Researchers developed a physics-based machine learning imaging system that enhances visibility through complex media, holding promise for biomedical imaging.
Key Details
- 1Team from UCLA and University of Rochester improved a light-scattering imaging system using AI (DeepTimeGate).
- 2The new method more than doubled the signal-to-noise ratio versus previous technologies.
- 3Creates images in real time (thousandths of a second) using standard silicon-based cameras.
- 4System reconstructed images with fewer artifacts and broader field of view.
- 5Potential biomedical uses include guiding surgeries and analyzing cloudy blood samples without dilution.
- 6Published in Nature Light: Science & Applications; funded by ONR, NSF, DOE.
Why It Matters
Real-time, affordable imaging through complex media can enable safer, more accessible biomedical diagnostics and surgical guidance using widely available camera hardware. This intersection of physics-based AI and radiology opens new frontiers in tissue imaging and laboratory workflows.
Source
EurekAlert
Related News
•EurekAlert
AI-Guided Handheld Cardiac Ultrasound Reduces Referrals and Costs in Spain
AI-guided handheld cardiac ultrasound enables primary care physicians to detect heart failure, reducing specialist referrals and saving costs.
•EurekAlert
AI Tool Predicts Which Rectal Cancer Patients Benefit from Intensive Therapy
UCL researchers developed an AI that analyzes biopsy slides to identify rectal cancer patients who benefit from adding irinotecan to standard chemoradiotherapy.
•EurekAlert
AI Model Accelerates Accurate Dose Prediction for CyberKnife Radiotherapy
A physics- and spatially informed diffusion model accelerates Monte Carlo–level dose estimation for CyberKnife radiotherapy using planning CT and anatomical data.