For this year’s ISMRM, I’ve investigated how we can image the pial arterial vasculature using a time-of flight contrast. Surprisingly, we found that we are not limited by physiological parameters, but ‘simply’ by the voxel size. All the gory details Read more…
Tom Shaw and I prepared a talk for this year’s OHBM Brainhack Trainhack where we show what containers are and how to use them for neuroscience applications. We cover docker and singularity and show how to build and run containers Read more…
For the 2020 OHBM Brainhack we made a little tutorial explaining git and github. We hope it’s useful and thanks to the organizers for putting this great event together 🙂 Video: Slides:
For a paper we recently submitted (Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks – pre-print here: https://arxiv.org/abs/1911.08118) I was wondering if we can do more than just provide the Read more…
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 Read more…
During our last week in Boston, I was invited to give a talk in the Science on Tap Seminar. Science on tap is a fantastic iniative driven by dedicated early career researchers that organise a talk with drinks and nibbles Read more…
Vascularisation of the human brain obtained with magnetic resonance angiography at 160µm isotropic resolution at 7 Tesla. During the Science-on-Tap meeting at the Martinos Center, MGH, I gave a brief introduction into the fundamental principles of magnetic resonance angiography, and Read more…
Our latest paper on applying Deep Learning to solve QSM was published in Neuroimage: http://www.sciencedirect.com/science/article/pii/S1053811919302605 The pre-print is here: https://www.biorxiv.org/content/early/2018/03/07/278036
I was invited to present a tutorial about deepQSM during the 2019 OHBM educational session meeting in Rome. The tutorial recording is here and starts at 1:16:56: The interactive code is here: The slides:
Our latest paper using a convolutional neural network to remove the background field in QSM was published in Zeitschrift für Medizinische Physik: http://www.sciencedirect.com/science/article/pii/S0939388918301673 The preprint is here: https://www.biorxiv.org/content/10.1101/522151v1