Dr Chantal Tax

2025

• Arends, G. C. et al., 2025. Feasibility of strong diffusion encoding and fast readout using a plug‐and‐play head gradient insert at 7 T. Magnetic Resonance in Medicine 94 (5), pp.2304-2316. (10.1002/mrm.30613)
• Canales-Rodríguez, E. J. et al., 2025. A diffusion MRI model for random walks confined on cylindrical surfaces: towards non-invasive quantification of myelin sheath radius. Frontiers in Physics 13 1516630. (10.3389/fphy.2025.1516630)
• Genc, S. et al. 2025. MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression. Nature Communications 16 3317. (10.1038/s41467-025-58604-w)
• Molendowska, M. et al. 2025. Giving the prostate the boost it needs: Spiral diffusion MRI using a high-performance whole-body gradient system for high b-values at short echo times. Magnetic Resonance in Medicine 93 (3), pp.1256-1272. (10.1002/mrm.30351)
• Schilling, K. G. et al., 2025. White matter geometry confounds Diffusion Tensor Imaging Along Perivascular Space (DTI‐ALPS) measures. Human Brain Mapping 46 (10) e70282. (10.1002/hbm.70282)
• Schilling, K. G. et al., 2025. The relationship of white matter tract orientation to vascular geometry in the human brain. Scientific Reports 15 (1) 18396. (10.1038/s41598-025-99724-z)
• Verschuur, A. S. et al., 2025. Methodological considerations on diffusion MRI tractography in infants aged 0-2 years: a scoping review. Pediatric Research 97 , pp.880-897. (10.1038/s41390-024-03463-2)
• Verschuur, A. S. et al., 2025. Diffusion MRI tractography with along-tract profiling reveals subtle neurodevelopmental differences between moderate and late preterm infants.. European Journal of Radiology 187 112098. (10.1016/j.ejrad.2025.112098)
• Verschuur, A. S. et al., 2025. Trends in term-equivalent age brain volumes in infants born across the gestational age spectrum. Children 12 1026. (10.3390/children12081026)

2024

• Fokkinga, E. et al., 2024. Advanced diffusion-weighted MRI for cancer microstructure assessment in body imaging, and its relationship with histology. Journal of Magnetic Resonance Imaging 60 (4), pp.1278-1304. (10.1002/jmri.29144)
• Genc, S. et al. 2024. Developmental differences in canonical cortical networks: insights from microstructure-informed tractography. Network Neuroscience 8 (3), pp.946-964. (10.1162/netn_a_00378)
• Ligneul, C. et al., 2024. Diffusion‐weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling. Magnetic Resonance in Medicine 91 (3), pp.860-885. (10.1002/mrm.29877)
• MacIver, C. L. et al. 2024. White matter microstructural changes using ultra-strong diffusion gradient MRI in adult-onset idiopathic focal cervical dystonia. Neurology 103 (4) e209695. (10.1212/WNL.0000000000209695)
• Molendowska, M. et al. 2024. Diffusion MRI in prostate cancer with ultra-strong whole body gradients. NMR in Biomedicine (10.1002/nbm.5229)
• Planchuelo-Gómez, Á. et al. 2024. Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning. Medical Image Analysis 94 103134. (10.1016/j.media.2024.103134)
• Verschuur, A. S. et al., 2024. Feasibility study to unveil the potential: considerations of constrained spherical deconvolution tractography with unsedated neonatal diffusion brain MRI data. Frontiers in Radiology 4 1416672. (10.3389/fradi.2024.1416672)

2023

• Barakovic, M. et al. 2023. Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology. Frontiers in Neuroscience 17 1209521. (10.3389/fnins.2023.1209521)
• Davies Jenkins, C. W. et al., 2023. Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients. Frontiers in Neuroscience 17 1258408. (10.3389/fnins.2023.1258408)
• Kleban, E. , Jones, D. and Tax, C. 2023. The impact of head orientation with respect to B0 on diffusion tensor MRI measures. Imaging Neuroscience 1 , pp.1-17. (10.1162/imag_a_00012)
• MacIver, C. et al. 2023. Macro- and micro-structural Insights into primary dystonia A UK Biobank study. Journal of Neurology (10.1007/s00415-023-12086-2)
• Soliman, R. K. et al., 2023. Constrained spherical deconvolution -based tractography of major language tracts reveals post-stroke bilateral white matter changes correlated to aphasia.. Magnetic Resonance Imaging 95 , pp.19-26. (10.1016/j.mri.2022.10.004)
• Tax, C. M. W. et al. 2023. Ultra-strong diffusion-weighted MRI reveals cerebellar grey matter abnormalities in movement disorders. NeuroImage: Clinical 38 103419. (10.1016/j.nicl.2023.103419)

