Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke.

PURPOSE: 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.

METHODS

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).

RESULTS

The proposed network was able to predict the five phase maps with high accuracy. On average, 0.897 R-squared, 0.581 × 10-1 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.

CONCLUSION

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.