Jelena Bozek

TL;DR: A fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain is proposed, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI.

TL;DR: A new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, is proposed and implemented and it is demonstrated that its use leads to improved and more robust alignment of multimodal imaging data.

TL;DR: This chapter gives a survey of image processing algorithms that have been developed for detection of masses and calcifications and an overview of algorithms in each step of the mass detection algorithms is given.

TL;DR: The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment and generated templates capture expected patterns of cortical development including an increase in gyrification as well as a increase in thickness and T1w/T2w myelination with increasing age.

TL;DR: An automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data which includes integrated dynamic distortion and slice-to-volume motion correction and an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection.