
Overview
Background
Dr Steffen Bollmann joined UQ’s School of Electrical Imaging and Computer Science in 2020 where he leads the Computational Imaging Group. The Group is developing computational methods to extract clinical and biological insights from magnetic resonance imaging (MRI) data. The aim is to make cutting-edge algorithms and tools available to a wide range of clinicians and researchers. This will enable better images, faster reconstruction times and the efficient extraction of clinical information to ensure a better understanding of a range of diseases. Dr Bollmann was appointed Artificial Intelligence (AI) lead for imaging at UQ’s Queensland Digital Health Centre (QDHeC) in 2023.
His research expertise is in quantitative susceptibility mapping, image segmentation and software applications to help researchers and clinicians access data and algorithms.
Dr Bollmann completed his PhD on multimodal imaging at the University Children’s Hospital and Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.
In 2014 he joined the Centre for Advanced Imaging at UQ as a National Imaging Facility Fellow, where he pioneered the application of deep learning methods for quantitative imaging techniques, in particular Quantitative Susceptibility Mapping.
In 2019 he joined the Siemens Healthineers collaborations team at the MGH Martinos Center in Boston on a one-year industry exchange where he worked on the translation of fast imaging techniques into clinical applications.
Availability
- Dr Steffen Bollmann is:
- Available for supervision
Fields of research
Research interests
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Reproducible Research Software
Developing software to enable reproducible neuroimaging, such as www.Neurodesk.org
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Computational Imaging
Developing tools to make computational algorithms for medical imaging more accessible and robust.
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Image Segmentation
Developing new methods to segment medical imaging data to extract quantitative information.
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Quantitative Susceptibility Mapping
Developing new methods to increase the robustness of processing quantitative susceptibility mapping.
Research impacts
Strong industry collaborations to bring research algorithms into applications such as Quantitative Susceptibility Mapping with industry partner Siemens Healthineers and the Neurodesk project with industry partner Oracle Cloud.
Further information is available at www.mri.sbollmann.net and regular research updates can be found on linkedin (https://www.linkedin.com/in/steffen-bollmann-00725097/) mastodon (https://masto.ai/@Sbollmann_MRI) and twitter/X (https://twitter.com/sbollmann_mri)
Works
Search Professor Steffen Bollmann’s works on UQ eSpace
2015
Conference Publication
When to perform channel combination in 7 Tesla quantitative susceptibility mapping?
Bollmann, Steffen, Zimmer, Fabian, O'Brien, Fabian, Vegh, Viktor and Barth, Markus (2015). When to perform channel combination in 7 Tesla quantitative susceptibility mapping?. Organization of the Human Brain Mapping, Hawaii Convention Centre, Honolulu, Hawaii, May.
2015
Journal Article
Developmental Changes in Gamma-Aminobutyric Acid Levels in Attention-Deficit/hyperactivity Disorder
Bollmann, S., Ghisleni, C., Poil, S.-S., Martin, E., Ball, J., Eich-Höchli, D., Edden, R. A. E., Klaver, P., Michels, L., Brandeis, D. and O'Gorman, R. L. (2015). Developmental Changes in Gamma-Aminobutyric Acid Levels in Attention-Deficit/hyperactivity Disorder. Translational Psychiatry, 5 (6) e589, e589.1-e589.8. doi: 10.1038/tp.2015.79
2015
Journal Article
Age-dependent and -independent changes in attention-deficit/hyperactivity disorder (ADHD) during spatial working memory performance
Bollmann, Steffen, Ghisleni, Carmen, Poil, Simon-Shlomo, Martin, Ernst, Ball, Juliane, Eich-Höchli, Dominique, Klaver, Peter, O’Gorman, Ruth L., Michels, Lars and Brandeis, Daniel (2015). Age-dependent and -independent changes in attention-deficit/hyperactivity disorder (ADHD) during spatial working memory performance. The World Journal of Biological Psychiatry, 18 (4), 279-290. doi: 10.3109/15622975.2015.1112034
2014
Journal Article
Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD)
Poil, S. -S., Bollmann, S., Ghisleni, C., O'Gorman, R. L., Klaver, P., Ball, J., Eich-Hoechli, D., Brandeis, D. and Michels, L. (2014). Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD). Clinical Neurophysiology, 125 (8), 1626-1638. doi: 10.1016/j.clinph.2013.12.118
2013
Journal Article
Coupling between resting cerebral perfusion and EEG
O'Gorman, R. L., Poil, S-S., Brandeis, D., Klaver, P., Bollmann, S., Ghisleni, C., Luechinger, R., Martin, E., Shankaranarayanan, A., Alsop, D. C. and Michels, L. (2013). Coupling between resting cerebral perfusion and EEG. Brain Topography, 26 (3), 442-457. doi: 10.1007/s10548-012-0265-7
