Normalized gradient fields for nonlinear motion correction of DCE-MRI time series

Dynamic MR image recordings (DCE-MRI) of moving organs using bolus injections create two differenttypes of dynamics in the images: (i) spatial motion artifacts due to patient movements, breathing andphysiological pulsations that we want to counteract and (ii) signal intensity changes during contrastagent wash-in and wash-out that we want to preserve. Proper image registration is needed to counteractthe motion artifacts and for a reliable assessment of physiological parameters. In this work we present apartial differential equation-based method for deformable multimodal image registration using normalized gradients and the Fourier transform to solve the Euler–Lagrange equations in a multilevel hierarchy.This approach is particularly well suited to handle the motion challenges in DCE-MRI time series, beingvalidated on ten DCE-MRI datasets from the moving kidney. We found that both normalized gradientsand mutual information work as high-performing cost functionals for motion correction of this type ofdata. Furthermore, we demonstrated that normalized gradients have improved performance comparedto mutual information as assessed by several performance measures. We conclude that normalized gradients can be a viable alternative to mutual information regarding registration accuracy, and with promisingclinical applications to DCE-MRI recordings from moving organs