Introduction: Perfusion MR and CT parameters are widely used during the assessment of acute ischemic strokes to evaluate the likely lesion growth. However, such perfusion parameters and derived ratios are sensitive to the choice of arterial input function or deconvolution method. Studies have shown that various deconvolution algorithms can lead to inconsistent values and also introduce distortions that influence measurement. In this work, we propose a deep convolutional neural network (CNN) to learn imaging features from source magnetic resonance perfusion images that are best predictive of tissue outcome.
Methods: We developed a deep CNN for voxel-by-voxel prediction. Perfusion-weighted images (PWIs), diffusion-weighted images (DWIs), and apparent diffusion coefficient (ADC) maps were obtained in five ischemic stroke patients (mean age: 69; mean NIHSS: 12) within 6.5 hours of stroke onset from UCLA stroke center database. The final infarct volumes were evaluated in the 5-7 days post-treatment FLAIR images. 53,000 training data were generated; each training data consisted of the raw voxel values obtained across the MR images. The CNN has three separate convolutional and pooling layers for each imaging channel; 128 combined features were learned in the final fully-connected layer, which were fitted to a logistic classifier for single voxel infarct prediction.
Results: The model achieved an area under the curve (AUC) of 0.941 +/- 0.002 (95% CI) in ten-fold cross validation, outperforming existing models with perfusion parameters. With a cut-off threshold of 0.5, the model achieved an accuracy of 0.865, a precision of 0.836, and a recall of 0.910. The model automatically learned spatio-temporal filters that detected temporal and spatial changes in the PWIs.
Conclusion: This pilot study showed that the deep CNN is capable of generating automatically learned features from source perfusion images that are predictive of tissue outcome, providing a potential alternative for stroke image analysis. Our future work includes experimenting on a larger dataset, and optimizing model parameters with pre-training and fine-tuning.