International Journal on Magnetic Particle Imaging IJMPI
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)

Copyright (c) 2022 John Drago, Erica Mason, Eli Mattingly, Monika ?liwiak, Lawrence Wald

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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

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