
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
2023
Journal Article
Variability of visual field maps in human early extrastriate cortex challenges the canonical model of organization of V2 and V3
Ribeiro, Fernanda Lenita, York, Ashley, Zavitz, Elizabeth, Bollmann, Steffen, Rosa, Marcello GP and Puckett, Alexander (2023). Variability of visual field maps in human early extrastriate cortex challenges the canonical model of organization of V2 and V3. eLife, 12, 12. doi: 10.7554/elife.86439
2023
Conference Publication
Investigating the computational reproducibility of Neurodesk
Dao, Thuy, Renton, Angela, Narayanan, Aswin, Barth, Markus and Bollmann, Steffen (2023). Investigating the computational reproducibility of Neurodesk. International Society for Magnetic Resonance in Medicine, Toronto, ON, Canada, 3-8 June 2023.
2023
Conference Publication
Proceedings of the OHBM Brainhack 2021
Nikolaidis, Aki, Manchini, Matteo, Auer, Tibor, L. Bottenhorn, Katherine, Alonso-Ortiz, Eva, Gonzalez-Escamilla, Gabriel, Valk, Sofie, Glatard, Tristan, Selim Atay, Melvin, M.M. Bayer, Johanna, Bijsterbosch, Janine, Algermissen, Johannes, Beck, Natacha, Bermudez, Patrick, Poyraz Bilgin, Isil, Bollmann, Steffen, Bradley, Claire, E.J. Campbell, Megan, Caron, Bryan, Civier, Oren, Pedro Coelho, Luis, El Damaty, Shady, Das, Samir, Dugré, Mathieu, Earl, Eric, Evas, Stefanie, Lopes Fischer, Nastassja, Fu Yap, De, G. Garner, Kelly ... P. Zwiers, Marcel (2023). Proceedings of the OHBM Brainhack 2021. OHBM Brainhack 2021, Online, 16-18 June 2021. Organization for Human Brain Mapping. doi: 10.52294/258801b4-a9a9-4d30-a468-c43646391211
2023
Journal Article
NeXtQSM—A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data
Cognolato, Francesco, O’Brien, Kieran, Jin, Jin, Robinson, Simon, Laun, Frederik B., Barth, Markus and Bollmann, Steffen (2023). NeXtQSM—A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data. Medical Image Analysis, 84 102700, 102700. doi: 10.1016/j.media.2022.102700
2023
Conference Publication
Isotropic QSM in seconds using super-resolution 2D EPI imaging in 3 orthogonal planes
Bachrata, Beata, Bollmann, Steffen, Grabner, Günther, Trattnig, Siegfried and Robinson, Simon (2023). Isotropic QSM in seconds using super-resolution 2D EPI imaging in 3 orthogonal planes. Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, London, United Kingdom, 7-12 May 2022. Concord, CA United States: International Society for Magnetic Resonance in Medicine. doi: 10.58530/2022/2362
2023
Conference Publication
Open-Source Hypothalamic-ForniX (OSHy-X) Atlases and Segmentation Tool for 3T and 7T
Chang, Jeryn, Steyn, Frederik, Ngo, Shyuan, Henderson, Robert, Guo, Christine, Bollmann, Steffen, Fripp, Jurgen, Barth, Markus and Shaw, Thomas (2023). Open-Source Hypothalamic-ForniX (OSHy-X) Atlases and Segmentation Tool for 3T and 7T. Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, London, United Kingdom, 7-12 May 2022. Concord, CA United States: International Society for Magnetic Resonance in Medicine. doi: 10.58530/2022/3808
2023
Journal Article
Deep learning based modelling of three-dimensional magnetic field
Nguyen, Van Tai, Bollmann, Steffen, Bermingham, Michael, Nguyen, Ha Xuan and Dargusch, Matthew S. (2023). Deep learning based modelling of three-dimensional magnetic field. Progress In Electromagnetics Research B, 100, 173-189. doi: 10.2528/pierb23051402
2023
Conference Publication
Evaluation of the REFILL dynamic distortion correction method for fMRI
Robinson, Simon, Bachrata, Beata, Eckstein, Korbinian, Dymerska, Barbara, Bollmann, Saskia, Bollmann, Steffen, Hodono, Shota, Cloos, Martijn, Tourell, Monique, Jin, Jin, O'Brien, Kieran, Reutens, David, Trattnig, Siegfried, Enzinger, Christian and Barth, Markus (2023). Evaluation of the REFILL dynamic distortion correction method for fMRI. Joint Annual Meeting ISMRM-ESMRMB ISMRT 31st Annual Meeting, London, United Kingdom, 7 - 12 May 2022. Berkeley, CA, United States: International Society for Magnetic Resonance in Medicine. doi: 10.58530/2022/2807
2022
Journal Article
Spikes with and without concurrent high-frequency oscillations: topographic relationship and neural correlates using EEG-fMRI
Urriola, Javier, Bollmann, Steffen, Tremayne, Fred, Burianová, Hana, Marstaller, Lars and Reutens, David (2022). Spikes with and without concurrent high-frequency oscillations: topographic relationship and neural correlates using EEG-fMRI. Epilepsy Research, 188 107039, 1-10. doi: 10.1016/j.eplepsyres.2022.107039
2022
Conference Publication
Investigating the computational reproducibility of Neurodesk
Dao, Thuy, Renton, Angela, Narayanan, Aswin, Barth, Markus and Bollmann, Steffen (2022). Investigating the computational reproducibility of Neurodesk. International Society for Magnetic Resonance in Medicine ANZ Chapter, Sydney, NSW, Australia, 12-13 November 2022.
