The imaging workup in acute stroke can be simplified by deriving non-contrast CT (NCCT) from CT perfusion (CTP) images. This results in reduced workup time and radiation dose. To achieve this, we present a stacked bidirectional convolutional LSTM (C-LSTM) network to predict 3D volumes from 4D spatiotemporal data. Several parameterizations of the C-LSTM network were trained on a set of 17 CTP-NCCT pairs to learn to derive a NCCT from CTP and were subsequently quantitatively evaluated on a separate cohort of 16 cases. The results show that the C-LSTM network clearly outperforms the baseline and competitive convolutional neural network methods. We show good scalability and performance of the method by continued training and testing on an independent dataset which includes pathology of 80 and 83 CTP-NCCT pairs, respectively. C-LSTM is, therefore, a promising general deep learning approach to learn from high-dimensional spatiotemporal medical images.

中文翻译：

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从时空4D CT派生3D非对比度CT的堆叠双向卷积LSTM。

通过从CT灌注（CTP）图像中获得非对比CT（NCCT），可以简化急性卒中的影像学检查。这导致减少的后处理时间和辐射剂量。为实现此目的，我们提出了一种堆叠式双向卷积LSTM（C-LSTM）网络，以根据4D时空数据预测3D体积。在一组17个CTP-NCCT对上对C-LSTM网络的几个参数化进行了训练，以学习从CTP导出NCCT，随后分别在16个案例中对它们进行了定量评估。结果表明，C-LSTM网络明显优于基线和竞争性卷积神经网络方法。通过在独立的数据集上进行持续的训练和测试，我们展示了该方法的良好可扩展性和性能，该数据集分别包含80和83个CTP-NCCT对的病理。因此，C-LSTM是