Respiratory motion correction in dynamic MRI using robust data decomposition registration – Application to DCE-MRI

• Published in: Medical image analysis Vol. 18; no. 2; pp. 301 - 313
• Main Authors: Hamy, Valentin, Dikaios, Nikolaos, Punwani, Shonit, Melbourne, Andrew, Latifoltojar, Arash, Makanyanga, Jesica, Chouhan, Manil, Helbren, Emma, Menys, Alex, Taylor, Stuart, Atkinson, David
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2014; Elsevier
• Subjects: Algorithms; Bioengineering; Computer Science; Computer Simulation; Contrast Media; Dynamic contrast-enhanced MRI; Female; Human health and pathology; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Intestinal Diseases - diagnosis; Intestine, Small; Life Sciences; Liver Diseases - diagnosis; Magnetic Resonance Imaging - methods; Male; Medical Imaging; Movement; Nuclear medicine; Principal Component Analysis; Prostatic Diseases - diagnosis; Registration; Reproducibility of Results; Respiration; Respiratory motion correction; Robust principal component analysis; Sensitivity and Specificity; Dynamic contrast-enhanced MRI; DCE-MRI; FFD; RDDR; PPCR; NMI; RMSE; ROI; GT; PCA; TIC; AUC; RC; Robust principal component analysis; DCT; Registration; RPCA; Respiratory motion correction; Registration Respiratory motion correction Dynamic contrast-enhanced MRI Robust principal component analysis DCE-MRI; ground truth; free-form deformation; progressive principal component registration; robust principal component analysis; normalized mutual information; discrete cosine transform; root mean squared error; dynamic contrast enhanced magnetic resonance imaging; residual complexity; area under the curve; robust data decomposition registration; time-intensity curve; region of interest; principal component analysis
• ISSN: 1361-8415; 1361-8423; 1361-8431; 1361-8423
• DOI: 10.1016/j.media.2013.10.016

•A registration technique based on low rank – sparse data decomposition is introduced.•Registration is applied to dynamic contrast enhanced MRI.•Errors in the monitoring of enhancement in dynamic contrast enhanced MRI are reduced. 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 (AUC60) at early enhancement.