Department of Radiology, The Affiliated Hospital of Hebei University, China.
Medical school, Hebei University, China.
J Xray Sci Technol. 2019;27(1):97-110. doi: 10.3233/XST-180443.
To assess the difference in absorbed organ dose and image quality for head-neck CT angiography using organ dose modulation compared with 3D smart mA modulation in different body mass indices (BMIs) using an adaptive statistical iterative reconstruction (ASiR-V) algorithm.
Three hundred patients underwent head-neck CTA were equally divided into three groups: A (18.5 kg/m2≦BMI < 24.9 kg/m2), B (24.9 kg/m2≦BMI < 29.9 kg/m2) and C (29.9 kg/m2≦BMI≦34.9 kg/m2). The groups were randomly subdivided into two subgroups (n = 50): A1-A2, B1-B2 and C1-C2. The patients in subgroups A1, B1 and C1 underwent organ dose modulation with the ASiR-V algorithm, while other patients underwent 3D smart mA modulation. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of all head-neck CT angiography images were calculated. Images were then subjectively evaluated. Mean values of several indices including dose-length product (DLP) were computed. The DLP was converted to the effective dose (ED). SNR, CNR and ED in groups A, B, and C were compared in statistical data analysis.
SNR, CNR, and subjective image scores show no statistical differences in three groups (P > 0.05). However, there is significant difference of ED values (P < 0.05) . For example, in subgroup A1 mean ED values are 15.30% and 23.66% lower than those in subgroup A2 at thyroid gland and eye lens, respectively. Similar patterns also exist in groups B (B1 vs. B2) and C (C1 vs. C2).
Using organ dose modulation and applying the ASiR-V algorithm can more effectively reduce the radiation dose in head-neck CT angiography than using 3D smart mA modulation, while maintaining image quality. Thus, using organ-based dose modulation has the additional benefit of reducing dose to the thyroid gland and eye lens.
使用基于器官的剂量调制和自适应统计迭代重建（ASiR-V）算法，评估在不同身体质量指数（BMI）下，与 3D 智能 mA 调制相比，头部颈部 CT 血管造影的吸收器官剂量和图像质量的差异。
300 名接受头颈部 CTA 的患者被平均分为三组：A 组（18.5kg/m2≦BMI ＜24.9kg/m2）、B 组（24.9kg/m2≦BMI ＜29.9kg/m2）和 C 组（29.9kg/m2≦BMI≦34.9kg/m2）。这些组又被随机分为两组（n=50）：A1-A2、B1-B2 和 C1-C2。A1、B1 和 C1 组的患者使用 ASiR-V 算法进行器官剂量调制，而其他患者则使用 3D 智能 mA 调制。计算所有头颈部 CT 血管造影图像的信噪比（SNR）和对比噪声比（CNR）。然后对图像进行主观评估。计算剂量长度乘积（DLP）等多个指标的平均值。将 DLP 转换为有效剂量（ED）。对 A、B 和 C 组的 SNR、CNR 和 ED 进行统计数据分析。
三组的 SNR、CNR 和主观图像评分无统计学差异（P ＞0.05）。但是 ED 值存在显著差异（P ＜0.05）。例如，在 A1 亚组中，甲状腺和晶状体的 ED 值分别比 A2 亚组低 15.30%和 23.66%。在 B 组（B1 与 B2）和 C 组（C1 与 C2）中也存在类似的模式。
与使用 3D 智能 mA 调制相比，使用器官剂量调制并应用 ASiR-V 算法可以更有效地降低头颈部 CT 血管造影的辐射剂量，同时保持图像质量。因此，使用基于器官的剂量调制还有降低甲状腺和晶状体剂量的额外好处。
