Dr Steffen Bollmann

Research Fellow

School of Information Technology and Electrical Engineering
Faculty of Engineering, Architecture and Information Technology

Affiliated Research Fellow

Centre for Advanced Imaging
s.bollmann@uq.edu.au
+61 7 334 60360

Overview

After obtaining a Master degree in Biomedical Engineering at the Ilmenau University of Technology, I completed a PhD on multimodal imaging at the University Children’s Hospital and ETH Zurich, Switzerland. In 2014 I joined the Centre for Advanced Imaging at the University of Queensland as a National Imaging Facility Fellow, where I pioneered the application of deep learning methods for quantitative imaging techniques, in particular Quantitative Susceptibility Mapping, in the group of Prof Markus Barth.

In 2019 I joined the Siemens Healthineers collaborations team at the MGH Martinos Center in Boston during a 1 year industry exchange where I worked on the translation of fast imaging techniques into clinical applications.

Research Interests

  • Image Segmentation
    I am developing new methods to segment magnetic resonance imaging data to extract regional information from quantitative MRI scans.
  • Quantitative Susceptibility Mapping
    I am developing new methods to increase the robustness of processing quantitative susceptibility mapping.

Research Impacts

Since joining the School of Information Technology and Electrical Engineering at the University of Queensland in 2020 I am developing computational methods to extract clinical and biological insights from quantitative magnetic resonance imaging data. My aim is to make these cutting-edge tools available to a wide range of clinicians and researchers to enable better images, faster reconstruction times and the efficient extraction of relevant information.

Further information is available at www.mri.sbollmann.net and I regularly post updates on my research on twitter: https://twitter.com/sbollmann_MRI

Qualifications

  • PhD, Swiss Federal Institute of Technology, Zurich

Publications

View all Publications

Supervision

View all Supervision

Available Projects

  • Convolutional neural networks are particulary well suited to solve a variety of inverse problems in medical imaging. This project is a great chance to get involved in the field of medical image processing using deep learning techniques from image reconstruction, registration to segmentation. Prior knowledge in Python, Tensorflow/Keras, Pytorch, and Linux shell scripting are recommended.

  • This project utilizes our minimum deformation modeling pipeline (https://github.com/CAIsr/volgenmodel-nipype) to build multi-modal models of the human brain (https://www.sciencedirect.com/science/article/pii/S1046202315000110).

View all Available Projects

Publications

Journal Article

Conference Publication

Other Outputs

  • 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

  • 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

  • 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

PhD and MPhil Supervision

Current Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Convolutional neural networks are particulary well suited to solve a variety of inverse problems in medical imaging. This project is a great chance to get involved in the field of medical image processing using deep learning techniques from image reconstruction, registration to segmentation. Prior knowledge in Python, Tensorflow/Keras, Pytorch, and Linux shell scripting are recommended.

  • This project utilizes our minimum deformation modeling pipeline (https://github.com/CAIsr/volgenmodel-nipype) to build multi-modal models of the human brain (https://www.sciencedirect.com/science/article/pii/S1046202315000110).