RPI ID: 2021-007-201
Innovation Summary: A hybrid image reconstruction system combines model-based and data-driven techniques to improve image quality across modalities. The architecture integrates physics-informed priors with deep learning algorithms to reduce noise and artifacts. It supports real-time processing and is adaptable to CT, MRI, and PET imaging. The system enhances resolution and diagnostic reliability.
Challenges / Opportunities: Traditional reconstruction methods struggle with balancing accuracy and computational efficiency. This invention merges analytical and AI-based approaches to overcome those limitations. It opens opportunities for faster, more accurate imaging in clinical and research settings. The system is compatible with existing hardware and software platforms.
Key Benefits / Advantages: ✔ Hybrid reconstruction architecture ✔ Reduced noise and artifacts ✔ Real-time processing ✔ Multi-modality compatibility ✔ Improved diagnostic reliability
Applications: • Medical imaging • Radiology • Research imaging
Keywords: #imagereconstruction #hybridAI #medicalimaging #deeplearning #radiology #diagnostics
Intellectual Property: US Application 17/393922 US12387392B2 filed 04-Aug-2021