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Dr Steffen Bollmann
Dr

Steffen Bollmann

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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

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)

Works

Search Professor Steffen Bollmann’s works on UQ eSpace

86 works between 2012 and 2025

21 - 40 of 86 works

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

Variability of visual field maps in human early extrastriate cortex challenges the canonical model of organization of V2 and V3

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.

Investigating the computational reproducibility of Neurodesk

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

Proceedings of the OHBM Brainhack 2021

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

NeXtQSM—A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data

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

Isotropic QSM in seconds using super-resolution 2D EPI imaging in 3 orthogonal planes

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

Open-Source Hypothalamic-ForniX (OSHy-X) Atlases and Segmentation Tool for 3T and 7T

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

Deep learning based modelling of three-dimensional magnetic field

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

Evaluation of the REFILL dynamic distortion correction method for fMRI

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

Spikes with and without concurrent high-frequency oscillations: topographic relationship and neural correlates using EEG-fMRI

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.

Investigating the computational reproducibility of Neurodesk

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

Efficient modelling of permanent magnet field distribution for deep learning applications

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

Open-Source Hypothalamic-ForniX (OSHy-X) Atlases and Segmentation Tool for 3T and 7T

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.

Quantitative susceptibility mapping as an alternative to CT for localizing gold intraprostatic fiducial markers

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

Deep learning–based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data

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

Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning

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.

QSMxT: a fully automated, quantitative susceptibility mapping pipeline

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.

QSMxT:a fully automated, quantitative susceptibility mapping pipeline

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.

QSM as an alternative to CT for localizing gold intraprostatic fiducial markers

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

QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping

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

Deep learning in magnetic resonance image reconstruction

Funding

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

Supervision

Availability

Dr Steffen Bollmann is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

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

Media

Enquiries

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