CCA4CTA: A Hybrid Attention Mechanism based Convolutional Network for Analysing Collateral Circulation via Multi-phase Cranial CTA

Abstract

The degree of establishment of cerebrovascular collateral circulation is closely related to the prognosis of patients with acute ischemic stroke, but the evaluation of collateral circulation requires high professional experience of physicians because of the complex structure of the cerebral vessels themselves, and the variety of scoring criteria resulting in poor consistency of results between physicians. Therefore, the use of computer-aided diagnostic techniques to evaluate the establishment of collateral circulation in patients with ischemic stroke is of great clinical importance. In this paper, we proposed a novel method for automatic scoring of collateral circulation via multiphase cranial CTA (computed tomography angiography) to assist physicians in diagnosis. We compared with existing mainstream classification n etworks, our method is able to achieve 90.43% accuracy. Further, the effectiveness of the method was further validated by ablation experiments. However, the multi-phase Cranial CTA collateral circulation scoring algorithm based on a feature fusion network with the hybrid attention mechanism effectively improves the efficiency of prognostic judgment, avoids the limitations of manual extraction of image features in the traditional ways, and plays an auxiliary role in diagnosis for physicians in clinical practice, which is useful for guiding the decision of clinical syndromes in lateral branch circulation stroke.