2022

• Fan, Q. et al., 2022. Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: methodological advances and scientific impact. NeuroImage 254 118958. (10.1016/j.neuroimage.2022.118958)
• MacIver, C. L. et al. 2022. Structural magnetic resonance imaging in dystonia: A systematic review of methodological approaches and findings. European Journal of Neurology 29 (11), pp.3418-3448. (10.1111/ene.15483)
• Molendowska, M. et al. 2022. Physiological effects of human body imaging with 300 mT/m gradients. Magnetic Resonance in Medicine 87 (5), pp.2512-2520. (10.1002/mrm.29118)
• Schilling, K. G. et al., 2022. Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography. Human Brain Mapping 43 (4), pp.1196-1213. (10.1002/hbm.25697)
• Shastin, D. et al. 2022. Surface-based tracking for short association fibre tractography. NeuroImage 260 119423. (10.1016/j.neuroimage.2022.119423)
• Tax, C. M. et al. 2022. What's new and what's next in diffusion MRI preprocessing. NeuroImage 249 118830. (10.1016/j.neuroimage.2021.118830)
• Verschuur, A. et al., 2022. Improved segmentation of neonatal brain MRI scans by addressing motion artifacts with data interpolation. Presented at: Proceedings of the 13th International Newborn Brain Conference: Neuro-imaging studies 10-12/02/2022. IOS Press. (10.3233/NPM-229001)

2021

• Barakovic, M. et al. 2021. Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation. NeuroImage 227 117617. (10.1016/j.neuroimage.2020.117617)
• Chamberland, M. et al. 2021. Detecting microstructural deviations in individuals with deep diffusion MRI tractometry. Nature Computational Science 1 , pp.598-606. (10.1038/s43588-021-00126-8)
• 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)
• Gholam, J. A. et al. 2021. aDWI-BIDS: advanced diffusion weighted imaging metadata for the brain imaging data structure. Presented at: ISMRM & SMRT Annual Meeting & Exhibition Virtual 15-20 May 2021.
• 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)
• Guo, F. et al., 2021. Fiber orientation distribution from diffusion MRI: Effects of inaccurate response function calibration. Journal of Neuroimaging 31 (6), pp.1082-1098. (10.1111/jon.12901)
• Koller, K. et al. 2021. MICRA: Microstructural Image Compilation with Repeated Acquisitions. NeuroImage 225 117406. (10.1016/j.neuroimage.2020.117406)
• Schilling, K. G. et al., 2021. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?. NeuroImage 243 118502. (10.1016/j.neuroimage.2021.118502)
• Schilling, K. G. et al., 2021. Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow. NeuroImage 242 118451. (10.1016/j.neuroimage.2021.118451)
• Tax, C. M. W. et al. 2021. Magnetic resonance imaging of T2 - and diffusion snisotropy using a tiltable receive coil. Presented at: Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy Dagstuhl, Germany 28 Oct–2 Nov 2018. Published in: Ozarslan, E. et al., Anisotropy Across Fields and Scales. Mathematics and Visualization Springer. , pp.247-262. (10.1007/978-3-030-56215-1_12)
• Tax, C. M. W. et al. 2021. Measuring compartmental T2-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-T2 correlation MRI. NeuroImage 236 117967. (10.1016/j.neuroimage.2021.117967)

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.
• 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)
• Harrison, J. R. et al. 2020. Imaging Alzheimer's genetic risk using Diffusion MRI: a systematic review. NeuroImage: Clinical 27 102359. (10.1016/j.nicl.2020.102359)
• 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.
• 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 116793. (10.1016/j.neuroimage.2020.116793)
• 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)
• 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)
• Ning, L. et al., 2020. Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and result. NeuroImage 221 117128. (10.1016/j.neuroimage.2020.117128)
• Pizzolato, M. et al., 2020. Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge. Presented at: MICCAI Workshop Shenzhen, China Oct 2019. Published in: Bonet-Carne, E. et al., Computational Diffusion MRI. Mathematics and Visualization Springer. , pp.195-208. (10.1007/978-3-030-52893-5_17)
• 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)
• St-Jean, S. et al., 2020. Automated characterization of noise distributions in diffusion MRI data. Medical Image Analysis 65 101758. (10.1016/j.media.2020.101758)
• 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 116534. (10.1016/j.neuroimage.2020.116534)
• 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)
• 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)

2019

• 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 2019. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE. , pp.1471-1474. (10.1109/ISBI.2019.8759100)
• Blumberg, S. B. et al., 2019. Multi-stage prediction networks for data harmonization. Presented at: Medical Image Computing and Computer Assisted Intervention – MICCAI Shenzhen, China 13-17 Oct 2019. Medical Image Computing and Computer Assisted Intervention – MICCAI Proceedings. Vol. 11767.Lecture Notes in Computer Science Springer. , pp.411-419. (10.1007/978-3-030-32251-9_45)
• 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)
• 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. Published in: Bonet-Carne, E. et al., Computational Diffusion MRI. Mathematics and Visualization Cham: Springer. , pp.359-366. (10.1007/978-3-030-05831-9_28)
• 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. Published in: Bonet-Carne, E. et al., Computational Diffusion MRI. Vol. 1.Mathematics and Visualization Cham: Springer. , pp.217-224. (10.1007/978-3-030-05831-9_18)
• 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)

2018

• 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)
• 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)

2017

• Maier-Hein, K. H. et al., 2017. The challenge of mapping the human connectome based on diffusion tractography. Nature Communications 8 (1) 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) 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)