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

Abstract

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.

Feasibility of free breathing Lung MRI for Radiotherapy using non-Cartesian k-space acquisition schemes

“…Motion during these time frames can cause inter-frame misalignment which can affect functional assessment. 6 An alternative method of overcoming sensitivity to motion is to change the way k-space is acquired and filled. 7 Non-Cartesian acquisition schemes provide a different sampling geometry to manage motion.…”

Correlation of quantitative dynamic contrast‐enhanced MRI with microvascular density in necrotic, partial necrotic, and viable liver tumors in a rabbit model

“…Conventional registration methods are likely to fail with DCE‐MRI data because of important local intensity changes across different time‐points. New methods have been proposed using tracer kinetic model‐driven registration, (20) progressive principal component registration, (21) and robust principal component registration, (22) but it is difficult to apply these methods into practical and clinical setting due to its complexity of algorithm. To minimize motion artifacts, in this study, we included only large VX2 tumors ($>5\text{mm}$), which are less affected by motion.…”

Imaging-Based Liver Function Tests – Past, Present and Future

“…With increasing temporal resolution, movements become smaller, but motion correction is still necessary, particularly since the higher temporal resolution allows data acquisition during free breathing. For motion correction, there is currently primarily experience with renal perfusion measurement in MRI [62,63]. However, motion correction has not yet been used clinically in Gd-EOB-based liver function measurement.…”