From the Department of Radiology.
J Comput Assist Tomogr. 2021;45(6):870-876. doi: 10.1097/RCT.0000000000001215.
Poor contrast enhancement is related to issues with examination execution, contrast prescription, computed tomography (CT) protocols, and patient conditions. Currently, our community has no metric to monitor true enhancement on routine single-phase examinations because this requires knowledge of both pre- and postcontrast CT number.
We propose an automatable solution to quantifying contrast enhancement without requiring a dedicated noncontrast series.
The difference in CT number between a target region in an enhanced and unenhanced image defines the metric "quantification of iodine contrast enhancement" (Q-ICE). Quantification of iodine contrast enhancement uses the noncontrast bolus tracking baseline image from routine abdominal examinations, which mitigates the need for a dedicated noncontrast series. We applied this method retrospectively to 312 patient livers from 2 sites between 2017 and 2020. Each site used a weight-based contrast injection protocol for weights 60 to 113 kg and a constant volume less than 60 kg and greater than 113 kg. Hypothesis testing was performed to compare Q-ICE between sites and detect Q-ICE dependence on weight and kilovoltage (kV).
Mean Q-ICE differed between sites (P = 0.004) by 4.96 Hounsfield unit with 95% confidence interval (1.63-8.28), albeit this difference was roughly 2 times smaller than the SD in Q-ICE across patients at a single site. For patients between 60 and 113 kg, we did not observe evidence of Q-ICE varying with patient weight (P = 0.920 and 0.064 for 120 and 140 kV, respectively). The Q-ICE did vary with patient weight for patients less than 60 kg (P = 0.003) and greater than 113 kg (P = 0.04). We observed a roughly 10 Hounsfield unit reduction in Q-ICE liver for patients scanned with 140 versus 120 kV. We observed several underenhancing examinations with an arterial phase appearance motivating our CT protocol optimization team to consider increasing the delay for slowly enhancing patients.
A quality metric for quantifying CT contrast enhancement was developed and suggested tangible opportunities for quality improvement and potential financial savings.
较差的对比增强与检查执行、对比剂处方、计算机断层扫描 (CT) 方案和患者状况有关。目前，我们的社区没有监测常规单相检查中真正增强的指标，因为这需要了解增强前和增强后的 CT 数。
我们提出了一种自动量化对比增强的解决方案，无需专门的非对比系列。
增强和未增强图像中目标区域的 CT 数差值定义了“碘对比增强量化”(Q-ICE) 度量。碘对比增强的量化使用来自常规腹部检查的非对比剂团注跟踪基线图像，从而减少了对专门的非对比系列的需求。我们在 2017 年至 2020 年间，将该方法应用于来自两个地点的 312 例患者肝脏。每个地点都为体重 60 至 113 公斤的患者使用基于体重的对比剂注射方案，为体重小于 60 公斤和大于 113 公斤的患者使用固定体积。进行假设检验以比较站点之间的 Q-ICE，并检测 Q-ICE 是否依赖于体重和千伏 (kV)。
站点之间的平均 Q-ICE 存在差异（P=0.004），差值为 4.96 个亨氏单位，置信区间为 1.63-8.28，尽管这一差异大约是单个站点内患者 Q-ICE 标准差的两倍。对于体重 60 至 113 公斤的患者，我们没有观察到 Q-ICE 随患者体重变化的证据（对于 120 和 140 kV，分别为 P=0.920 和 0.064）。体重小于 60 公斤（P=0.003）和大于 113 公斤（P=0.04）的患者 Q-ICE 确实随体重而变化。我们观察到使用 140 千伏与 120 千伏扫描时，肝脏 Q-ICE 降低了约 10 个亨氏单位。我们观察到一些动脉期表现的增强不足的检查，这促使我们的 CT 协议优化团队考虑增加对增强缓慢的患者的延迟。
开发了一种用于量化 CT 对比增强的质量指标，并提出了切实可行的质量改进和潜在节省资金的机会。
