
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
2021
Journal Article
Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective
Levitis, Elizabeth, van Praag, Cassandra D Gould, Gau, Rémi, Heunis, Stephan, DuPre, Elizabeth, Kiar, Gregory, Bottenhorn, Katherine L, Glatard, Tristan, Nikolaidis, Aki, Whitaker, Kirstie Jane, Mancini, Matteo, Niso, Guiomar, Afyouni, Soroosh, Alonso-Ortiz, Eva, Appelhoff, Stefan, Arnatkeviciute, Aurina, Atay, Selim Melvin, Auer, Tibor, Baracchini, Giulia, Bayer, Johanna M M, Beauvais, Michael J S, Bijsterbosch, Janine D, Bilgin, Isil P, Bollmann, Saskia, Bollmann, Steffen, Botvinik-Nezer, Rotem, Bright, Molly G, Calhoun, Vince D, Chen, Xiao ... Maumet, Camille (2021). Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective. GigaScience, 10 (8) giab051. doi: 10.1093/gigascience/giab051
2021
Journal Article
Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
Gau, Rémi, Noble, Stephanie, Heuer, Katja, Bottenhorn, Katherine L., Bilgin, Isil P., Yang, Yu-Fang, Huntenburg, Julia M., Bayer, Johanna M.M., Bethlehem, Richard A.I., Rhoads, Shawn A., Vogelbacher, Christoph, Borghesani, Valentina, Levitis, Elizabeth, Wang, Hao-Ting, Van Den Bossche, Sofie, Kobeleva, Xenia, Legarreta, Jon Haitz, Guay, Samuel, Atay, Selim Melvin, Varoquaux, Gael P., Huijser, Dorien C., Sandström, Malin S., Herholz, Peer, Nastase, Samuel A., Badhwar, AmanPreet, Dumas, Guillaume, Schwab, Simon, Moia, Stefano, Dayan, Michael ... Zuo, Xi-Nian (2021). Brainhack: Developing a culture of open, inclusive, community-driven neuroscience. Neuron, 109 (11), 1769-1775. doi: 10.1016/j.neuron.2021.04.001
2021
Conference Publication
Robust masking techniques for multi-echo quantitative susceptibility mapping
Stewart, Ashley, Robinson, Simon Daniel, O'Brien, Kieran, Jin, Jin, Widhalm, Georg, Hangel, Gilbert, Walls, Angela, Goodwin, Jonathan, Eckstein, Korbinian, Barth, Markus and Bollmann, Steffen (2021). Robust masking techniques for multi-echo quantitative susceptibility mapping. ISMRM 2021, Online, 15-20 May 2021. Concord, CA United States: International Society for Magnetic Resonance in Medicine.
2021
Conference Publication
Submillimeter, aub-minute quantitative susceptibility mapping using a multi-shot 3D-EPI with 2D CAIPIRINHA acceleration
Tourell, Monique, Jin, Jin, Stewart, Ashley, Bollmann, Saskia, Bollmann, Steffen, Robinson, Simon, O'Brien, Kieran and Barth, Markus (2021). Submillimeter, aub-minute quantitative susceptibility mapping using a multi-shot 3D-EPI with 2D CAIPIRINHA acceleration. ISMRM 2021, Online, 15-20 May 2021. Concord, CA United States: International Society for Magnetic Resonance in Medicine.
2021
Conference Publication
QSMxT - a cross-platform, flexible, lightweight, and scalable processing pipeline for quantitative susceptibility mapping
Stewart, Ashley, Robinson, Simon Daniel, O'Brien, Kieran, Jin, Jin, Walls, Angela, Narayanan, Aswin, Barth, Markus and Bollmann, Steffen (2021). QSMxT - a cross-platform, flexible, lightweight, and scalable processing pipeline for quantitative susceptibility mapping. ISMRM 2021, Online, 15-21 May 2021. Concord, CA USA: International Society for Magnetic Resonance in Medicine.
