Markus Barth, Ph.D.
Affiliations: | University of Queensland, Saint Lucia, Queensland, Australia |
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Sign in to add trainee| Michiel Kleinnijenhuis | grad student | 2009-2013 | Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands |
| Anne Margarette Maallo | grad student | 2014-2018 | University of Queensland |
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| Ehrhardt SE, Wards Y, Rideaux R, et al. (2024) Neurochemical predictors of generalised learning induced by brain stimulation and training. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience |
| Shaw TB, York A, Barth M, et al. (2020) Towards Optimising MRI Characterisation of Tissue (TOMCAT) Dataset including all Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) data. Data in Brief. 32: 106043 |
| Thapaliya K, Vegh V, Bollmann S, et al. (2020) Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters. Frontiers in Neuroscience. 14: 271 |
| Sood S, Reutens DC, Kadamangudi S, et al. (2020) Field strength influences on gradient recalled echo MRI signal compartment frequency shifts. Magnetic Resonance Imaging |
| Shaw T, York A, Ziaei M, et al. (2020) Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using Multi-Contrast MRI. Neuroimage. 116798 |
| Puckett AM, Bollmann S, Junday K, et al. (2019) Bayesian population receptive field modeling in human somatosensory cortex. Neuroimage. 208: 116465 |
| Shaw TB, Bollmann S, Atcheson NT, et al. (2019) Non-linear realignment improves hippocampus subfield segmentation reliability. Neuroimage. 116206 |
| Thapaliya K, Urriola J, Barth M, et al. (2019) 7T GRE-MRI signal compartments are sensitive to dysplastic tissue in focal epilepsy. Magnetic Resonance Imaging |
| Bollmann S, Rasmussen KGB, Kristensen M, et al. (2019) DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping. Neuroimage |
| Bollmann S, Kristensen MH, Larsen MS, et al. (2019) SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network. Zeitschrift Fur Medizinische Physik |