NeXtQSM preprint

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)

QSMxT preprint

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…

Building an Interactive paper supplement with Google Colab and the Open Science Foundation

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…

Non-linear Realignment Using Minimum Deformation Averaging for Single-subject fMRI at Ultra-high Field

Motion dominates the contribution to variance in fMRI time series and it is therefore important to account for this variability correctly. Currently, most correction schemes use a rigid body realignment procedure, but interactions with magnetic field inhomogeneities and physiological fluctuations lead to non-linear deformations. Non-linear realignment increased spatial resolution by Read more…