Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.
J Digit Imaging. 2012 Feb;25(1):179-88. doi: 10.1007/s10278-011-9410-1.
Imaging centers nationwide are seeking innovative means to record and monitor computed tomography (CT)-related radiation dose in light of multiple instances of patient overexposure to medical radiation. As a solution, we have developed RADIANCE, an automated pipeline for extraction, archival, and reporting of CT-related dose parameters. Estimation of whole-body effective dose from CT dose length product (DLP)--an indirect estimate of radiation dose--requires anatomy-specific conversion factors that cannot be applied to total DLP, but instead necessitate individual anatomy-based DLPs. A challenge exists because the total DLP reported on a dose sheet often includes multiple separate examinations (e.g., chest CT followed by abdominopelvic CT). Furthermore, the individual reported series DLPs may not be clearly or consistently labeled. For example, "arterial" could refer to the arterial phase of the triple liver CT or the arterial phase of a CT angiogram. To address this problem, we have designed an intelligent algorithm to parse dose sheets for multi-series CT examinations and correctly separate the total DLP into its anatomic components. The algorithm uses information from the departmental PACS to determine how many distinct CT examinations were concurrently performed. Then, it matches the number of distinct accession numbers to the series that were acquired and anatomically matches individual series DLPs to their appropriate CT examinations. This algorithm allows for more accurate dose analytics, but there remain instances where automatic sorting is not feasible. To ultimately improve radiology patient care, we must standardize series names and exam names to unequivocally sort exams by anatomy and correctly estimate whole-body effective dose.
全国的影像中心都在寻求创新的方法来记录和监测与计算机断层扫描（CT）相关的辐射剂量，因为有多次患者过度接受医疗辐射的情况发生。作为一种解决方案，我们开发了 RADIANCE，这是一种用于提取、存档和报告与 CT 相关剂量参数的自动化管道。从 CT 剂量长度乘积（DLP）估算全身有效剂量 - 辐射剂量的间接估计值 - 需要特定解剖结构的转换系数，这些系数不能应用于总 DLP，而是需要基于个体解剖结构的 DLP。存在一个挑战，因为剂量表上报告的总 DLP 通常包括多个单独的检查（例如，胸部 CT 后紧接着进行腹部和盆腔 CT）。此外，单独报告的系列 DLP 可能没有明确或一致的标记。例如，“动脉”可能指的是三肝 CT 的动脉期，或者 CT 血管造影的动脉期。为了解决这个问题，我们设计了一个智能算法来解析多系列 CT 检查的剂量表，并将总 DLP 正确地分解为其解剖结构成分。该算法使用来自部门 PACS 的信息来确定同时进行了多少个不同的 CT 检查。然后，它将不同的访问编号数量与获取的系列进行匹配，并将个体系列 DLP 与适当的 CT 检查进行解剖匹配。该算法允许更准确的剂量分析，但仍然存在自动排序不可行的情况。为了最终改善放射科患者护理，我们必须对系列名称和检查名称进行标准化，以便明确地按解剖结构对检查进行排序，并正确估算全身有效剂量。
