Classifying MRI motion severity using a stacked ensemble approach.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Radiology

Background:

• Motion artifacts are a frequent issue in Magnetic Resonance Imaging (MRI), often necessitating repeat scans and impacting diagnostic accuracy.
• Current clinical tools for identifying and quantifying MRI motion artifacts are limited.
• Subtle motion can retain diagnostic value, while severe motion may render images uninterpretable, requiring re-examination.

Purpose of the Study:

• To develop and validate a deep learning-based tool for automatic quantification of motion artifact severity in MRI brain scans.
• To assess the impact of subject movement parameters (displacement, rotation) on image quality.
• To enhance diagnostic quality and workflow efficiency in MRI examinations.

Main Methods:

• Development of a state-of-the-art stacked ensemble model utilizing deep learning.
• Classification of motion artifacts into five severity levels: no motion, slight, mild, moderate, and severe.
• Evaluation of the model's robustness across different acquisition parameters (T1-weighted, T2-weighted slices, various anatomical planes) and rigid-body motion.

Main Results:

• The stacked ensemble model achieved high performance metrics: 91.6% accuracy, 94.8% area under the curve, and 90% Cohen's Kappa.
• The model demonstrated robust prediction of rigid-body motion severity across diverse MRI acquisition parameters.
• The ensemble approach proved more accurate and robust compared to individual base learning models.

Conclusions:

• Deep learning offers a powerful method for automatic identification and quantification of motion artifacts in MRI.
• The developed stacked ensemble model can significantly aid in maintaining diagnostic quality and improving MRI workflow efficiency.
• This tool has the potential to reduce unnecessary repeat scans and support radiologists in image interpretation.