Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis

Abstract

State-of-the-art concepts in the field of computer assisted medical interventions are typically based on registering pre-operative imaging data to the patient. While this approach has many relevant clinical applications, it suffers from one core bottleneck: it cannot account for tissue dynamics because it works with “offline” data. To overcome this issue, we propose a new approach to surgical imaging that combines the power of multispectral imaging with the speed and robustness of deep learning based image analysis. Core innovation is an end-to-end deep learning architecture that integrates all preprocessing steps as well as the actual regression task in a single network. According to a quantitative in silico validation, our approach is well-suited for solving the inverse problem of relating multispectral image pixels to underlying functional tissue properties in real time. A porcine study further suggests that our method is capable of monitoring haemodynamic changes in vivo. Deep learning based multispectral imaging could thus become a valuable tool for imaging tissue dynamics.