Localized signal suppression in magnetic particle imaging

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2026-02-14

Date

2025-08

Authors

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Advisor

Çukur, Emine Ülkü Sarıtaş

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Abstract

Magnetic Particle Imaging (MPI) is a tracer-based imaging modality offering high temporal and spatial resolution without any signal from the background tissue. MPIimages the spatial distribution of magnetic nanoparticles (MNPs). However, these MNPs can accumulate in off-target organs, such as the liver and spleen, and the resulting strong signal can obscure nearby target signals. This problem is fur ther exacerbated by the broad point spread function of the imaging system, which causes even distant off-target regions to interfere with the desired signal. This thesis proposes localized signal suppression through the use of additional DC saturation fields that locally saturate MNP magnetization. After deriving the relevant imaging equations, the requirements for the saturation field strength are determined via simulations. Next, several coil designs are evaluated for localized MNP saturation. Quantitative results show that there is a balance between sup pression efficiency and spatial spillover, guiding optimal coil design. Electromag netic interference caused by interactions between the saturation coil and the rest of the MPI hardware is investigated by analyzing the sources of secondary field inductions. Among several potential solutions to address this challenge, an L choke component is incorporated into the final circuitry to minimize interference. Imaging experiments on a custom MPI scanner demonstrate that the proposed approach effectively reduces off-target signals and enhances target-to-background contrast in MPI images. The results of this thesis demonstrate localized signal suppression as a viable and successful approach for improving contrast in MPI in the case of MNP accumulation in off-target tissues.

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Degree Discipline

Electrical and Electronic Engineering

Degree Level

Master's

Degree Name

MS (Master of Science)

Citation

Published Version (Please cite this version)

Language

English

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