I recently had the wonderful opportunity to present our deep learning work in magnetic resonance imaging at the ML4AU CoP showcase event to more than 65 participants. I showed one example on efficiently solving an ill-posed inverse problem for quantitative susceptibility mapping that was trained with purely synthetic data describing Read more…
When using a High Performance Computing System you also might have run into the problem of how to get data in and out of this system. We are very lucky in Australia to have CloudStor, which is Aarnets hosted OwnCloud version with 1TB of storage available for every researcher and Read more…
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…
I am extremely lucky to have been awarded an ARC Linkage grant together with our partner Siemens Healthineers to advance deep learning applications for medical imaging in the next 3 years. It’s not just exciting because this is my first grant I have ever won, but also because this project Read more…
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)
For the OHBM 2021 I created a short 2 minute summary of our NeuroDesk project 🙂
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…
We were very fortunate to be highlighted in MRM for our paper “Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power through deep learning B1+ estimation” and the reproducible research habits in our group 🙂 The interview covering the contents of the paper can be found Read more…
This is the educational talk I prepared for ISMRM 2021 about Quantitative Susceptibility Mapping: The slides are here: I also prepared a computational notebook that illustrates some of the concepts interactively in code: http://bit.ly/ISMRM-QSM-2021 (or https://colab.research.google.com/drive/10iyYqhzF-U5NOTjIT_jqEr5mAH-aew-k?usp=sharing)
I was recently invited to join the panel for an @OSFramework webinar on Leveraging Open Ecosystems to Enhance Reproducible Workflows: The webinar covers OSF integration with @protocolsIO, R @aaronwolen, command line interfaces and jupyter notebooks – thank you for the great discussions!