Dr Chantal Tax
Research Fellow
Overview
Research summary
My research focuses on image processing and biomedical modelling of magnetic resonance imaging data (MRI), with a focus on diffusion MRI data of the in-vivo human brain. I am interested in optimising different aspects of the preprocessing and analysis pipeline, as well as applying differential geometric methods to diffusion MRI data. My research in CUBRIC has been primarily focussed on extracting new information from multidimensional data acquired with the ultra-strong gradient Connectom scanner, and on combining diffusion MRI with relaxometry.
Biography
Undergraduate education
2006-2009: B.Sc. Biomedical Engineering (graduated With Great Appreciation)
Eindhoven University of Technology, The Netherlands
2010-2012: B.Sc. Applied Physics (passed 8 courses, 28 ECTS)
Eindhoven University of Technology, The Netherlands
Postgraduate education
2009-2012: M.Sc. Medical Engineering (graduated Cum Laude)
Eindhoven University of Technology, The Netherlands
2010-2012: Certificate Technological Management (graduated)
Eindhoven University of Technology,The Netherlands
2012-2016: Ph.D. Medical Imaging (graduated Cum Laude)
Utrecht University, The Netherlands
Employment
08/2017 – present: Research Fellow
Cardiff University, School of Psychology, Cardiff University Brain Research Imaging Centre (Cardiff UK)
09/2016 – 08/2017: Research Associate
Cardiff University, School of Psychology, Cardiff University Brain Research Imaging Centre (Cardiff UK)
03/2012 – 09/2016: PhD candidate
University Medical Center Utrecht, Image Sciences Institute, PROVIDI-lab (Utrecht The Netherlands)
07/2015 – 12/2015: Visiting PhD candidate
Harvard Medical School, Laboratory of Mathematics in Imaging (LMI) (Boston USA)
06/2014 – 09/2014: Visiting PhD candidate
Université de Sherbrooke, Sherbrooke Connectivity Imaging Lab (SCIL) (Sherbrooke Canada)
09/2010 – 03/2012: Master Student
Eindhoven University of Technology, BioMedical Image Analyisis (BMIA) Group / Kempenhaeghe Epileptology and Sleep Medicine Expertise Center (Eindhoven The Netherlands)
05/2010 – 08/2010: Visiting Master student
Royal Prince Alfred Hospital, Department of Clinical Neurophysiology (Sydney Australia)
Committee membership and associated activities
MICCAI Computational Diffusion MRI (CDMRI) Workshop Organising Committee (2016 – present)
MICCAI Diffusion MRI data Harmonisation Challenge (2016 – present)
KNAW Faces of Science (2014 - present)
ISMRM Session moderator (2015, 2017)
ISMRM Benelux Chapter Meeting Organising Committee (2014 – 2015)
Chair of the Medical Imaging Symposium for PhD Students (MISP, 2013)
Honours and awards
Awards
- Best PhD thesis award, Brain Center Rudolf Magnus (2017)
- Cum Laude PhD thesis award, Girard de Mielet-van Coehoorn Stichting (2017)
- Best PhD thesis award, Utrecht University ImagO Graduate School (2017)
- ISMRM Junior Fellow (2016)
- MRM Distinguished Reviewer (2016, 2017)
- ISMRM Magna Cum Laude Merit Award (2013, 2014, 2016)
- Best Research Contribution in Bio-imaging – BASP Frontiers Workshop (2015)
- Best first article award, Utrecht University ImagO Graduate School (2013)
- Best presentation award,Utrecht University ImagO Graduate School (2013)
- Best MSc thesis award, Eindhoven University of Technology, faculty of BioMedical Engineering (2012)
- IMDI Talent prize, IMDI symposium (2012)
- Best ASCI paper award, ICT.OPEN (2012)
Travel stipends
- ISMRM Junior Fellow Educational Stipend Award, (2016)
- ISMRM Educational Stipend Award (2012, 2013, 2014)
- ISMRM Travel Award for the ISMRM Diffusion workshop (2013)
Publications
2021
- Barakovic, M.et al. 2021. Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation. NeuroImage 227, article number: 117617. (10.1016/j.neuroimage.2020.117617)
- de Almeida Martins, J. P.et al. 2021. Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain. Human Brain Mapping 42(2), pp. 310-328. (10.1002/hbm.25224)
- Guo, F.et al. 2021. The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion MRI data. Human Brain Mapping 42(2), pp. 367-383. (10.1002/hbm.25228)
- Koller, K.et al. 2021. MICRA: Microstructural Image Compilation with Repeated Acquisitions. NeuroImage 225, article number: 117406. (10.1016/j.neuroimage.2020.117406)
2020
- Tong, Q.et al. 2020. A deep learning–based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols. Magnetic Resonance Imaging 73, pp. 31-44. (10.1016/j.mri.2020.08.001)
- Ning, L.et al. 2020. Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and result. NeuroImage 221, article number: 117128. (10.1016/j.neuroimage.2020.117128)
- Moyer, D.et al. 2020. Scanner invariant representations for diffusion MRI harmonization. Magnetic Resonance in Medicine 84(4), pp. 2174-2189. (10.1002/mrm.28243)
- St-Jean, S.et al. 2020. Automated characterization of noise distributions in diffusion MRI data. Medical Image Analysis 65, article number: 101758. (10.1016/j.media.2020.101758)
- Kleban, E.et al. 2020. Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain. NeuroImage 217, article number: 116793. (10.1016/j.neuroimage.2020.116793)
- Tax, C. M. W. 2020. Estimating chemical and microstructural heterogeneity by correlating relaxation and diffusion. In: Topgaard, D. ed. Advanced Diffusion Encoding Methods in MRI. Royal Society of Chemistry, pp. 186=227., (10.1039/9781788019910-00186)
- Genc, S.et al. 2020. Impact of b-value on estimates of apparent fibre density. Human Brain Mapping 41(10), pp. 2583-2595. (10.1002/hbm.24964)
- Rheault, F.et al. 2020. Tractostorm: the what, why, and how of tractography dissection reproducibility. Human Brain Mapping 41(7), pp. 1859-1874. (10.1002/hbm.24917)
- Tax, C.et al. 2020. The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain. NeuroImage 210, article number: 116534. (10.1016/j.neuroimage.2020.116534)
- Martins, J. P. d. A.et al. 2020. Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI. Magnetic Resonance 1, pp. 27-43. (10.5194/mr-1-27-2020)
- Chamberland, M.et al. 2020. Tractometry-based anomaly detection for single-subject white matter analysis. Presented at: Medical Imaging with Deep Learning (MIDL 2020), Montréal, Canada, 6-9 July 2020.
