With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields,
the amount of data collected per subject in a given MRI experiment has increased
considerably. Standard image processing packages are often challenged by the size of
these data. Dedicated methods are needed to leverage their extraordinary spatial resolution.
Here, we introduce a flexible Python toolbox that implements a set of advanced techniques
for high-resolution neuroimaging. With these tools, segmentation and laminar analysis of …