International Journal on Magnetic Particle Imaging
Vol 8 No 1 Suppl 1 (2022): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2022.2203047

Proceedings Articles

Image Time-Series Stability for MPI-Based Functional Neuroimaging

Main Article Content

John M. Drago  (Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA; Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Boston, MA, USA), Erica E. Mason (Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Boston, MA, USA), Eli Mattingly (Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard-MIT Health Sciences and Technology, Cambridge, MA, USA), Monika Śliwiak  (Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Boston, MA, USA), Lawrence L. Wald  (Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard-MIT Health Sciences and Technology, Cambridge, MA, USA)

Abstract




The temporal stability of an image time-series becomes a critical performance metric when MPI is used as a functional neuroimaging modality. We apply an existing framework for assessing time-series variance from the functional MRI (fMRI) literature to phantom MPI time-series images. In this framework, sources of time-series variance are divided into those arising from thermal noise sources (which do not scale with the signal level) and intensity variations that scale with the signal level. The latter are often thought of as “physiological noise” if they arise from physiological processes or as instrumental “nuisance fluctuations” when arising from instrumental instabilities, such as system gain fluctuations. We analyze the phantom imaging time-series stability of a rodent-sized field-free line (FFL) MPI scanner and assess the relative contributions of these two variance sources to the time-series by varying the super-paramagnetic iron-oxide nanoparticle (SPION) concentration. These measurements permit characterization of our system’s time-series noise, identify future areas of improvement, and suggest the signal levels at which the time-series will be dominated by instrumental instabilities rather than thermal noise or physiological modulations.


Article Details

References

[1] T. Kim, K. S. Hendrich, K. Masamoto, and S.-G. Kim. Arterial versus total blood volume changes during neural activity-induced cerebral blood flow change: Implication for bold fmri. Journal of Cerebral Blood Flow & Metabolism, 27(6):1235–1247, 2007.

[2] G.Krüger and G. H. Glover. Physiological noise in oxygenation- sensitive magnetic resonance imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Mag- netic Resonance in Medicine, 46(4):631–637, 2001.

[3] C.Triantafyllou, R. D. Hoge, G. Krueger, C. J. Wiggins, A. Potthast, G. C. Wiggins, and L. L. Wald. Comparison of physiological noise at 1.5 t, 3 t and 7 t and optimization of fmri acquisition parameters. Neuroimage, 26(1):243–250, 2005.

[4] C. Z. Cooley, J. B. Mandeville, E. E. Mason, E. T. Mandeville, and L. L. Wald. Rodent cerebral blood volume (cbv) changes during hyper- capnia observed using magnetic particle imaging (mpi) detection. NeuroImage, 178:713–720, 2018.

[5] K. Herb, E. Mason, E. Mattingly, J. Mandeville, E. Mandeville, C. Cooley, and L. Wald. Functional mpi (fmpi) of hypercapnia in ro- dent brain with mpi time-series imaging. International Journal on Magnetic Particle Imaging, 6(2 Suppl 1), 2020.

[6] E. Mattingly, E. Mason, K. Herb, M. S ́liwiak, K. Brandt, C. Cooley, and L. Wald. Os-mpi: An open-source magnetic particle imaging project. International Journal on Magnetic Particle Imaging, 6(2 Suppl 1), 2020.

[7] A. M. Smith, B. K. Lewis, U. E. Ruttimann, Q. Y. Frank, T. M. Sinnwell, Y. Yang, J. H. Duyn, and J. A. Frank. Investigation of low frequency drift in fmri signal. Neuroimage, 9(5):526–533, 1999.

[8] R. Kopel, R. Sladky, P. Laub, Y. Koush, F. Robineau, C. Hutton, N. Weiskopf, P. Vuilleumier, D. Van De Ville, and F. Scharnowski. No time for drifting: Comparing performance and applicability of sig- nal detrending algorithms for real-time fmri. NeuroImage, 191:421– 429, 2019.