Evaluating Distortions in fMRI Phantoms: The Impact of Air Bubbles on Imaging Accuracy
Poster No:
1622
Submission Type:
Abstract Submission
Authors:
Kristian Galea1, Brandon Seychell2, Therese Hunter2, Claude Bajada2,1
Institutions:
1University of Malta Magnetic Resonance Imaging Platform, University of Malta, Msida, Malta, 2Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
First Author:
Kristian Galea
University of Malta Magnetic Resonance Imaging Platform, University of Malta
Msida, Malta
University of Malta Magnetic Resonance Imaging Platform, University of Malta
Msida, Malta
Co-Author(s):
Brandon Seychell
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Therese Hunter
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Claude Bajada
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta|University of Malta Magnetic Resonance Imaging Platform, University of Malta
Msida, Malta|Msida, Malta
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta|University of Malta Magnetic Resonance Imaging Platform, University of Malta
Msida, Malta|Msida, Malta
Introduction:
fMRI is a non-invasive method for investigating and comprehending human brain function. fMRI uses fast imaging sequences like echo-planar imaging (EPI) which are sensitive to the BOLD effect; associated with neural activity (Cortese et al., 2021). Applications in fMRI span from diverse aspects of cognition to clinical (Merritt et al., 2021). However, clinical utility has been hindered by technical hurdles (Mizutani-Tiebel et al., 2022). One such issue is macroscopic susceptibility effects at the air/tissue interfaces creating image distortions and signal losses in EPI. This effect is called susceptibility artifacts which reduce the signal-to-noise ratio (Devlin et al., 2000). Moreover, reliability and validity of fMRI studies are dependent on the availability of robust fMRI quality assurance (QA) protocols (Lu et al., 2019). QA protocols typically use phantoms for assessing and optimising MRI scanner performance. Efforts to develop physical fMRI phantoms for QA include the phantom recipe of the Function Biomedical Informatics Research Network (FBIRN) (Keator et al., 2016). However, the shape of the FBIRN phantom is not brain-mimicking. Other studies have produced phantoms with an alterable signal but simplified brain contours (McIlvain et al., 2019). Another study created an anthropomorphic brain phantom without an alterable signal (Altermatt et al., 2019). This preliminary study aims to evaluate the extent of susceptibility distortions caused by air bubbles in the phantom solution. The findings emphasise the impact of air bubbles on system accuracy and suggest ways to improve the phantom for QA purposes. This research lays the groundwork for the development of brain-mimicking phantoms capable of modulating MRI signals to simulate dynamic fMRI time-series.
Methods:
The FBIRN phantom (Friedman and Clover, 2006) is made up of agar to simulate biological tissue, together with nickel chloride and sodium chloride to provide grey matter comparable relaxation values and mimic the RF load of a human head. To create the mold, a brain model was 3D printed using a participant's T1w image. Silicone rubber was poured over the print to form the mold. The final phantom solution was made up of 3% (w/v) agar, 0.5% (w/v) NaCl, and 2.18 mM NiCl2 dissolved in deionised water. The solution was autoclaved at 121oC and 15 PSI for 20 minutes to ensure sterility. After the solution was cooled down to 50oC, it was degassed using a vacuum pump. This step was used to minimise air in the phantom. The solution was then poured in the mold and allowed to gel. The resulting phantom was placed in a water-filled plastic container to further minimise susceptibility artifacts from air-phantom boundaries. The phantom was scanned using a 3T Siemens Vida MRI scanner. A T1w image was acquired using an MPRAGE sequence (13 mm3 resolution, TE=2.66 ms, TR=2190 ms, Inversion time=925 ms, α=8o, GRAPPA with R=2). A T2w image was acquired (13 mm3 resolution, TE=417 ms, TR=3140 ms, GRAPPA with R=2). Finally, the phantom was scanned using a gradient echo planar imaging (EPI) fMRI pulse sequence was obtained (1.83 mm3 isotropic resolution, TR=1840ms, TE=34ms, α=65o, SMS with MB=4).
Results:
Our results (Fig 1) demonstrate the successful production and application of a phantom in both structural and functional scans. Despite degassing procedures, residual air bubbles were observed, which appeared more pronounced in fMRI sequences (Fig 1C). This effect can be attributed to the differing magnetic susceptibility between air and agar, leading to increased distortion.
Conclusions:
An anthropomorphic brain phantom was developed and utilized for fMRI, laying the groundwork for improving existing phantom designs. Future advancements will focus on creating brain-mimicking phantoms capable of modifying MR signals to simulate dynamic time-series by incorporating specific chemicals and proteins.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Methods Development 2
Other Methods 1
Keywords:
FUNCTIONAL MRI
Other - Phantoms
1|2Indicates the priority used for review
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2. Cortese, S. et al. (2021) ‘Systematic Review and Meta-analysis: Resting-State Functional Magnetic Resonance Imaging Studies of Attention-Deficit/Hyperactivity Disorder’, Journal of the American Academy of Child and Adolescent Psychiatry. doi: 10.1016/j.jaac.2020.08.014.
3. Devlin, J. T. et al. (2000) ‘Susceptibility-induced loss of signal: Comparing PET and fMRI on a semantic task’, NeuroImage. doi: 10.1006/nimg.2000.0595.
4. Friedman, L. and Glover, G. H. (2006) ‘Report on a multicenter fMRI quality assurance protocol’, Journal of Magnetic Resonance Imaging. doi: 10.1002/jmri.20583.
5. Keator, D. B. et al. (2016) ‘The Function Biomedical Informatics Research Network Data Repository’, NeuroImage. doi: 10.1016/j.neuroimage.2015.09.003.
6. Lu, W. et al. (2019) ‘Quality assurance of human functional magnetic resonance imaging: A literature review’, Quantitative Imaging in Medicine and Surgery. doi: 10.21037/qims.2019.04.18.
7. McIlvain, G. et al. (2019) ‘Reliable preparation of agarose phantoms for use in quantitative magnetic resonance elastography’, Journal of the Mechanical Behavior of Biomedical Materials. doi: 10.1016/j.jmbbm.2019.05.001.
8. Merritt, C. C. et al. (2021) ‘The neural underpinnings of intergroup social cognition: An fMRI meta-analysis’, Social Cognitive and Affective Neuroscience. doi: 10.1093/scan/nsab034.
9. Mizutani-Tiebel, Y. et al. (2022) ‘Concurrent TMS-fMRI: Technical Challenges, Developments, and Overview of Previous Studies’, Frontiers in Psychiatry. doi: 10.3389/fpsyt.2022.825205.
Funding Sources
The study is financed by Xjenza Malta, for and on behalf of the Foundation for Science and Technology, through FUSION: Research Excellence Programme (Project: SARA, Grant ID: REP-2024-033).
Acknowledgements
The authors gratefully acknowledge the provision of scanning services by the University of Malta’s MRI Platform (UMRI).