This repository contains scripts written to produce a number of research articles at University College London during my post-doctoral training, which are shared to support reproducibile research. Whenever possible, data are also included in the repository. If you use data or code included in PaperScripts in your research, please remember to cite our papers as explained below.
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sc_unireadout: code and simulated data part of "Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising". Grussu F, Battiston M, Veraart J, Schneider T, Cohen-Adad J, Shepherd TM, Alexander DC, Fieremans E, Novikov DS, Gandini Wheeler-Kingshott CAM; NeuroImage 2020, 217: 116884, doi: 10.1016/j.neuroimage.2020.116884. -
sardunet: code for "Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging". Grussu F, Blumberg SB, Battiston M, Kakkar LS, Lin H, Ianuș A, Schneider T, Singh S, Bourne R, Punwani S, Atkinson D, Gandini Wheeler-Kingshott CAM, Panagiotaki E, Mertzanidou T and Alexander DC. Frontiers in Physics 2021, 9:752208, doi: 10.3389/fphy.2021.752208.
PaperScripts and all data and code within it is distributed under the BSD 2-Clause License, Copyright (c) 2019, 2020, 2021 University College London. All rights reserved. Link to license here.
The use of PaperScripts MUST also comply with the individual licenses of all of its dependencies.
Funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 634541) and from the United Kingdom Engineering and Physical Sciences Research Council (EPSRC R006032/1 and M020533/1) is acknowledged.
Specific funding sources are acknowledged within each project's folder.