Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea.
Daejeon St. Mary's Hospital, Catholic University, Daejeon, 34943, South Korea.
Int J Comput Assist Radiol Surg. 2020 Jan;15(1):151-162. doi: 10.1007/s11548-019-02060-7. Epub 2019 Sep 3.
Acute ischemic stroke is one of the primary causes of death worldwide. Recent studies have shown that the assessment of collateral status could aid in improving the treatment for patients with acute ischemic stroke. We present a 3D deep regression neural network to automatically generate the collateral images from dynamic susceptibility contrast-enhanced magnetic resonance perfusion (DSC-MRP) in acute ischemic stroke.
This retrospective study includes 144 subjects with acute ischemic stroke (stroke cases) and 201 subjects without acute ischemic stroke (controls). DSC-MRP images of these subjects were manually inspected for collateral assessment in arterial, capillary, early and late venous, and delay phases. The proposed network was trained on 205 subjects, and the optimal model was chosen using the validation set of 64 subjects. The predictive power of the network was assessed on the test set of 76 subjects using the squared correlation coefficient (R-squared), mean absolute error (MAE), Tanimoto measure (TM), and structural similarity index (SSIM).
The proposed network was able to predict the five phase maps with high accuracy. On average, 0.897 R-squared, 0.581 × 10 MAE, 0.946 TM, and 0.846 SSIM were achieved for the five phase maps. No statistically significant difference was, in general, found between controls and stroke cases. The performance of the proposed network was lower in the arterial and venous phases than the other three phases.
The results suggested that the proposed network performs equally well for both control and acute ischemic stroke groups. The proposed network could help automate the assessment of collateral status in an efficient and effective manner and improve the quality and yield of diagnosis of acute ischemic stroke. The follow-up study will entail the clinical evaluation of the collateral images that are generated by the proposed network.
急性缺血性脑卒中是全球主要的死亡原因之一。最近的研究表明，侧支循环状态的评估可以帮助改善急性缺血性脑卒中患者的治疗效果。我们提出了一种 3D 深度回归神经网络，用于自动从动态磁敏感对比磁共振灌注（DSC-MRP）中生成侧支循环图像。
本回顾性研究共纳入 144 例急性缺血性脑卒中患者（脑卒中病例）和 201 例无急性缺血性脑卒中患者（对照组）。对这些患者的 DSC-MRP 图像进行手动检查，以评估动脉、毛细血管、早期和晚期静脉以及延迟期的侧支循环情况。该网络在 205 例患者中进行了训练，并使用 64 例患者的验证集选择了最佳模型。使用 76 例患者的测试集评估了网络的预测能力，使用平方相关系数（R-squared）、平均绝对误差（MAE）、Tanimoto 度量（TM）和结构相似性指数（SSIM）进行评估。
该网络能够准确预测五个时相图。平均而言，五个时相图的 R-squared 为 0.897，MAE 为 0.581×10，TM 为 0.946，SSIM 为 0.846。一般来说，对照组和脑卒中病例组之间没有统计学上的显著差异。该网络在动脉期和静脉期的性能低于其他三个时相。
研究结果表明，该网络对对照组和急性缺血性脑卒中组的表现相同。该网络可以帮助高效、有效地自动评估侧支循环状态，并提高急性缺血性脑卒中诊断的质量和效率。后续的研究将对该网络生成的侧支循环图像进行临床评估。
