Purpose

Oxygen extraction fraction (OEF) is a biomarker for the viability of brain tissue in ischemic stroke. However, acquisition of the OEF map using positron emission tomography (PET) with oxygen-15 gas is uncomfortable for patients because of the long fixation time, invasive arterial sampling, and radiation exposure. We aimed to predict the OEF map from magnetic resonance (MR) and PET images using a deep convolutional neural network (CNN) and to demonstrate which PET and MR images are optimal as inputs for the prediction of OEF maps.

Methods

Cerebral blood flow at rest (CBF) and during stress (sCBF), cerebral blood volume (CBV) maps acquired from oxygen-15 PET, and routine MR images (T1-, T2-, and T2*-weighted images) for 113 patients with steno-occlusive disease were learned with U-Net. MR and PET images acquired from the other 25 patients were used as test data. We compared the predicted OEF maps and intraclass correlation (ICC) with the real OEF values among combinations of MRI, CBF, CBV, and sCBF.

Results

Among the combinations of input images, OEF maps predicted by the model learned with MRI, CBF, CBV, and sCBF maps were the most similar to the real OEF maps (ICC: 0.597 ± 0.082). However, the contrast of predicted OEF maps was lower than that of real OEF maps.

Conclusion

These results suggest that the deep CNN learned useful features from CBF, sCBF, CBV, and MR images and predict qualitatively realistic OEF maps. These findings suggest that the deep CNN model can shorten the fixation time for ¹⁵O PET by skipping ¹⁵O₂ scans. Further training with a larger data set is required to predict accurate OEF maps quantitatively.

中文翻译：

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通过卷积神经网络预测氧气提取分数图：验证 MR 和 PET 图像之间的输入数据

目的

氧提取分数 (OEF) 是缺血性卒中脑组织活力的生物标志物。然而，由于固定时间长、侵入性动脉采样和辐射暴露，使用正电子发射断层扫描 (PET) 和氧气 15 气体获取 OEF 图对患者来说是不舒服的。我们旨在使用深度卷积神经网络 (CNN) 从磁共振 (MR) 和 PET 图像中预测 OEF 图，并证明哪些 PET 和 MR 图像最适合作为 OEF 图预测的输入。

方法

113 名患者的静息 (CBF) 和压力期间 (sCBF) 脑血流量、从氧 15 PET 获取的脑血容量 (CBV) 图和常规 MR 图像（T1、T2 和 T2* 加权图像）通过 U-Net 学习了狭窄闭塞性疾病。从其他 25 名患者获得的 MR 和 PET 图像用作测试数据。我们将预测的 OEF 图和组内相关性 (ICC) 与 MRI、CBF、CBV 和 sCBF 组合中的实际 OEF 值进行了比较。

结果

在输入图像的组合中，使用 MRI、CBF、CBV 和 sCBF 图学习的模型预测的 OEF 图与真实的 OEF 图最相似（ICC：0.597 ± 0.082）。然而，预测的 OEF 图的对比度低于真实的 OEF 图。

结论

这些结果表明，深度 CNN 从 CBF、sCBF、CBV 和 MR 图像中学习了有用的特征，并预测了定性逼真的 OEF 图。这些发现表明，深度 CNN 模型可以通过跳过¹⁵ O ₂扫描来缩短¹⁵ O PET的固定时间。需要使用更大的数据集进行进一步的训练才能定量预测准确的 OEF 图。