Dr Steffen Bollmann
Dr

Steffen Bollmann

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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

Biomedical and Clinical Sciences Clinical sciences Engineering Image processing Information and Computing Sciences Modelling and simulation Neurosciences

Research interests

  • Reproducible Research Software

    Developing software to enable reproducible neuroimaging, such as www.Neurodesk.org

  • Computational Imaging

    Developing tools to make computational algorithms for medical imaging more accessible and robust.

  • Image Segmentation

    Developing new methods to segment medical imaging data to extract quantitative information.

  • 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)

Search Professor Steffen Bollmann’s works on UQ eSpace

86 works between 2012 and 2025
All (86) Journal Article (48) Conference Publication (34) Other Outputs (4)

61 - 80 of 86 works

2017

Journal Article

Assessment of microstructural signal compartments across the corpus callosum using multi-echo gradient recalled echo at 7 T

Thapaliya, Kiran, Vegh, Viktor, Bollmann, Steffen and Barth, Markus (2017). Assessment of microstructural signal compartments across the corpus callosum using multi-echo gradient recalled echo at 7 T. NeuroImage, 182, 407-416. doi: 10.1016/j.neuroimage.2017.11.029

Assessment of microstructural signal compartments across the corpus callosum using multi-echo gradient recalled echo at 7 T

2017

Journal Article

Real-Time Clustered Multiple Signal Classification (RTC-MUSIC)

Dinh, Christoph, Esch, Lorenz, Rühle, Johannes, Bollmann, Steffen, Güllmar, Daniel, Baumgarten, Daniel, Hämäläinen, Matti S. and Haueisen, Jens (2017). Real-Time Clustered Multiple Signal Classification (RTC-MUSIC). Brain Topography, 31 (1), 125-128. doi: 10.1007/s10548-017-0586-7

Real-Time Clustered Multiple Signal Classification (RTC-MUSIC)

2017

Journal Article

The challenge of bias-free coil combination for quantitative susceptibility mapping at ultra-high field

Bollmann, Steffen, Robinson, Simon Daniel, O'Brien, Kieran, Vegh, Viktor, Janke, Andrew, Marstaller, Lars, Reutens, David and Barth, Markus (2017). The challenge of bias-free coil combination for quantitative susceptibility mapping at ultra-high field. Magnetic Resonance in Medicine, 79 (1), 97-107. doi: 10.1002/mrm.26644

The challenge of bias-free coil combination for quantitative susceptibility mapping at ultra-high field

2017

Journal Article

The PhysIO toolbox for modeling physiological noise in fMRI data

Kasper, Lars, Bollmann, Steffen, Diaconescu, Andreea O., Hutton, Chloe, Heinzle, Jakob, Iglesias, Sandra, Hauser, Tobias U., Sebold, Miriam, Manjaly, Zina-Mary, Pruessmann, Klaas P. and Stephan, Klaas E. (2017). The PhysIO toolbox for modeling physiological noise in fMRI data. Journal of Neuroscience Methods, 276, 56-72. doi: 10.1016/j.jneumeth.2016.10.019

The PhysIO toolbox for modeling physiological noise in fMRI data

2017

Other Outputs

MP2RAGE T1-weighted average 7T model

Bollmann, Steffen, Janke, Andrew, Marstaller, Lars, Reutens, David, O'Brien, Kieran and Barth, Markus (2017). MP2RAGE T1-weighted average 7T model. The University of Queensland. (Dataset) doi: 10.14264/uql.2017.266

MP2RAGE T1-weighted average 7T model

2017

Other Outputs

GRE and QSM average 7T model

Bollmann, Steffen, Janke, Andrew, Marstaller, Lars, Reutens, David, O'Brien, Kieran and Barth, Markus (2017). GRE and QSM average 7T model. The University of Queensland. (Dataset) doi: 10.14264/uql.2017.178

GRE and QSM average 7T model

2017

Conference Publication

Deep Mapping: Using deep convolutional neural networks to estimate quantitative T1 maps trained on a 7 T minimum deformation average model

Bollmann, Steffen, Andrew Janke and Markus Barth (2017). Deep Mapping: Using deep convolutional neural networks to estimate quantitative T1 maps trained on a 7 T minimum deformation average model. ISMRM, Honolulu, 22-27 April 2017.

