Tracer-Kinetic Model-Driven Motion Correction with Application to Renal DCE-MRI

A major challenge of the image registration in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is related to the image contrast variations caused by the contrast agent passage. Tracer-kinetic model-driven motion correction is an attractive solution for DCE-MRI, but previous studies only use the 3-parameter modified Tofts model. Firstly, a generalisation based on a 4-parameter 2-compartment tracer-kinetic model is presented. A practical limitation of these models is the need for non-linear least-squares (NLLS) fitting. This is prohibitively slow for image-wide parameter estimations, and is biased by the choice of initial values. To overcome this limitation, a fast linear least-squares (LLS) method to fit the two-compartment exchange and -filtration models (2CFM) to the data is introduced. Simulations of normal and pathological data were used to evaluate calculation time, accuracy and precision of the LLS against the NLLS method. Results show that the LLS method leads to a significant reduction in the calculation times. Secondly, a novel tracer-kinetic model-driven motion correction algorithm is introduced which uses a 4-parameter 2-compartment model to tackle the problem of image registration in 2D renal DCE-MRI. The core architecture of the algorithm can briefly described as follows: the 2CFM is linearly fitted pixel-by-pixel and the model fit is used as target for registration; then a free-form deformation model is used for pairwise co-registration of source and target images at the same time point. Another challenge that has been addressed is the computational complexity of non-rigid registration algorithms by precomputing steps to remove redundant calculations. Results in 5 subjects and simulated phantoms show that the algorithm is computationally efficient and improves alignment of the data. The proposed registration algorithm is then translated to 3D renal dynamic MR data. Translation to 3D is however challenging due to ghosting artefacts caused by within-frame breathing motion. Results in 8 patients show that the algorithm effectively removes between-frame breathing motion despite significant within-frame artefacts. Finally, the effect of motion correction on the clinical utility has been examined. Quantitative evaluation of single-kidney glomerular filtration rate derived from DCE-MRI against reference measurements shows a reduction of the bias, but precision is limited by within-frame artefacts. The suggested registration algorithm with a 4-parameter model is shown to be a computational efficient approach which effectively removes between-frame motion in a series of 2D and 3D renal DCE-MRI data.