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| 4826.
 | A realistic in-silico brain phantom for magnetic susceptibility-separation algorithm validation Daniel Ridani1, Benjamin De-Leener1,2,3, and Eva Alonso-Ortiz1,3,4
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada, 2Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada, 3CHU Sainte-Justine Research Center, Montreal, QC, Canada, 4Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC, Canada Keywords: Susceptibility/QSM, Quantitative Imaging, Phantoms, QSM, Simulation, Susceptibility, Anisotropy Motivation: Positive and negative susceptibility mapping is an emerging method that can benefit from the availability of validation tools. Goal(s): To create an in-silico brain phantom for positive and negative susceptibility and to assess the impact of white matter’s anisotropic susceptibility on susceptibility-separation techniques. Approach: Simulate positive and negative susceptibility maps and gradient-echo data with/without anisotropy. Process simulated data with different susceptibility-separation algorithms. Compare the results with the ground truth. Results: The error associated with negative susceptibility measurements is ~9% greater when anisotropy effects are present in the phantom, suggesting that a new susceptibility-separation algorithm that considers myelin’s anisotropic susceptibility may be warranted. Impact: Researchers developing novel magnetic susceptibility-separation methods can use our proposed phantom to test different aspects of their technique, ranging from the biophysical model to image processing methods and imaging protocol parameters. |
| 4827.
 | QSM-CI: An automated continuous QSM challenge Ashley Wilton Stewart1, Korbinian Eckstein2, Thuy Thanh Dao1, and Steffen Bollmann2,3
1School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia, 2School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, Australia, 3Queensland Digital Health Centre, The University of Queensland, Brisbane, Australia Keywords: Electromagnetic Tissue Properties, Challenges, Quantitative Susceptibility Mapping Motivation: Reconstruction challenges for Quantitative Susceptibility Mapping (QSM) offer a common evaluation but are challenging to run and offer only a single snapshot of algorithm performance in time. Goal(s): To develop a QSM challenge with continuous integration (QSM-CI), enabling automatic, transparent evaluation of community-submitted algorithms across diverse datasets and metrics. Approach: QSM-CI was implemented using GitHub Actions and a user interface for displaying metrics and collecting community visual ratings, which inform a qualitative Elo metric alongside quantitative assessments. Results: The QSM-CI prototype implementation is publicly available and has been tested using a range of QSM algorithms. Impact: QSM-CI will facilitate a QSM challenge that allows for continuous evaluations using current and future datasets, algorithms, and metrics. This ensures the continued accessibility of the challenge and continued relevance as new methods, metrics and test data are made available. |
| 4828.
 | Improved QSM Pipeline to Investigate the Effect of Sickle Cell Anaemia on Brain Magnetic Susceptibility in Tanzanian Children at 1.5 Tesla Mitchel Lee1, Russell Murdoch1, Mboka Jakob2, Fenella Kirkham3, and Karin Shmueli1
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Department of Radiology & Imaging, Muhimbili University of Health and Allied Sciences, Dar Es Salaam, Tanzania, 3Imaging and Biophysics, Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, United Kingdom Keywords: Gray Matter, Gray Matter Motivation: Sickle cell anaemia (SCA) is a major global health burden, but disease mechanisms in the brain are not well understood. Goal(s): To improve a quantitative susceptibility mapping (QSM) pipeline and apply it to updated data to investigate brain susceptibility differences between SCA patients and controls. To investigate correlations between blood haemoglobin and brain magnetic susceptibility. Approach: QSM was optimised using denoising/masking approaches. Linear regressions of susceptibility against log(age) were used to compare age-corrected susceptibility in grey matter structures across the age range and correlate with haemoglobin. Results: Susceptibility increases with age differently for SCA vs controls. Haemoglobin was not significantly correlated with susceptibility. Impact: This work provides novel insight into the relationship between grey matter magnetic susceptibility and sickle cell anaemia, demonstrating differential trajectories with age between SCA patients and healthy controls. This may support the view of SCA as an accelerated aging syndrome. |
| 4829.
 | Open-Source Algorithm for Automatic Magnetic Susceptibility Determination from Field Maps Niklas Wehkamp1, Philipp Rovedo1, Jochen Leupold1, Sebastien Bär1, and Maxim Zaitsev1
1Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany Keywords: Software Tools, Susceptibility Motivation: The magnetic susceptibility is a fundamental material property for MR and NMR equipment engineering. The literature provides several theoretical solutions to measure the magnetic susceptibility. However, an openly available implementation that allows to determine the magnetic susceptibility automatically is missing.
Goal(s): Develop a non-proprietary approach to determine the magnetic susceptibility from measured field maps.
Approach: Measure the field map of a cylindrical sample. Develop a Python program to extract the magnetic susceptibility of the sample.
Results: The measured reference samples reflect the magnetic susceptibility of the literature. Code for data processing is available through the open access repository.
Impact: Our
research provides a programmatic
solution to automatically
determine the magnetic susceptibility of cylindrical samples from
field map measurements in MRI
systems. This
will aid MR
and NMR equipment engineers
to measure the magnetic
susceptibility of
any
material of interest. |