2022
Journal Article
Efficient modelling of permanent magnet field distribution for deep learning applications
Nguyen, Van Tai, Bollmann, Steffen, Bermingham, Michael and Dargusch, Matthew S. (2022). Efficient modelling of permanent magnet field distribution for deep learning applications. Journal of Magnetism and Magnetic Materials, 559 169521, 1-12. doi: 10.1016/j.jmmm.2022.169521
2022
Journal Article
Open-Source Hypothalamic-ForniX (OSHy-X) Atlases and Segmentation Tool for 3T and 7T
Chang, Jeryn, Steyn, Frederik, Ngo, Shyuan, Henderson, Robert, Guo, Christine, Bollmann, Steffen, Fripp, Jurgen, Barth, Markus and Shaw, Thomas (2022). Open-Source Hypothalamic-ForniX (OSHy-X) Atlases and Segmentation Tool for 3T and 7T. Journal of Open Source Software, 7 (76), 4368. doi: 10.21105/joss.04368
2022
Conference Publication
Quantitative susceptibility mapping as an alternative to CT for localizing gold intraprostatic fiducial markers
Stewart, Ashley Wilton, Goodwin, Jonathan, Robinson, Simon Daniel, O’Brien, Kieran, Jin, Jin, Barth, Markus and Bollmann, Stefan (2022). Quantitative susceptibility mapping as an alternative to CT for localizing gold intraprostatic fiducial markers. ISMRM 2022, London, United Kingdom, 7-12 May 2022. Concord, CA USA: International Society for Magnetic Resonance in Medicine.
2022
Journal Article
Deep learning–based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data
Hanspach, Jannis, Bollmann, Steffen, Grigo, Johanna, Karius, Andre, Uder, Michael and Laun, Frederik B. (2022). Deep learning–based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data. Magnetic Resonance in Medicine, 88 (4), 1548-1560. doi: 10.1002/mrm.29265
2021
Journal Article
Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning
Ribeiro, Fernanda L., Bollmann, Steffen and Puckett, Alexander M. (2021). Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning. NeuroImage, 244 118624, 118624. doi: 10.1016/j.neuroimage.2021.118624
2021
Conference Publication
QSMxT: a fully automated, quantitative susceptibility mapping pipeline
Stewart, Ashley, Robinson, Simon Daniel, O’Brien, Kieran, Jin, Jin, Walls, Angela, Narayanan, Aswin, Barth, Markus and Bollman, Steffen (2021). QSMxT: a fully automated, quantitative susceptibility mapping pipeline. AusIron 2021, Brisbane, QLD Australia, 29 November 2021. Brisbane, QLD Australia: QIMR Berghofer Medical Research Institute.
2021
Conference Publication
QSMxT:a fully automated, quantitative susceptibility mapping pipeline
Stewart, Ashley Wilton, Robinson, Simon Daniel, O’Brien, Kieran, Jin, Jin, Walls, Angela, Narayanan, Aswin, Barth, Markus and Bollmann, Steffen (2021). QSMxT:a fully automated, quantitative susceptibility mapping pipeline. 3rd Annual Meeting of the ISMRM ANZ Chapter, 2021, Online, 22-23 November. International Society for Magnetic Resonance in Medicine ANZ Chapter.
2021
Conference Publication
QSM as an alternative to CT for localizing gold intraprostatic fiducial markers
Wilton Stewart, Ashley, Goodwin, Jonathan, Robinson, Simon Daniel, O’Brien, Kieran, Jin, Jin, Barth, Markus and Bollmann, Steffen (2021). QSM as an alternative to CT for localizing gold intraprostatic fiducial markers. 3rd Annual Meeting of the ISMRM ANZ Chapter, 2021, Online, 22-23 November 2021. International Society for Magnetic Resonance in Medicine ANZ Chapter.
2021
Journal Article
QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping
Stewart, Ashley Wilton, Robinson, Simon Daniel, O’Brien, Kieran, Jin, Jin, Widhalm, Georg, Hangel, Gilbert, Walls, Angela, Goodwin, Jonathan, Eckstein, Korbinian, Tourell, Monique, Morgan, Catherine, Narayanan, Aswin, Barth, Markus and Bollmann, Steffen (2021). QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping. Magnetic Resonance in Medicine, 87 (3), 1289-1300. doi: 10.1002/mrm.29048
2021
Journal Article
Deep learning in magnetic resonance image reconstruction
Chandra, Shekhar S., Bran Lorenzana, Marlon, Liu, Xinwen, Liu, Siyu, Bollmann, Steffen and Crozier, Stuart (2021). Deep learning in magnetic resonance image reconstruction. Journal of Medical Imaging and Radiation Oncology, 65 (5) 1754-9485.13276, 564-577. doi: 10.1111/1754-9485.13276
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
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
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Doctor Philosophy
Neural Network¿Enhanced Multimodal Brain Electrical Source Imaging and Applications
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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