3D mobile regression vision transformer for collateral imaging in acute ischemic stroke

Sumin Jung, Hyun Yang, Hyun Jeong Kim, Hong Gee Roh, Jin Tae Kwak

Abstract: 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.