Leveraging AI to Enhance Image Quality and Workflow

Presentation Description / Summary: Magnetic resonance imaging (MRI) is the cornerstone of neurologic diagnosis, yet for decades its performance has been defined by hardware specifications—field strength, gradient performance, coil design—and the expertise of the operator. With the integration of artificial intelligence (AI), that paradigm is rapidly shifting. This session will introduce the core principles of how AI functions in image reconstruction and optimization, translating complex computational methods into accessible concepts for clinicians. Attendees will learn how AI-driven approaches are revitalizing older hardware platforms, allowing systems with lower field strengths or less powerful gradients to generate images of diagnostic quality that previously required more advanced technology. By minimizing technical struggles, suppressing artifacts, and reducing scan times, AI not only extends the lifespan and utility of existing equipment but also improves efficiency and patient safety. The session will also examine the evolving landscape of AI applications, highlighting the critical differences between proprietary solutions tied to specific vendors and platform-agnostic tools that can be applied across hardware types. Understanding these distinctions is essential as veterinary MRI moves into an era where access, cost, and performance are increasingly mediated by software intelligence rather than raw magnet strength. Through practical examples and evidence-based discussion, participants will leave with a clear understanding of how AI is reshaping the diagnostic imaging workflow, why it matters for the future of veterinary neurology, and how to apply this knowledge to optimize imaging in their own practices.