对比取决于患者特定的因素，如心输出量，因此我们计划借助图谱中包含的双能量信息对造影剂摄取进行标准化。这将最大限度地减少患者之间和患者内部的影响，并增加可重复性。此外，应制定器官特异性的材料评分，以每个器官为基础量化患者的材料组成。新方法（DECT图谱、材料分解等）应通过幻影和患者研究进行测试和评估，并进行相应的优化。

During the last decade, dual energy CT (DECT) became widely available in clinical routine. Offered by all major CT manufacturers, with differing hard- and software concepts, the DECT applications are alike: the acquired DECT data are used to conduct two- or multi material decompositions, e.g. to separate iodine and bone from soft tissue or to quantify contrast agent and fat, to classify or characterize tissue, or to increase contrasts (CNR maximization), or to suppress contrasts (artifact reduction). The applications are designed to work with certain organs and the user needs to take care to invoke the correct application and to interpret its output only in the appropriate organ or anatomical region. E.g. interpreting the output of a kidney stone applications in organs other than the kidney will yield a wrong classification. To obtain quantitative results the applications require to set patient-specific parameters. In order to calibrate those the user is asked to place ROIs in predefined anatomical regions. Since this is time-consuming users are often tempted to use the default settings instead of optimizing them. Here, we want to develop a DECT atlas to utilize its anatomical (and functional) information for con-text-sensitive DECT imaging and material decomposition, and to be able to automatically calibrate the open parameters without the need of user interaction. To improve quantification the initial images shall not be reconstructed separately but rather undergo a rawdata-based decomposition before being converted into image domain. A dedicated user interface shall be developed to provide the new context-sensitive DECT information - such as automatically decomposing each organ into different but reasonable basis materials, for example - and to display it to the reader in a convenient way. Similarly, user-placed ROIs shall trigger a context-sensitive statistical evaluation of the ROI's contents and provide it to the user. This will help to quantify the iodine uptake in a tumor or a lesion, to separate it from fat or calcium components, to estimate its blood supply etc. Since the DECT data display the contrast uptake just for a given instance in time and since this contrast depends on patient-specific factors such as the cardiac output, we are planning to normalize the contrast uptake with the help of the dual energy information contained in the atlas. This will minimize inter and intra patient effects and increase the reproducibility. In addition, organ-specific material scores shall be developed that quantify a patient's material composition on an organ by organ basis. The new methods (DECT atlas, material decomposition, ...) shall be tested and evaluated using phantom and patient studies, and shall be optimized accordingly.