Selection field generation using permanent magnets and electromagnets for a magnetic particle imaging scanner

Irfan, Muhammad; Doğan, Oğuz; Doǧan, N.; Bingölbali, A.

doi:10.1016/j.aej.2022.01.028

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…The MPI system has a resolution of 3.5 × 8.0 mm and an FOV of 2.5 cm × 5.0 cm [18]. Irfan et al presented a study on a selection field generation system that can be used for MPI with a selection field size of 4.3 T/m along three axes using a 10 cm-sized permanent magnet [21]. Although some studies have proposed solutions to generate a selection field of 7.0 T/m or more, it is difficult to find examples of actual application to MPI.…”

Section: Discussionmentioning

confidence: 99%

A Novel Field-Free Line Generator for Mechanically Scanned Magnetic Particle Imaging

Kim,

Jeong,

Seo

et al. 2024

Sensors

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In this study, we propose an efficient field-free line (FFL) generator for mechanically driven FFL magnetic particle imaging (MPI) applications. The novel FFL generator comprises pairs of Halbach arrays and bar magnets. The proposed design generates high-gradient FFLs with low-mass permanent magnets, realizing fine spatial resolutions in MPI. We investigate the magnetic field generated using simulations and experiments. Our results show that the FFL generator yields a high gradient of 4.76 T/m at a cylindrical field of view of 30 mm diameter and a 70 mm open bore. A spatial resolution of less than 3.5 mm was obtained in the mechanically driven FFL-MPI.

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Section: Discussionmentioning

confidence: 99%

A Novel Field-Free Line Generator for Mechanically Scanned Magnetic Particle Imaging

Kim,

Jeong,

Seo

et al. 2024

Sensors

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“…Versatile MPI scanners based on FFP, and FFL spatial encoding were designed and implemented at the preclinical stage around the world and a few of them is presented in Table III. The selection field is achieved with permanent magnets (NdFeB) and/or electromagnets [89]. Generally, the selection field is generated from two setups equal in magnitude but opposite in magnetic direction, so magnetic fields cancel each other at the midpoint of the geometry.…”

Section: Spatial Encodingmentioning

confidence: 99%

Comprehensive Evaluation of Magnetic Particle Imaging (MPI) Scanners for Biomedical Applications

Irfan

Doǧan

2022

IEEE Access

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Magnetic particle imaging (MPI) is an emerging tomographic imaging technique that tracks and quantitatively measures the spatial distribution of the superparamagnetic iron oxide nanoparticles (SPIONs). It is a radiation-free, background-free, and signal attenuation-free imaging modality that utilizes the non-linear behavior of the tracer response. The lowest acquisition time, high spatial resolution, and extreme sensitivity make it ideal for medical imaging in the future in comparison to magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). SPIONs are the main source of signal generation and have a significant influence on MPI characteristics. Many groups in the world are working to produce optimal tracer agents with a low toxicity profile for MPI applications. Versatile MPI scanners are developed and implemented at the pre-clinical stage to evaluate the performance of the system parameters. This review aims at giving an overview of the current developments and significant achievements of the tracers, imager design, image reconstruction, and potential applications of MPI since its first exposure to the scientific world in 2005.INDEX TERMS Computed tomography (CT), Magnetic particle imaging (MPI), Magnetic resonance imaging (MRI), Positron emission tomography (PET), Superparamagnetic iron oxide nanoparticles (SPIONs).

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Machine Learning and Deep Learning Applications in Magnetic Particle Imaging

Nigam,

Gjelaj,

Wang

et al. 2024

Magnetic Resonance Imaging

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In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non‐ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X‐space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence‐based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future.Level of Evidence5Technical EfficacyStage 1

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Selection field generation using permanent magnets and electromagnets for a magnetic particle imaging scanner

Cited by 8 publications

References 25 publications

A Novel Field-Free Line Generator for Mechanically Scanned Magnetic Particle Imaging

A Novel Field-Free Line Generator for Mechanically Scanned Magnetic Particle Imaging

Comprehensive Evaluation of Magnetic Particle Imaging (MPI) Scanners for Biomedical Applications

Machine Learning and Deep Learning Applications in Magnetic Particle Imaging

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