Issue 25, 2024

Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications

Abstract

Recent advancements in medical imaging have brought forth various techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound, each contributing to improved diagnostic capabilities. Most recently, magnetic particle imaging (MPI) has become a rapidly advancing imaging modality with profound implications for medical diagnostics and therapeutics. By directly detecting the magnetization response of magnetic tracers, MPI surpasses conventional imaging modalities in sensitivity and quantifiability, particularly in stem cell tracking applications. Herein, this comprehensive review explores the fundamental principles, instrumentation, magnetic nanoparticle tracer design, and applications of MPI, offering insights into recent advancements and future directions. Novel tracer designs, such as zinc-doped iron oxide nanoparticles (Zn-IONPs), exhibit enhanced performance, broadening MPI's utility. Spatial encoding strategies, scanning trajectories, and instrumentation innovations are elucidated, illuminating the technical underpinnings of MPI's evolution. Moreover, integrating machine learning and deep learning methods enhances MPI's image processing capabilities, paving the way for more efficient segmentation, quantification, and reconstruction. The potential of superferromagnetic iron oxide nanoparticle chains (SFMIOs) as new MPI tracers further advanced the imaging quality and expanded clinical applications, underscoring the promising future of this emerging imaging modality.

Graphical abstract: Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications

Article information

Article type
Review Article
Submitted
18 Mar 2024
Accepted
23 May 2024
First published
24 May 2024

Nanoscale, 2024,16, 11802-11824

Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications

B. Rezaei, Z. W. Tay, S. Mostufa, O. N. Manzari, E. Azizi, S. Ciannella, H. Moni, C. Li, M. Zeng, J. Gómez-Pastora and K. Wu, Nanoscale, 2024, 16, 11802 DOI: 10.1039/D4NR01195C

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