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QSMxT: Robust Masking and Artefact Reduction for Quantitative Susceptibility Mapping

Ashley Wilton Stewart, Simon Daniel Robinson, Kieran O’Brien, Jin Jin, Georg Widhalm, Gilbert Hangel, Angela Walls, Jonathan Goodwin, Korbinian Eckstein, Monique Tourell, Catherine Morgan, Aswin Narayanan, Markus Barth, Steffen Bollmann
doi: https://doi.org/10.1101/2021.05.05.442850
Ashley Wilton Stewart
1Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia
2Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
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  • For correspondence: a.stewart.au@gmail.com
Simon Daniel Robinson
2Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
3High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
4School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
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Kieran O’Brien
1Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia
2Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
5Siemens Healthcare Pty Ltd, Brisbane, Australia
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Jin Jin
1Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia
2Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
5Siemens Healthcare Pty Ltd, Brisbane, Australia
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Georg Widhalm
6Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
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Gilbert Hangel
3High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
6Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
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Angela Walls
7Clinical & Research Imaging Centre, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
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Jonathan Goodwin
8Department of Radiation Oncology, Calvary Mater Hospital, Newcastle, New South Wales, Australia
9School of Mathematical and Physical Science, University of Newcastle, Newcastle, New South Wales, Australia
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Korbinian Eckstein
3High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
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Monique Tourell
1Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia
2Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
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Catherine Morgan
10School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
11Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
12Centre for Advanced MRI, The University of Auckland, Auckland, New Zealand
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Aswin Narayanan
1Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia
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Markus Barth
1Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia
2Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
4School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
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Steffen Bollmann
1Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia
2Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
4School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
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Abstract

Purpose Quantitative Susceptibility Mapping (QSM) is a post-processing technique applied to gradient-echo phase data. QSM algorithms require a signal mask to delineate regions with reliable phase signal for subsequent susceptibility estimation. Existing masking techniques used in QSM have limitations that introduce artefacts, exclude anatomical detail, and rely on parameter tuning and anatomical priors that narrow their application. Here, a robust masking and reconstruction procedure is presented to overcome these limitations and enable automated QSM processing for a wide range of use-cases implemented in an open-source software framework: QSMxT.

Methods A robust masking technique that automatically separates reliable from less reliable phase regions was developed and combined with a two-pass reconstruction procedure that operates on the separated sources before combination, extracting more information while reducing the influence of artefacts.

Result Compared with standard masking and reconstruction procedures, the two-pass inversion reduces streaking artefacts caused by unreliable phase and high dynamic ranges of susceptibility sources. QSMxT is robust across a range of datasets at 3 T in healthy volunteers and phantoms, at 7 T in tumour patients, and in the QSM challenge 2.0 simulated brain dataset, with significant artefact and error reductions, greater anatomical detail, and minimal parameter tuning.

Conclusion QSMxT generates masks for QSM that separate reliable from less reliable phase regions, enables a more accurate QSM reconstruction that mitigates artefacts, operates without anatomical priors, and requires minimal parameter tuning. QSMxT makes QSM processing more accessible, reliable and reproducible.

Competing Interest Statement

Kieran O'Brien and Jin Jin are employees of Siemens Healthineers in Australia.

Footnotes

  • Submitted to Magnetic Resonance in Medicine.

  • https://github.com/QSMxT/QSMxT

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 06, 2021.
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QSMxT: Robust Masking and Artefact Reduction for Quantitative Susceptibility Mapping
Ashley Wilton Stewart, Simon Daniel Robinson, Kieran O’Brien, Jin Jin, Georg Widhalm, Gilbert Hangel, Angela Walls, Jonathan Goodwin, Korbinian Eckstein, Monique Tourell, Catherine Morgan, Aswin Narayanan, Markus Barth, Steffen Bollmann
bioRxiv 2021.05.05.442850; doi: https://doi.org/10.1101/2021.05.05.442850
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QSMxT: Robust Masking and Artefact Reduction for Quantitative Susceptibility Mapping
Ashley Wilton Stewart, Simon Daniel Robinson, Kieran O’Brien, Jin Jin, Georg Widhalm, Gilbert Hangel, Angela Walls, Jonathan Goodwin, Korbinian Eckstein, Monique Tourell, Catherine Morgan, Aswin Narayanan, Markus Barth, Steffen Bollmann
bioRxiv 2021.05.05.442850; doi: https://doi.org/10.1101/2021.05.05.442850

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