Imaging of the pial arterial vasculature of the human brain in vivo using high-resolution 7T time-of-flight angiography

Our latest work is out in eLife: https://elifesciences.org/articles/71186 We show how small pial arteries can be targeted effectively with high-resolution in vivo MR imaging. The data is openly available (https://doi.org/10.17605/OSF.IO/NR6GC) and may be used for building models of brain physiology. The pial arterial vasculature of the human brain is the Read more…

TOMCAT dataset on Oracle Open Data

Oracle Cloud recently launched a platform to host scientific datasets and we uploaded our unique Hippocampus dataset (Towards Optimising MRI Characterisation of Tissue (TOMCAT) Dataset including all Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) data – ScienceDirect): https://opendata.oraclecloud.com/ords/r/opendata/opendata/details?data_set_id=28&bucket_name=TOMCAT&prefix=&clear=RR&session=610618357889967&cs=1o7GdDLNfm3PHWwxI2thFXrA8jHPcvjZCdFibkYYIPPofNfOuvVt8B6S0B7YTnzIOf6cDk9BXW5hSV9b8LmhoQA More background can be found in this blogpost: Oracle Open Data platform Read more…

Combining conda and google colab

Sometimes it’s necessary to downgrade packages in google colab to restore compatibility with an older version (e.g. pytorch). One way of doing this is using conda within colab and a few tricks make this work. Here is the colab notebook: https://colab.research.google.com/drive/1W5UnflB8m1zo6lP1y11V78QczMRBI0FE?usp=sharing Here a quick code gist:

NeXtQSM preprint

Our new preprint is online. NeXtQSM is a deep learning pipeline for data-consistent quantitative susceptibility mapping trained with simulated data build by our CIBIT MPhil student Francesco Cognolato: [2107.07752] NeXtQSM — A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data (arxiv.org)