Understanding MRI Drift: Impact on Imaging Quality and Advances in AI-Driven Correction for Clinical Reliability

Magnetic Resonance Imaging is a crucial diagnostic tool known for its high-resolution imaging capabilities, particularly in soft tissue assessment. However, MRI systems are prone to drift, which can compromise imaging quality and reduce diagnostic reliability over time. MRI drift arises from multiple factors, including hardware instabilities, temperature fluctuations, physiological variations, and aging effects on the system. Traditional correction techniques, such as system recalibration and phantom-based approaches, have limitations in maintaining long-term imaging stability. Recent advancements in artificial intelligence-driven correction methods and real-time adaptive algorithms have shown significant potential in mitigating MRI drift by enhancing imaging accuracy and automation. This research provides an in-depth analysis of the causes of MRI drift, existing correction strategies, and emerging AI-based solutions, emphasizing their role in improving MRI reproducibility and clinical applicability. Furthermore, future directions highlight the need for standardized AI frameworks, hybrid correction techniques, and self-calibrating MRI systems to ensure long-term imaging stability and diagnostic precision.