- Jenkins, C.et al. 2020. DW-MRS with ultra-strong diffusion gradients. Presented at: ISMRM & SMRT Virtual Conference & Exhibition 2020, Online, 8-14 August 2020.
- Harrison, J. R.et al. 2020. Imaging Alzheimer's genetic risk using Diffusion MRI: a systematic review. NeuroImage: Clinical 27, article number: 102359. (10.1016/j.nicl.2020.102359)
2019
- Chamberland, M.et al. 2019. Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain. NeuroImage 200, pp. 89-100. (10.1016/j.neuroimage.2019.06.020)
- Tax, C. M. W.et al. 2019. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms. NeuroImage 195, pp. 285-299. (10.1016/j.neuroimage.2019.01.077)
- Afzali Deligani, M.et al. 2019. Comparison of different tensor encoding combinations in microstructural parameter estimation. Presented at: IEEE International Symposium on Biomedical Imaging, Venice, Italy, 8-11 Apr 20192019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE pp. 1471-1474., (10.1109/ISBI.2019.8759100)
- Ning, L.et al. 2019. Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): Progress and results. Presented at: MICCAI 2018, Granada, Spain, 16-20 September 2018 Presented at Bonet-Carne, E. et al. eds.Computational Diffusion MRI, Vol. 1. Mathematics and Visualization Cham: Springer pp. 217-224., (10.1007/978-3-030-05831-9_18)
- Chamberland, M.et al. 2019. Obtaining representative core streamlines for white matter tractometry of the human brain. Presented at: International MICCAI Workshop, Granada, Spain, Sep 2018 Presented at Bonet-Carne, E. et al. eds.Computational Diffusion MRI. Mathematics and Visualization Cham: Springer pp. 359-366., (10.1007/978-3-030-05831-9_28)
2018
- Chamberland, M., Tax, C. M. W. and Jones, D. K. 2018. Meyer's loop tractography for image-guided surgery depends on imaging protocol and hardware. NeuroImage: Clinical 20, pp. 458-465. (10.1016/j.nicl.2018.08.021)
- Jones, D. K.et al. 2018. Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI. NeuroImage 182, pp. 8-38. (10.1016/j.neuroimage.2018.05.047)
- Albi, A.et al. 2018. Image registration to compensate for EPI distortion in patients with brain tumors: an evaluation of tract-specific effects. Journal of Neuroimaging 28(2), pp. 173-182. (10.1111/jon.12485)
2017
- Maier-Hein, K. H.et al. 2017. The challenge of mapping the human connectome based on diffusion tractography. Nature Communications 8(1), article number: 1349. (10.1038/s41467-017-01285-x)
- Vos, S. B.et al. 2017. The importance of correcting for signal drift in diffusion MRI. Magnetic Resonance in Medicine 77(1), pp. 285-299. (10.1002/mrm.26124)
2016
- Tax, C. M. W.et al. 2016. Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI. NeuroImage 142, pp. 260-279. (10.1016/j.neuroimage.2016.07.042)
2015
- Tax, C. M. W.et al. 2015. Seeing more by showing less: orientation-dependent transparency rendering for fiber tractography visualization. PLOS ONE 10(10), article number: e0139434. (10.1371/journal.pone.0139434)
2014
- Bach, M.et al. 2014. Methodological considerations on tract-based spatial statistics (TBSS). NeuroImage 100, pp. 358-369. (10.1016/j.neuroimage.2014.06.021)
Research topics and related papers
Combination of relaxometric measurements with diffusion imaging data
Exploring the extended measurement space accessible with ultra-strong gradients
Integration of artefact detection and mitigation in diffusion MRI through robust estimation
Applying differential geometric frameworks in diffusion MRI data
Funding
Rubicon grant from the Netherlands Organisation for Scientific Research – Postdoctoral fellowship (2017-2019)
Marina van Damme Grant (2015)
Research group
Research collaborators
Supervision
Postgraduate research interests
If you are interested in applying for a PhD, or for further information regarding my postgraduate research, please contact me directly (contact details available on the 'Overview' page), or submit a formal application.