Segmentation

Designers of headgear often still have to rely on outdated anthropometric tables which offer only univariate measurements and very limited information about the variability of the human head. Promising solutions exist to supplement these tables with more complete 3D data, and several 3D model databases are currently on the market.

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.

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.

Rats are widely used in experimental neurobiological research, and rat brain atlases are important resources for identifying brain regions in the context of experimental microsurgery, tissue sampling, and neuroimaging, as well as comparison of findings across experiments. Currently, most available rat brain atlases are constructed from histological material derived from single specimens, and provide two-dimensional or threedimensional (3D) outlines of diverse brain regions and fiber tracts.

Statistical Shape Models capture the shape variation of a training set of shapes and can be registered to an image of an object of the class they represent by simple adjustment of their parameters. We have applied statistical shape models of healthy tracheas to the assessment and stenting of tracheal stenosis. The idea is that a model with healthy tracheas only will not be influenced by local geometric variations typical of stenosis. The model produces a shape that is an estimation of the shape of the patient's trachea if it were not narrowed.