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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

81 - 86 of 86 works

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.

When to perform channel combination in 7 Tesla quantitative susceptibility mapping?

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

Developmental Changes in Gamma-Aminobutyric Acid Levels in Attention-Deficit/hyperactivity Disorder

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

Age-dependent and -independent changes in attention-deficit/hyperactivity disorder (ADHD) during spatial working memory performance

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

Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD)

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

Coupling between resting cerebral perfusion and EEG

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

A GPU-accelerated Performance Optimized RAP-MUSIC Algorithm for Real-Time Source Localization

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

    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

  • Doctor Philosophy

    Structure-function brain network dynamics in post-stroke depression

    Associate Advisor

    Other advisors: Dr Lena Oestreich

Completed supervision

Media

Enquiries

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