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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

41 - 60 of 86 works

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

Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective

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

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

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.

Robust masking techniques for multi-echo quantitative susceptibility mapping

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.

Submillimeter, aub-minute quantitative susceptibility mapping using a multi-shot 3D-EPI with 2D CAIPIRINHA acceleration

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.

QSMxT - a cross-platform, flexible, lightweight, and scalable processing pipeline for quantitative susceptibility mapping

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

Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power through deep learning estimation

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

MRI phase offset correction method impacts quantitative susceptibility mapping

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

Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI

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

Towards optimising MRI characterisation of Tissue (TOMCAT) dataset including all Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) data

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

Influence of 7T GRE-MRI signal compartment model choice on tissue parameters

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

Overview of quantitative susceptibility mapping using deep learning: current status, challenges and opportunities

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

Functional connectivity of the irritative zone identified by electrical source imaging, and EEG-correlated fMRI analyses

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

Non-linear realignment improves hippocampus subfield segmentation reliability

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

7T GRE-MRI signal compartments are sensitive to dysplastic tissue in focal epilepsy

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

DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping

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.

Quantitative susceptibility mapping for routine clinical use – an inline automated QSM reconstruction pipeline

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

SHARQnet – Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network

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.

Improving FLAIR SAR efficiency by predicting B1-maps at 7T from a standard localizer scan using deep convolutional neural networks

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.

Myelin water fraction across the corpus callosum using multi-echo gradient echo at 7T - influence of model settings and flip angle

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.

7T GRE-MRI frequency shifts obtained from signal compartments can differentiate normal from dysplastic tissue in focal epilepsy

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

    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

  • Doctor Philosophy

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

    Associate Advisor

    Other advisors: Professor Markus Barth

Completed supervision

Media

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