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Determining the resolution of a tracer for magnetic particle imaging by means of magnetic particle spectroscopy

Magnetic particle imaging (MPI) is an imaging technique used to quantify the three-dimensional distribution of magnetic nanoparticles (MNPs) administered as a tracer into a biological system. Magnetic particle spectroscopy (MPS) is the zero‑dimensional MPI counterpart with much higher sensitivity but no spatial coding. In general, MPS is used to qualitatively assess the MPI capability of tracer systems based on specific harmonic spectra measurements. So far, it has not been shown which MPS parameters can be used to evaluate and assess MPI resolution.

 

In this work [1], we investigated the relation between three characteristic MPS parameters (the amplitude of the third harmonic normalized to the iron amount of the sample A3*, the (concentration independent) ratio between 5th and 3rd harmonic A5/A3, and the number of MPS harmonics Ak above the limit of detection (LOD) kA>LOD of the MPS device) and the achievable MPI resolution. The results showed that the number of harmonics above the limit of detection (kA>LOD) in the MPS spectra correlated strongly with the achievable MPI resolution. This suggests that the parameter kA>LOD can be utilized to predict the resolution of a tracer in MPI, eliminating the need for time-consuming acquisition of system functions (SF) in MPI. Consequently, our study provides valuable insights into tracer performance assessment and facilitates the evaluation of tracers for MPI performance prior to SF recording, thereby saving valuable time.

[1] Amani Remmo et al., Determining the resolution of a tracer for magnetic particle imaging by means of magnetic particle spectroscopy, RSC Advances 13 (23), 15730-15736 (2023), 10.1039/D3RA01394D

 


Figure 1: Specific MPS amplitude A3* (a), harmonic ratio A5/A3 (b), and number of harmonics above LOD kA>LOD (c) determined from MPS measurements as a function of the resolution r obtained by the two-voxel-analysis from MPI. 

 

Contact: Amani Remmo, 8.23

 

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