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…

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)

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…

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…

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