Zhu Guangming, Jiang Bin, Chen Hui, Heit Jeremy J, Etter Micah, Hishaw G Alex, Faizy Tobias D, Steinberg Gary, Wintermark Max
Department of Neurology, University of Arizona, Tucson, AZ, USA.
Department of Radiology, Neuroradiology Section, Stanford University, Stanford, CA, USA.
Neuroradiology. 2025 Apr 4. doi: 10.1007/s00234-025-03605-1.
Cerebrovascular reactivity (CVR) assesses vascular health in various brain conditions, but CVR measurement requires a challenge to cerebral perfusion such as the administration of acetazolamide(ACZ), thus limiting widespread use. We determined whether generative adversarial networks (GANs) can create CVR images from baseline pre-ACZ arterial spin labeling (ASL) MRI.
This study included 203 Moyamoya cases with a total of 3248 pre- and post-ACZ ASL Cerebral Blood Flow (CBF) images. Reference CVRs were generated from these CBF slices. From this set, 2640 slices were used to train a Pixel-to-Pixel GAN consisting of a generator and discriminator network, with the remaining 608 slices reserved as a testing set. Following training, the pre-ACZ CBF in the testing set was introduced to the trained model to generate synthesized CVR. The quality of the synthesized CVR was evaluated with structural similarity index(SSI), spatial correlation coefficient(SCC), and the root mean squared error(RMSE), compared with reference CVR. The segmentations of the low CVR regions were compared using the Dice similarity coefficient (DSC). Reference and synthesized CVRs in single-slice and individual-hemisphere settings were reviewed to assess CVR status, with Cohen's Kappa measuring consistency.
The mean SSIs of the CVR of training and testing sets were 0.943 ± 0.019 and 0.943 ± 0.020. The mean SCCs of the CVR of training and testing sets were 0.988 ± 0.009 and 0.987 ± 0.011. The mean RMSEs of the CVR are 0.077 ± 0.015 and 0.079 ± 0.018. Mean DSC of low CVR area of testing sets was 0.593 ± 0.128. Visual interpretation yielded Cohen's Kappa values of 0.896 and 0.813 for the training and testing sets in the single-slice setting, and 0.781 and 0.730 in the individual-hemisphere setting.
Synthesized CVR by GANs from baseline ASL without challenge may be a useful alternative in detecting vascular deficits in clinical applications when ACZ challenge is not feasible.
脑血管反应性（CVR）可评估各种脑部疾病中的血管健康状况，但CVR测量需要对脑灌注进行刺激，如给予乙酰唑胺（ACZ），因此限制了其广泛应用。我们确定了生成对抗网络（GAN）是否可以从ACZ前的基线动脉自旋标记（ASL）MRI创建CVR图像。
本研究纳入了203例烟雾病病例，共有3248张ACZ前后的ASL脑血流量（CBF）图像。从这些CBF切片生成参考CVR。从该数据集中，2640张切片用于训练由生成器和判别器网络组成的像素到像素GAN，其余608张切片留作测试集。训练后，将测试集中ACZ前的CBF引入训练模型以生成合成CVR。将合成CVR的质量与参考CVR进行比较，用结构相似性指数（SSI）、空间相关系数（SCC）和均方根误差（RMSE）进行评估。使用Dice相似系数（DSC）比较低CVR区域的分割。在单切片和单半球设置下审查参考CVR和合成CVR，以评估CVR状态，用Cohen's Kappa测量一致性。
训练集和测试集CVR的平均SSI分别为0.943±0.019和0.943±0.020。训练集和测试集CVR的平均SCC分别为0.988±0.009和0.987±0.011。CVR的平均RMSE分别为0.077±0.015和0.079±0.018。测试集低CVR区域的平均DSC为0.593±0.128。在单切片设置下，视觉解释得出训练集和测试集的Cohen's Kappa值分别为0.896和0.813，在单半球设置下分别为0.781和0.730。
当ACZ刺激不可行时，GAN从无刺激的基线ASL合成CVR可能是临床应用中检测血管缺陷的一种有用替代方法。
