Diffusional kurtosis imaging (DKI) is a recently proposed extension of the conventional DTI model. It has been shown to offer more sensitive characterization of neural tissues than DTI. So far, DKI has only been applied to adult human and small animal studies, but not yet to human newborns. In this work, we present an optimized workflow for the acquisition and processing of DKI images of newborns. First, optimal set of diffusion weighting gradients for DKI studies of newborn subjects is proposed. Optimized gradients allow to estimate DKI parameters with the highest precision.
Motion and distortion correction of the data is very important in diffusion-weighted MR imaging. When scanning non-cooperative subjects or non-sedated newborns, resulting images are significantly distorted by motion. When scanning at b-values higher than 1500 s/mm², distortion from eddy currents becomes severe if no compensatory scheme is applied. In our research, we use and evaluate different state of the art motion and distortion correction approaches.
Estimation and removal of noise from Magnetic Resonance Images (MRI) is an active area of research. Noise remains one of the main causes of quality deterioration in MRI and is a subject in a large number of papers in the MRI literature. Consideration of how noise affects the true signal is important for proper interpretation and analysis of MR images. This work mainly deals with the estimation of noise and the underlying true signal from both single and multiple coil acquired magnetic resonance images.
