Learning Thermal Process Representations for Intraoperative Analysis of Cortical Perfusion During Ischemic Strokes

Abstract

Thermal imaging is a non-invasive and marker-free approach for intraoperative measurements of small temperature variations. In this work, we demonstrate the abilities of active dynamic thermal imaging for analysis of tissue perfusion state in case of cerebral ischemia. For this purpose, a NaCl irrigation is applied to the exposed cortex during hemicraniectomy. The caused temperature changes are measured by a thermal imaging system whilst tissue heating is modeled by a double exponential function. Modeled temperature decay constants allow us to characterize tissue perfusion with respect to its dynamic thermal properties. As intraoperative imaging prevents the usage of computational intense parameter optimization schemes we discuss a deep learning framework that approximates these constants given a simple temperature sequence. The framework is compared to common Levenberg-Marquardt based parameter optimization approaches. The proposed deep parameter approximation framework shows good performance compared to numerical optimization with random initialization. We further validated the approximated parameters by an intraoperative case suffering acute cerebral ischemia. The results indicate that even approximated temperature decay constants allow us to quantify cortical perfusion. Latter yield a standardized representation of cortical thermodynamic properties and might guide further research regarding specific intraoperative therapies and characterization of pathologies with atypical cortical perfusion.