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…
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)
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