Exploring the Feasibility of AI-Assisted Spine MRI Protocol Optimization Using DICOM Image Metadata
It addresses the challenge of variability in MRI equipment and protocols for medical physicists in clinical settings, offering a tool for quality control, but it is incremental as it applies existing AI methods to a specific domain.
This study tackled the problem of optimizing MRI spine protocols by using AI models trained on DICOM metadata to classify image quality as good or bad, achieving F1 scores from 77% to 93% on datasets with 292 or more instances.
Artificial intelligence (AI) is increasingly being utilized to optimize magnetic resonance imaging (MRI) protocols. Given that image details are critical for diagnostic accuracy, optimizing MRI acquisition protocols is essential for enhancing image quality. While medical physicists are responsible for this optimization, the variability in equipment usage and the wide range of MRI protocols in clinical settings pose significant challenges. This study aims to validate the application of AI in optimizing MRI protocols using dynamic data from clinical practice, specifically DICOM metadata. To achieve this, four MRI spine exam databases were created, with the target attribute being the binary classification of image quality (good or bad). Five AI models were trained to identify trends in acquisition parameters that influence image quality, grounded in MRI theory. These trends were analyzed using SHAP graphs. The models achieved F1 performance ranging from 77% to 93% for datasets containing 292 or more instances, with the observed trends aligning with MRI theory. The models effectively reflected the practical realities of clinical MRI settings, offering a valuable tool for medical physicists in quality control tasks. In conclusion, AI has demonstrated its potential to optimize MRI protocols, supporting medical physicists in improving image quality and enhancing the efficiency of quality control in clinical practice.