Exploring the Feasibility of AI-Assisted Spine MRI Protocol Optimization Using DICOM Image Metadata
Journal:
arXiv
Published Date:
Feb 4, 2025
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.
