Exploring the Feasibility of AI-Assisted Spine MRI Protocol Optimization Using DICOM Image Metadata

TL;DR

This study demonstrates that AI models trained on DICOM metadata can effectively predict MRI image quality, aiding medical physicists in optimizing protocols and improving diagnostic imaging in clinical settings.

Contribution

The paper validates the use of AI models trained on clinical DICOM metadata for MRI protocol optimization, aligning model insights with MRI theory and practical clinical application.

Findings

AI models achieved F1 scores up to 93% on large datasets.

Model trends matched MRI theoretical expectations.

AI can assist in clinical MRI quality control tasks.

Abstract

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.