Deep Mapping: Using deep convolutional neural networks to estimate quantitative T1 maps trained on a 7 T minimum deformation average model

2017

Other Outputs

Turbo Spin Echo average 7T model

Bollmann, Steffen, Janke, Andrew, Marstaller, Lars, Reutens, David, O'Brien, Kieran and Barth, Markus (2017). Turbo Spin Echo average 7T model. The University of Queensland. (Dataset) doi: 10.14264/uql.2017.267

Turbo Spin Echo average 7T model

2017

Conference Publication

Non-Linear Realignment Using Minimum Deformation Averaging for Single-Subject FMRI at Ultra-High Field

Saskia Bollmann, Steffen Bollmann, Alex Puckett, Andrew Janke and Markus Barth (2017). Non-Linear Realignment Using Minimum Deformation Averaging for Single-Subject FMRI at Ultra-High Field. ISMRM, Honolulu, 22-27 April 2017.

Non-Linear Realignment Using Minimum Deformation Averaging for Single-Subject FMRI at Ultra-High Field

2017

Conference Publication

Contrast Matching of Ultra-High Resolution Minimum Deformation Averaged MRI Models to Facilitate Computation of a Multi-Modal Model of the Human Brain

J. Munk, N. Jacobsen, M. Plocharski, L. R. Østergaard, M. Barth, A. Janke and S. Bollmann (2017). Contrast Matching of Ultra-High Resolution Minimum Deformation Averaged MRI Models to Facilitate Computation of a Multi-Modal Model of the Human Brain. ISMRM, Honolulu, 22-27 April 2017.

Contrast Matching of Ultra-High Resolution Minimum Deformation Averaged MRI Models to Facilitate Computation of a Multi-Modal Model of the Human Brain

2017

Conference Publication

Signal Compartments Mapped from Multi-Echo Gradient Recalled Echo Data Vary across the Corpus Callosum

Kiran Thapaliya, S. Bollmann, V. Vegh and M. Barth (2017). Signal Compartments Mapped from Multi-Echo Gradient Recalled Echo Data Vary across the Corpus Callosum. ISMRM, Honolulu, 22-27 April 2017.

Signal Compartments Mapped from Multi-Echo Gradient Recalled Echo Data Vary across the Corpus Callosum

2016

Journal Article

Accelerated mapping of magnetic susceptibility using 3D planes-on-a-paddlewheel (POP) EPI at ultra-high field strength

Stäb, Daniel, Bollmann, Steffen, Langkammer, Christian, Bredies, Kristian and Barth, Markus (2016). Accelerated mapping of magnetic susceptibility using 3D planes-on-a-paddlewheel (POP) EPI at ultra-high field strength. NMR in Biomedicine, 30 (4) e3620, e3620. doi: 10.1002/nbm.3620

Accelerated mapping of magnetic susceptibility using 3D planes-on-a-paddlewheel (POP) EPI at ultra-high field strength

2016

Journal Article

Echo time-dependent quantitative susceptibility mapping contains information on tissue properties

Sood, Surabhi, Urriola, Javier, Reutens, David C., O'Brien, Kieran, Bollmann, Steffen, Barth, Markus and Vegh, Viktor (2016). Echo time-dependent quantitative susceptibility mapping contains information on tissue properties. Magnetic Resonance in Medicine, 77 (5), 1946-1958. doi: 10.1002/mrm.26281

Echo time-dependent quantitative susceptibility mapping contains information on tissue properties

2016

Journal Article

Pulsed arterial spin labelling at ultra-high field with a B1 +-optimised adiabatic labelling pulse

Zimmer, Fabian, O'Brien, Kieran, Bollmann, Steffen, Pfeuffer, Josef, Heberlein, Keith and Barth, Markus (2016). Pulsed arterial spin labelling at ultra-high field with a B1 +-optimised adiabatic labelling pulse. Magnetic Resonance Materials in Physics, Biology and Medicine, 29 (3), 463-473. doi: 10.1007/s10334-016-0555-2

Pulsed arterial spin labelling at ultra-high field with a B1 +-optimised adiabatic labelling pulse

2016

Conference Publication

Contribution of cortical layer cytoarchitecture to quantitative susceptibility mapping

Sood, Surabhi, Urriola, Javier, Reutens, David C., Bollmann, Steffen, O'Brien, Kieran, Barth, Markus and Vegh, Viktor (2016). Contribution of cortical layer cytoarchitecture to quantitative susceptibility mapping. Organisation of the Human Brain Mapping, Geneva, Switzerland, 26-30 June, 2016.

Contribution of cortical layer cytoarchitecture to quantitative susceptibility mapping

2016

Conference Publication

Selective combination of MRI phase images

Vegh, Viktor, O'Brien, Kieran, Reutens, David C., Bollmann, Steffen and Barth, Markus (2016). Selective combination of MRI phase images. International Symposium on Magnetic Resonance in Medicine, Singapore, 7-13 May, 2016.

Selective combination of MRI phase images

2016

Conference Publication

Echo time based influences on quantitative susceptibility mapping

Sood, Surabhi, Urriola, Javier, Reutens, David C., Bollmann, Steffen, O'Brien, Kieran, Barth, Markus and Vegh, Viktor (2016). Echo time based influences on quantitative susceptibility mapping. International Symposium on Magnetic Resonance in Medicine, Singapore, May.

