Congratulations to Dr. Ruogu Fang, assistant professor, whose research titled, STIR-Net: Spatial-Temporal Image Restoration Net for CTPerfusion Radiation Reduction, was recently published in Frontiers in Neurology, section Stroke.
Fang and UF BME researchers, Yao Xiao, Ph.D. student; Peng Liu, Ph.D. student; Yun Liang, Ph.D. student and Skylar Stolte, undergraduate student, propose a deep learning-based Spatial-Temporal Image Restoration Network (aka STIR-Net) to simultaneously perform spatial-temporal denoising and super-resolution for computed tomography perfusion (CTP) images. The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to state-of-the-art methods. The impact of this work is significant to medical imaging safety, especially for patients with stroke and other neurovascular diseases.
CTP imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows visual anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. The accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation and even cancer.