Respiratory motion correction in dynamic MRI using robust data decomposition registration – Application to DCE-MRI
Résumé
Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC 60) at early enhancement.
Mots clés
Registration Respiratory motion correction Dynamic contrast-enhanced MRI Robust principal component analysis DCE-MRI
dynamic contrast enhanced magnetic resonance imaging
FFD
free-form deformation
NMI
normalized mutual information
PPCR
progressive principal component registration
PCA
principal component analysis
RPCA
robust principal component analysis
RDDR
robust data decomposition registration
RC
residual complexity
DCT
discrete cosine transform
ROI
region of interest
TIC
time-intensity curve
GT
ground truth
RMSE
root mean squared error
AUC
area under the curve