Episode 39: Artificial Intelligence in MRI

• Define artificial intelligence (AI), machine learning (ML), and deep learning (DL) in the context of medical imaging.
• Describe the fundamental process of training AI models, including data requirements, image annotations, and validation strategies.
• Differentiate between image data and raw data used in AI model development for MRI.
• Explain how AI is applied to MRI acquisition and reconstruction to enable accelerated imaging techniques such as compressed sensing and parallel imaging.
• Discuss how AI improves image quality through noise reduction and artifact correction in MRI.
• Identify the role of AI in enhancing visualization of gadolinium contrast uptake in MR images.

The content of this Artificial Intelligence in MRI program is intended for healthcare professionals who work in the medical imaging environment. These individuals include Radiologists, Radiology Technologists, Radiology Fellows/Residents.

This program has been submitted for 1.00 hours of Category A CE credit as designated by the International Society for MR Radiographers & Technologists (ISMRT) RCEEM.

Release Date: 06/06/2026 | Expires: 06/06/2027

Participants may claim only one type of credit for this educational activity.

Faculty and Planner Disclosures:
As an accredited provider of continuing medical education, Northwest Imaging Forums, Inc. (NWIF) ensures balance, independence, objectivity, and scientific rigor in all its educational activities. In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, all faculty and planners are required to disclose all financial relationships with ineligible companies within the last 24 months. All relevant financial relationships have been mitigated by NWIF to ensure the integrity of the CME activity. The planner has nothing to disclose.