Back to all news
Deep Learning Model Enables 3D Handheld Photoacoustic-Ultrasound Imaging Without Sensors
Pusan National University develops MoGLo-Net, an AI model that reconstructs 3D images from handheld 2D photoacoustic and ultrasound scans without external sensors.
Key Details
- 1MoGLo-Net uses deep learning to track handheld ultrasound transducer motion from tissue speckle data, eliminating need for external tracking hardware.
- 2Combines ResNet-based encoder and LSTM-based motion estimator for accurate motion tracking and 3D reconstruction.
- 3Validated using both proprietary and public datasets, outperforming state-of-the-art methods on all metrics.
- 4Successfully achieved 3D blood vessel reconstructions from combined ultrasound and photoacoustic data.
- 5Published June 13, 2025, in IEEE Transactions on Medical Imaging (DOI: 10.1109/TMI.2025.3579454).
- 6Innovation aims to make advanced 3D imaging safer, more accurate, and accessible without costly hardware.
Why It Matters
MoGLo-Net's ability to reconstruct 3D volumes from standard handheld imaging without external sensors could democratize high-end ultrasound and photoacoustic imaging, especially important in resource-limited settings. This advance may enable more effective diagnosis and real-time imaging guidance, expanding access to advanced imaging technologies while reducing costs and complexity of equipment.
Source
EurekAlert
Related News
•EurekAlert
MD Anderson Unveils New AI Genomics Insights and Therapeutic Advances
MD Anderson reports breakthroughs in cancer therapeutics and provides critical insights into AI models for genomic analysis.
•EurekAlert
UCLA Researchers Present AI, Blood Biomarker Advances at SABCS 2025
UCLA Health researchers unveil major advances in breast cancer AI pathology, liquid biopsy, and biomarker strategies at the 2025 SABCS.
•EurekAlert
SH17 Dataset Boosts AI Detection of PPE for Worker Safety
University of Windsor researchers released SH17, a 8,099-image open dataset for AI-driven detection of personal protective equipment (PPE) in manufacturing settings.