Our article “QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping” was just published in MRM 🙂 In this article we developed an automated, scalable, and robust QSM workflow that starts from Dicom images and produces segmentations of regions of interest: The full paper is here (unfortunately behind a 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 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…
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)
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
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
Our latest paper looking at different ways of combining phase for QSM is now published in MRM: http://doi.wiley.com/10.1002/mrm.26644