2012
Conference Publication
A GPU-accelerated Performance Optimized RAP-MUSIC Algorithm for Real-Time Source Localization
Dinh, C., Ruehle, J., Bollmann, S., Haueisen, J. and Guellmar, D. (2012). A GPU-accelerated Performance Optimized RAP-MUSIC Algorithm for Real-Time Source Localization. BMT 2012 - 46th annual conference of the German Society for Biomedical Engineering, Jena, Germany, 16-19 September 2012. Berlin, Germany: Walter de Gruyter. doi: 10.1515/bmt-2012-4260
Funding
Current funding
Past funding
Supervision
Availability
- Dr Steffen Bollmann is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Available projects
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Reproducible Neuroimaging Framework NeuroDesk
This project provides a reproducible neuroimaging data processing platform based on software containers (docker and singularity). More information about this project can be found in the Nature Methods article: https://rdcu.be/dQJjq
The candidate will be able to learn about container technology and add new features to the platform, like the support of GPUs for deep learning applications, the support for M1/Arm processors by using muli-architecture builds, develop cloud deployment patterns using Kubernetes, build large language models to support users in programming neuroimaging applications and many more.
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Towards Standards and Benchmarks for Reproducible Neuroimaging Research
Address the reproducibility crisis in neuroimaging by developing methodologies and standards for defining reproducible, benchmarked analysis pipelines.
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Reproducible Neuroimaging Framework NeuroDesk
This project provides a reproducible neuroimaging data processing platform based on software containers (docker and singularity). More information about this project can be found in the Nature Methods article: https://rdcu.be/dQJjq
The candidate will be able to learn about container technology and add new features to the platform, like the support of GPUs for deep learning applications, the support for M1/Arm processors by using muli-architecture builds, develop cloud deployment patterns using Kubernetes, build large language models to support users in programming neuroimaging applications and many more.
Supervision history
Current supervision
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Doctor Philosophy
Robust Deep learning for Quantitative Susceptibility Mapping
Principal Advisor
Other advisors: Dr Fernanda Lenita Ribeiro
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Doctor Philosophy
Computational Medical Imaging
Principal Advisor
Other advisors: Dr Fernanda Lenita Ribeiro, Mr Aswin Narayanan
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Doctor Philosophy
Development of a deep learning framework for multi-modal medical imaging
Associate Advisor
Other advisors: Professor Markus Barth
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Doctor Philosophy
Neural Network¿Enhanced Multimodal Brain Electrical Source Imaging and Applications
Associate Advisor
Other advisors: Professor Markus Barth
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Doctor Philosophy
Structure-function brain network dynamics in post-stroke depression
Associate Advisor
Other advisors: Dr Lena Oestreich
Completed supervision
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2023
Doctor Philosophy
Automated Quantitative Susceptibility Mapping for Clinical Applications
Principal Advisor
Other advisors: Professor Markus Barth, Dr Monique Tourell
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2023
Master Philosophy
Solving Quantitative Susceptibility Mapping using Deep Learning
Principal Advisor
Other advisors: Professor Markus Barth
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2021
Doctor Philosophy
Computational in vivo Tissue Characterisation for Multi-Contrast High-Resolution Magnetic Resonance Imaging Data
Principal Advisor
Other advisors: Professor Markus Barth
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2024
Doctor Philosophy
Magnetostatic Modelling based on Deep Learning
Associate Advisor
Other advisors: Associate Professor Michael Bermingham, Professor Matthew Dargusch
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2021
Doctor Philosophy
Sequence Development to Improve Image Quality for T2- and Diffusion Weighted Imaging at 7T
Associate Advisor
Other advisors: Professor Markus Barth
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2020
Doctor Philosophy
MR signal modelling approaches to characterise tissue microstructure in in-vivo human brain
Associate Advisor
Other advisors: Professor Markus Barth
Media
Enquiries
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