Echo time based influences on quantitative susceptibility mapping

2016

Conference Publication

A 7T Human Brain Microstructure Atlas by Minimum Deformation Averaging at 300μm

Janke, Andrew L ., O'Brian, Kieran, Bollmann, Steffen, Kober, Tobias and Barth, Markus (2016). A 7T Human Brain Microstructure Atlas by Minimum Deformation Averaging at 300μm. International Society for Magnetic Resonance in Medicine, Singapore, 7-13 May 2016.

A 7T Human Brain Microstructure Atlas by Minimum Deformation Averaging at 300μm

2015

Journal Article

Effects of Steroid Hormones on Sex Differences in Cerebral Perfusion

Ghisleni, Carmen, Bollmann, Steffen, Biason-Lauber, Anna, Poil, Simon-Shlomo, Brandeis, Daniel, Martin, Ernst, Michels, Lars, Hersberger, Martin, Suckling, John, Klaver, Peter and O'Gorman, Ruth L. (2015). Effects of Steroid Hormones on Sex Differences in Cerebral Perfusion. PLoS One, 10 (9) e0135827, e0135827-e0135827. doi: 10.1371/journal.pone.0135827

Effects of Steroid Hormones on Sex Differences in Cerebral Perfusion

2015

Journal Article

Subcortical Glutamate Mediates the Reduction of Short-Range Functional Connectivity with Age in a Developmental Cohort

Ghisleni, Carmen, Bollmann, Steffen, Poil, Simon-Shlomo, Brandeis, Daniel, Martin, Ernst, Michels, Lars, O’Gorman, Ruth L. and Klaver, Peter (2015). Subcortical Glutamate Mediates the Reduction of Short-Range Functional Connectivity with Age in a Developmental Cohort. The Journal of Neuroscience, 35 (22), 8433-8441. doi: 10.1523/JNEUROSCI.4375-14.2015

Subcortical Glutamate Mediates the Reduction of Short-Range Functional Connectivity with Age in a Developmental Cohort

Current funding

  • 2025 - 2027
    Towards Standards and Benchmarks for Reproducible Neuroimaging Research
    ARC Discovery Projects
    Open grant
  • 2024 - 2026
    Neurodesk: a software platform for reproducible neuroimaging
    Wellcome Trust Discretionary Award
    Open grant

Past funding

  • 2024
    Proof of concept study for replacing preoperative spine CT with MRI scans (QDHeC Sub-Project 004.A)
    Stryker European Operations Ltd
    Open grant
  • 2023
    Research advisory for University of South Carolina's 'Improving usage of the Aphasia Research Cohort (ARC) repository' project
    University of South Carolina
    Open grant
  • 2022 - 2025
    Robust, valid and interpretable deep learning for quantitative imaging
    ARC Linkage Projects
    Open grant
  • 2021 - 2022
    Translating deep learning models into medical imaging applications using secure cloud computing.
    UQ Knowledge Exchange & Translation Fund
    Open grant
  • 2021 - 2023
    Australian Electrophysiology Data Analytics PlaTform (AEDAPT) (ARDC grant administered by Swinburne University of Technology)
    Swinburne University of Technology
    Open grant
  • 2017 - 2024
    ARC Training Centre for Innovation in Biomedical Imaging Technology
    ARC Industrial Transformation Training Centres
    Open grant
  • 2015 - 2017
    Integrating high resolution anatomy, structural and functional connectivity with EEG at 7T: Towards biomarkers for neurodegenerative diseases
    UQ Postdoctoral Research Fellowship
    Open grant

Availability

Dr Steffen Bollmann is:
Available for supervision

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Available projects

  • 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.

  • 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.

  • 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

  • Doctor Philosophy

    Robust Deep learning for Quantitative Susceptibility Mapping

    Principal Advisor

    Other advisors: Dr Fernanda Lenita Ribeiro

  • Doctor Philosophy

    Computational Medical Imaging

    Principal Advisor

    Other advisors: Dr Fernanda Lenita Ribeiro, Mr Aswin Narayanan

  • Doctor Philosophy

    Structure-function brain network dynamics in post-stroke depression

    Associate Advisor

    Other advisors: Dr Lena Oestreich

  • Doctor Philosophy

    Development of a deep learning framework for multi-modal medical imaging

    Associate Advisor

    Other advisors: Professor Markus Barth

  • Doctor Philosophy

    Neural Network¿Enhanced Multimodal Brain Electrical Source Imaging and Applications

    Associate Advisor

    Other advisors: Professor Markus Barth

Completed supervision

Enquiries

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