Deep Learning-Driven Computational Imaging for Noninvasive Monitoring System of Brain Temperature and Metabolism: A Hypothermia Validation for Acute Ischemic Stroke.
Authors
Affiliations (9)
Affiliations (9)
- School of Information Science and Technology Beijing University of Technology Beijing China.
- Beijing Institute of Brain Disorders Capital Medical University Beijing China.
- China-America Institute of Neuroscience and Beijing Institute of Geriatrics Xuanwu Hospital Capital Medical University Beijing China.
- Department of Neurology and Neurosurgery, Xuanwu Hospital Capital Medical University Beijing China.
- School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China.
- School of Engineering Medicine Beihang University Beijing China.
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation Chinese Academy of Sciences Beijing China.
- Tianjin Huanhu Hospital Tianjin University Tianjin China.
- Graduate School of Engineering Maebashi Institute of Technology Maebashi Japan.
Abstract
Brain temperature (BT) is a critical physiological indicator closely associated with neurological function and disease progression. However, real-time, noninvasive monitoring of BT remains a challenge due to the limitations of current technologies. Here, we present a novel multimodal framework combining bioheat transfer modeling, deep learning, and computational thermography for accurate BT prediction and imaging. A one-dimensional convolutional neural network was trained on multimodal clinical data, integrating cerebral blood flow, tissue oxygen saturation, and intracranial pressure, achieving a mean absolute error of 0.31°C in BT prediction. The framework incorporates finite element analysis to generate 3D thermographic maps of brain tissue with a spatial resolution of 0.4 mm, validated using MRI-derived data. This approach demonstrated robust performance in predicting localized temperature variations in acute ischemic stroke patients undergoing therapeutic hypothermia, with deviations below 0.45°C. Our findings highlight the potential of this system to enable precise BT monitoring, bridging the gap between computational modeling and clinical neuro-thermometry, and paving the way for advanced diagnostic and therapeutic interventions.