2021
Journal Article
Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power through deep learning estimation
Abbasi‐Rad, Shahrokh, O’Brien, Kieran, Kelly, Samuel, Vegh, Viktor, Rodell, Anders, Tesiram, Yasvir, Jin, Jin, Barth, Markus and Bollmann, Steffen (2021). Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power through deep learning estimation. Magnetic Resonance in Medicine, 85 (5), 2462-2476. doi: 10.1002/mrm.28590
2020
Journal Article
MRI phase offset correction method impacts quantitative susceptibility mapping
Abdulla, Shaeez Usman, Reutens, David, Bollmann, Steffen and Vegh, Viktor (2020). MRI phase offset correction method impacts quantitative susceptibility mapping. Magnetic Resonance Imaging, 74, 139-151. doi: 10.1016/j.mri.2020.08.009
2020
Journal Article
Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI
Shaw, Thomas, York, Ashley, Ziaei, Maryam, Barth, Markus and Bollmann, Steffen (2020). Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI. NeuroImage, 218 116798, 116798. doi: 10.1016/j.neuroimage.2020.116798
2020
Journal Article
Towards optimising MRI characterisation of Tissue (TOMCAT) dataset including all Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) data
Shaw, Thomas, York, Ashley, Barth, Markus and Bollmann, Steffen (2020). Towards optimising MRI characterisation of Tissue (TOMCAT) dataset including all Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) data. Data in Brief, 32 106043, 106043. doi: 10.1016/j.dib.2020.106043
2020
Journal Article
Influence of 7T GRE-MRI signal compartment model choice on tissue parameters
Thapaliya, Kiran, Vegh, Viktor, Bollmann, Steffen and Barth, Markus (2020). Influence of 7T GRE-MRI signal compartment model choice on tissue parameters. Frontiers in Neuroscience, 14 271, 271. doi: 10.3389/fnins.2020.00271
2020
Journal Article
Overview of quantitative susceptibility mapping using deep learning: current status, challenges and opportunities
Jung, Woojin, Bollmann, Steffen and Lee, Jongho (2020). Overview of quantitative susceptibility mapping using deep learning: current status, challenges and opportunities. NMR in Biomedicine, 35 (4) e4292, e4292. doi: 10.1002/nbm.4292
2020
Journal Article
Functional connectivity of the irritative zone identified by electrical source imaging, and EEG-correlated fMRI analyses
Urriola, Javier, Bollmann, Steffen, Tremayne, Fred, Burianová, Hana, Marstaller, Lars and Reutens, David (2020). Functional connectivity of the irritative zone identified by electrical source imaging, and EEG-correlated fMRI analyses. NeuroImage: Clinical, 28 102440, 102440. doi: 10.1016/j.nicl.2020.102440
2019
Journal Article
Non-linear realignment improves hippocampus subfield segmentation reliability
Shaw, Thomas B., Bollmann, Steffen, Atcheson, Nicole T., Strike, Lachlan T., Guo, Christine, McMahon, Katie L., Fripp, Jurgen, Wright, Margaret J., Salvado, Olivier and Barth, Markus (2019). Non-linear realignment improves hippocampus subfield segmentation reliability. NeuroImage, 203 116206, 116206. doi: 10.1016/j.neuroimage.2019.116206
2019
Journal Article
7T GRE-MRI signal compartments are sensitive to dysplastic tissue in focal epilepsy
Thapaliya, Kiran, Urriola, Javier, Barth, Markus, Reutens, David C., Bollmann, Steffen and Vegh, Viktor (2019). 7T GRE-MRI signal compartments are sensitive to dysplastic tissue in focal epilepsy. Magnetic Resonance Imaging, 61, 1-8. doi: 10.1016/j.mri.2019.05.011
2019
Journal Article
DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping
Bollmann, Steffen, Rasmussen, Kasper Gade Bøtker, Kristensen, Mads, Blendal, Rasmus Guldhammer, Østergaard, Lasse Riis, Plocharski, Maciej, O'Brien, Kieran, Langkammer, Christian, Janke, Andrew and Barth, Markus (2019). DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping. NeuroImage, 195, 373-383. doi: 10.1016/j.neuroimage.2019.03.060
2019
Conference Publication
Quantitative susceptibility mapping for routine clinical use – an inline automated QSM reconstruction pipeline
Stewart, Ashley, O'Brien, Kieran, Kim, Jinsuh, Maréchal, Bénédicte, Nasrallah, Fatima, Kean, Michael, Barth, Markus and Bollmann, Steffen (2019). Quantitative susceptibility mapping for routine clinical use – an inline automated QSM reconstruction pipeline. ISMRM 2019, Montreal, Canada, 11-16 May 2019. Concord, CA United States: International Society for Magnetic Resonance in Medicine.
2019
Journal Article
SHARQnet – Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network
Bollmann, Steffen, Kristensen, Matilde Holm, Larsen, Morten Skaarup, Olsen, Mathias Vassard, Pedersen, Mads Jozwiak, Østergaard, Lasse Riis, O’Brien, Kieran, Langkammer, Christian, Fazlollahi, Amir and Barth, Markus (2019). SHARQnet – Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network. Zeitschrift für Medizinische Physik, 29 (2), 139-149. doi: 10.1016/j.zemedi.2019.01.001
2018
Conference Publication
Improving FLAIR SAR efficiency by predicting B1-maps at 7T from a standard localizer scan using deep convolutional neural networks
Bollmann, Steffen, Kelly, Samuel, Vegh, Viktor, Rodell, Anders, Tesiram, Yas, Barth, Markus and O'Brien, Kieran (2018). Improving FLAIR SAR efficiency by predicting B1-maps at 7T from a standard localizer scan using deep convolutional neural networks. ISMRM, Paris, France, 17-21 June 2018.
2018
Conference Publication
Myelin water fraction across the corpus callosum using multi-echo gradient echo at 7T - influence of model settings and flip angle
Thapaliya, Kiran, Vegh, Viktor, Bollmann, Steffen and Barth, Markus (2018). Myelin water fraction across the corpus callosum using multi-echo gradient echo at 7T - influence of model settings and flip angle. ISMRM, Paris, France, 16-21 June 2018.
2018
Conference Publication
7T GRE-MRI frequency shifts obtained from signal compartments can differentiate normal from dysplastic tissue in focal epilepsy
Thapaliya, Kiran, Barth, Markus, Bollmann, Steffen, Reutens, David and Vegh, Viktor (2018). 7T GRE-MRI frequency shifts obtained from signal compartments can differentiate normal from dysplastic tissue in focal epilepsy. ISMRM, Paris, France, 16-21 June 2018.
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
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
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Doctor Philosophy
Development of a deep learning framework for multi-modal medical imaging
Associate Advisor
Other advisors: Professor Markus Barth
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
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