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Updated: Aug 5, 2026

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
Published on: February 27, 2011
Systematic evaluation of magnetic particle imaging quantification strategies in biologically relevant scenarios using
Daniela Paola Valdés1, Sarah N Zammataro2, Andrii Melnyk1
1Department of Chemical Engineering, University of Florida, Gainesville, FL 32611, United States of America.
Abstract:
Objective.Many applications of magnetic nanoparticles (MNPs) require accumulation at target organs that can be challenging to achieve and to characterize. Magnetic particle imaging (MPI) enables sensitive and quantitative detection of MNPs, however the proximity of target signals to high accumulation organs hinders accurate quantification due to signal spillover effects. Specifically, we sought to systematically evaluate MPI quantification strategies under conditions mimicking pre-clinical animal studies and account for spillover.Approach.We developed an anatomically accurate 3D-printed mouse phantom with a fillable liver cavity, brain and lung ports and a hind flank cavity. By emulating high liver MNP uptake alongside low MNP concentrations in target locations at varying distances, we compared quantification from threshold-based and constant-volume segmentations, as well as a subtraction approach, both for 2D and 3D MPI scans.Main results.Thresholding was reliable for isolated high-signal regions but overestimated signal near the liver or at low signal-to-noise ratios. Constant-volume segmentation improved signal separation, and subtraction strategies mitigated spillover overestimation, enhancing the limit of quantification. With the subtraction approach we were able to quantify as low a signal as 0.05 and 0.25% of the total dose in the phantom in the brain/lung and hind flank of the mouse phantom with 3D MPI, respectively.Significance.These results underscore the importance of accounting for superimposed signals in quantitative MPI and highlight anatomically correct phantoms as essential tools for refining biodistribution assessment methods in nanomedicine using MPI.

