Kang Seong-Hyeon, Kim Kyuseok, Shim Jina, Lee Youngjin
Department of Radiological Science, Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon, 21936, Republic of Korea.
Institute of Human Convergence Health Science, Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon, 21936, Republic of Korea.
Sci Rep. 2025 Apr 17;15(1):13281. doi: 10.1038/s41598-025-98098-6.
Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i.e., vertebrae and skull). However, sCTA is not considered mainstream because of the high radiation dose generated by the two-scan protocol. We aimed to solve the overexposure problem by training a U-Net-based CA segmentation model using a low-dose computed tomographic angiography (CTA) image-based dataset with various pre-processing methods to achieve a performance similar to that of sCTA. We optimized a non-local means (NLM) algorithm using the coefficient of variation and contrast-to-noise ratio. In addition, datasets were constructed by predicting the CA mask using a semiautomatic thresholding technique based on region growing method. Then, CTA images of 35 (2052 slices), 4 (248 slices), and 5 patients (594 slices) were used, respectively, for the train, validation, and test sets. To evaluate the performance of the U-Net-based CA segmentation model quantitatively according to the constructed dataset, the average precision (AP), intersection over union (IoU), and F1-score were calculated. For the dataset to which both the optimized NLM algorithm and semiautomatic thresholding technique were applied, the segmentation model showed the most improved performance. In particular, the quantitative evaluation of the low-dose CTA image with the NLM algorithm and the semiautomatic thresholding-based U-Net model calculated AP, IoU, and F1-scores of approximately 0.880, 0.955, and 0.809, respectively, which were most similar to the CA segmentation performance of the sCTA technique. The proposed U-Net model provided CA segmentation results without additional radiation exposure. In addition, the selection and optimization of an appropriate pre-processing methods were identified as essential for achieving higher segmentation performance for the U-Net model.
减法计算机断层血管造影（sCTA）能够有效地将强化的脑动脉与信号强度和位置相近的结构（即椎骨和颅骨）区分开来。然而，由于双扫描方案产生的高辐射剂量，sCTA并不被视为主流技术。我们旨在通过使用基于低剂量计算机断层血管造影（CTA）图像的数据集，并采用各种预处理方法训练基于U-Net的脑动脉（CA）分割模型，以达到与sCTA相似的性能，从而解决过度曝光问题。我们使用变异系数和对比噪声比优化了非局部均值（NLM）算法。此外，基于区域生长法的半自动阈值技术预测CA掩码来构建数据集。然后，分别使用35例患者（2052层）、4例患者（248层）和5例患者（594层）的CTA图像作为训练集、验证集和测试集。为了根据构建的数据集定量评估基于U-Net的CA分割模型的性能，计算了平均精度（AP）、交并比（IoU）和F1分数。对于同时应用了优化的NLM算法和半自动阈值技术的数据集，分割模型表现出最大的性能提升。特别是，使用NLM算法和基于半自动阈值的U-Net模型对低剂量CTA图像进行定量评估时，计算得到的AP、IoU和F1分数分别约为0.880、0.955和0.809，与sCTA技术的CA分割性能最为相似。所提出的U-Net模型无需额外的辐射暴露即可提供CA分割结果。此外，确定选择和优化合适的预处理方法对于U-Net模型实现更高的分割性能至关重要。
