Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging - with data from the Osteoarthritis Initiative

TL;DR

This paper introduces simple normalization methods to correct scanner drift in knee MRI scans, improving the consistency of automatic segmentation and volume change measurements in longitudinal studies.

Contribution

The study proposes and validates two effective scanner drift normalization techniques, with Atlas Affine Normalization showing significant improvements in segmentation accuracy.

Findings

Atlas Affine Normalization reduces scanner drift effects

Improves consistency of cartilage volume change measurements

Validated on 1975 scans from diverse MRI systems

Abstract

Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic image analysis methods used in longitudinal studies. The implication is increased measurement variation and a risk of bias in the estimations (e.g. in the volume change for a structure). We proposed two quite different approaches for scanner drift normalization and demonstrated the performance for segmentation of knee MRI using the fully automatic KneeIQ framework. The validation included a total of 1975 scans from both high-field and low-field MRI. The results demonstrated that the pre-processing method denoted Atlas Affine Normalization significantly removed scanner drift effects and ensured that the cartilage volume change quantifications became consistent with manual expert scores.