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…
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 this year’s AICI artificial intelligence in clinical imaging forum I prepared a tutorial for applying deep learning models to medical imaging data. It was very interesting to prepare the tutorial and see what models exist and how difficult it still is to make them work with real data. Here Read more…
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…
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 and published article here: https://doi.org/10.1002/mrm.28590) I was wondering if we can do more than just provide the source code. One problem I Read more…