Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Building 62, RM 4126, Silver Spring, MD.
Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Building 62, RM 4126, Silver Spring, MD.
Acad Radiol. 2019 Jul;26(7):937-948. doi: 10.1016/j.acra.2018.09.006. Epub 2018 Oct 3.
The quantitative assessment of volumetric CT for discriminating small changes in nodule size has been under-examined. This phantom study examined the effect of imaging protocol, nodule size, and measurement method on volume-based change discrimination across low and high object to background contrast tasks.
Eight spherical objects ranging in diameter from 5.0 mm to 5.75 mm and 8.0 mm to 8.75 mm with 0.25 mm increments were scanned within an anthropomorphic phantom with either foam-background (high-contrast task, ∼1000 HU object to background difference)) or gelatin-background (low-contrast task, ∼50 to 100 HU difference). Ten repeat acquisitions were collected for each protocol with varying exposures, reconstructed slice thicknesses and reconstruction kernels. Volume measurements were obtained using a matched-filter approach (MF) and a publicly available 3D segmentation-based tool (SB). Discrimination of nodule sizes was assessed using the area under the ROC curve (AUC).
Using a low-dose (1.3 mGy), thin-slice (≤1.5 mm) protocol, changes of 0.25 mm in diameter were detected with AU = 1.0 for all baseline sizes for the high-contrast task regardless of measurement method. For the more challenging low-contrast task and same protocol, MF detected changes of 0.25 mm from baseline sizes ≥5.25 mm and volume changes ≥9.4% with AUC≥0.81 whereas corresponding results for SB were poor (AUC within 0.49-0.60). Performance for SB was improved, but still inconsistent, when exposure was increased to 4.4 mGy.
The reliable discrimination of small changes in pulmonary nodule size with low-dose, thin-slice CT protocols suitable for lung cancer screening was dependent on the inter-related effects of nodule to background contrast and measurement method.
定量评估 CT 容积在鉴别结节大小的微小变化方面的应用尚未得到充分研究。这项体模研究考察了成像方案、结节大小和测量方法对低对比和高对比任务中基于体积的变化鉴别能力的影响。
在一个具有泡沫背景（高对比任务，对象与背景差异约为 1000 HU）或明胶背景（低对比任务，差异约为 50 至 100 HU）的人体模体内，扫描了 8 个直径从 5.0 毫米到 5.75 毫米以及 8.0 毫米到 8.75 毫米、每 0.25 毫米递增的球形物体。对于每个方案，使用不同的曝光、重建层厚和重建核，采集了 10 次重复采集。使用匹配滤波器方法（MF）和公开的 3D 分割工具（SB）进行体积测量。使用 ROC 曲线下面积（AUC）评估结节大小的鉴别能力。
使用低剂量（1.3 mGy）、薄层（≤1.5 毫米）方案，对于高对比任务，所有基线大小的结节直径变化 0.25 毫米都可检测到，无论使用哪种测量方法，AUC 均为 1.0。对于更具挑战性的低对比任务和相同的方案，MF 可以检测到基线大小≥5.25 毫米的 0.25 毫米直径变化和体积变化≥9.4%，AUC≥0.81，而 SB 的相应结果较差（AUC 在 0.49-0.60 之间）。当曝光增加到 4.4 mGy 时，SB 的性能有所提高，但仍不一致。
在适合肺癌筛查的低剂量、薄层 CT 方案中，可靠地鉴别肺结节大小的微小变化取决于结节与背景对比度和测量方法的相互影响。
