Purpose

The accurate and timely assessment of the collateral perfusion status is crucial in the diagnosis and treatment of patients with acute ischemic stroke. Previous works have shown that collateral imaging, derived from CT angiography, MR perfusion, and MR angiography, aids in evaluating the collateral status. However, such methods are time-consuming and/or sub-optimal due to the nature of manual processing and heuristics. Recently, deep learning approaches have shown to be promising for generating collateral imaging. These, however, suffer from the computational complexity and cost.

Methods

In this study, we propose a mobile, lightweight deep regression neural network for collateral imaging in acute ischemic stroke, leveraging dynamic susceptibility contrast MR perfusion (DSC-MRP). Built based upon lightweight convolution and Transformer architectures, the proposed model manages the balance between the model complexity and performance.

Results

We evaluated the performance of the proposed model in generating the five-phase collateral maps, including arterial, capillary, early venous, late venous, and delayed phases, using DSC-MRP from 952 patients. In comparison with various deep learning models, the proposed method was superior to the competitors with similar complexity and was comparable to the competitors of high complexity.

Conclusion

The results suggest that the proposed model is able to facilitate rapid and precise assessment of the collateral status of patients with acute ischemic stroke, leading to improved patient care and outcome.