Publications

Neurodesk paper published in Nature Methods

Our www.neurodesk.org paper was published in Nature Methods today: https://rdcu.be/dvqwU And we published a blog post describing why and how we developed this platform: https://communities.springernature.com/posts/reproducible-neuroimaging-for-everyone-behind-the-scenes-of-neurodesk 🧠 As researchers in neuroimaging, we often face hurdles with software installation and inconsistent results across different computing environments. But Neurodesk helps with that! ✨ Neurodesk is a Read more…

By Steffen, ago

NeXtQSM—A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data

Our latest work just got published in Medical Image Analysis: Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem Read more…

By Steffen, ago

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…

By Saskia Bollmann, ago

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…

By Steffen, ago

Zotero and NextCloud

Zotero is an open source reference and citation management tool that probably everyone knows and loves. Zotero stores the PDFs of publicaitons and books and when you want to keep your library synchronized you need some central storage. Now it’s important to stress that it’s a very bad idea to Read more…

By Steffen, ago

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)

By Steffen, ago

QSMxT preprint

Our latest preprint describes the QSMxT pipeline developed by Ashley Stewart – a CIBIT PhD student in our team: QSMxT enables robust & automated quantitative susceptibility mapping and extraction of quantitative information from regions of interest. The code can be found here: https://github.com/QSMxT/QSMxT The preprint is here: QSMxT: Robust Masking Read more…

By Steffen, ago

Improving FLAIR SAR Efficiency at 7T by Adaptive Tailoring of Adiabatic Pulse Power through Deep Learning Estimation

The full publication can be found here: https://doi.org/10.1002/mrm.28590 The preprint is here: [1911.08118] Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks (arxiv.org) The data used to train the model is on OSF: And we also built a colab notebook (described Read more…

By Steffen, ago

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

NMR in Biomedicine published our special issue review article, summarising and discussing the recent developments in deep learning QSM. The paper is here: https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.4292 The preprint is here: https://arxiv.org/abs/1912.05410 We also created a github repository collecting implementations of deep learning QSM algorithms that we are keeping updated regularly: https://github.com/dlQSM/dlQSM

By Steffen, ago