RPI ID: 2020-029-201

Innovation Summary:
A deep learning-based system is developed to reduce motion artifacts in dynamic imaging sequences. The model uses temporal and spatial features to identify and correct distortions caused by patient movement. It is applicable to modalities such as MRI and CT, where motion can compromise diagnostic accuracy. The method supports integration into real-time imaging workflows.

Challenges / Opportunities:
Motion artifacts are a persistent challenge in dynamic imaging, especially in uncooperative or pediatric patients. This invention offers an automated correction mechanism that improves image clarity without requiring repeat scans. It opens opportunities for faster imaging protocols and reduced patient burden. The system enhances diagnostic confidence across clinical settings.

Key Benefits / Advantages:
✔ Reduces motion artifacts
✔ Enhances image clarity
✔ Supports real-time correction
✔ Compatible with MRI and CT
✔ Improves diagnostic reliability

Applications:
• MRI
• CT
• Pediatric imaging

Keywords:
#motioncorrection #deeplearning #medicalimaging #dynamicimaging #MRI #CT

Intellectual Property:
US Patent Application , US20220292641A1, 17/796715, filed 01-Aug-2022