Influence of signal bandwidth and phase delay on complex susceptibility based magnetic particle imaging

Jiang, Tao; Pi, Shiqiang; Shi, Jiangshan; Zhang, Pu; Liu, Wenzhong; Cheng, Jingjing

doi:10.1088/1361-6501/ab2d40

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…Magnetic particle imaging (MPI) directly measures the spatial distribution of MNP concentration using the magnetic response of the MNPs to drive and selection magnetic fields [24][25][26][27]. Furthermore, multi-color MPI, in principle, allows the imaging of biomolecules in addition to concentration via the measurements of nanoparticle relaxation [28,29].…”

Section: Introductionmentioning

confidence: 99%

Magnetic nanoparticle-based biomolecule imaging with a scanning magnetic particle spectrometer

2020

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This study reports on a wash-free, inexpensive and sensitive approach of biomolecule imaging with magnetic nanoparticles (MNPs) via a custom-built scanning magnetic particle spectrometer (SMPS). Streptavidin-coated MNPs are used as magnetic biomarkers for the detection of Immunoglobulin G (IgG) conjugated with biotin (IgG-Biotin) while five samples with differentconcentration IgG-Biotin are prepared for experiments. The measurements of the ac susceptibility indicate that the conjugation of the IgG-Biotin onto the surface of the MNPs forms cross-linking between the MNPs, thus increasing the characteristic Brownian relaxation time from 0.627 to 1.448 ms. The ratio of the 3rd to the 1st harmonics is measured on the samples with different-concentration IgG-Biotin in ac magnetic fields with a frequency ranging from about 300 Hz to 2 kHz. It shows that the measurement sensitivity of the IgG-Biotin concentration decreases from 4.62×10 -3 to 0.39×10 -3 nM −1 with increasing excitation frequency. Phantom images of the harmonic ratio, measured with the SMPS, indicate that unbound and bound MNPs can be easily distinguished. Furthermore, the excitation frequency dependence of the contrast-to-noise ratio of the images is discussed based on the measurement sensitivity and the standard deviation of the measured image intensity. This study demonstrates the feasibility of the SMPS for imaging biomolecules bound onto the MNPs, which is of great interest to disease diagnostics and therapy.

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Section: Introductionmentioning

confidence: 99%

Magnetic nanoparticle-based biomolecule imaging with a scanning magnetic particle spectrometer

2020

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“…Figure 3 shows the home-made csMPI scanner and the receiver chain [36,37]. The csMPI system is mainly composed of an excitation module, signal detection module, and signal processing module.…”

Section: Methodsmentioning

confidence: 99%

“…As previously shown [36,37], a complex susceptibilitybased MPI (csMPI) was proposed in previous studies to minimize the negative effects of relaxation on spatial resolution. A strong low-frequency focus field is combined with a weak high-frequency drive field in the csMPI.…”

Section: Introductionmentioning

confidence: 99%

An improved point spread function for complex susceptibility-based magnetic particle imaging

Jiang¹,

Yi²,

Du³

et al. 2022

Meas. Sci. Technol.

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Spatial resolution is a key metric for characterizing magnetic particle imaging (MPI), and magnetic relaxation is a critical factor affecting the spatial resolution. This study investigates the point spread functions (PSFs) of MPI and analyzes the potential of breaking through the spatial resolution limit, which equals the full width at half maximum (FWHM) of the Langevin function derivative. In this work, different PSFs of MPI were built based on the magnitude and real and imaginary parts of complex susceptibility. The imaging performance was evaluated using the FWHM and a self-defined convergence parameter. The results show that image reconstruction can achieve a narrower PSF based on the imaginary part of complex susceptibility, and the heavy-tailed distribution of the derivative of the Langevin curve can be optimized. This suggests that there is scope to improve the spatial resolution and image contrast of MPI.

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“…Some popular feature extraction methods entirely use the information extracted from data in various representations and multiple domains, including statistical features, power spectral density (PSD), fast Fourier transform, Hilbert transform, and entropy [15][16][17][18]. Profile envelopes, such as those derived from Hilbert and wavelet transforms, RMS, and peak values, offer valuable insights into the signal, but they are not suitable as inputs of the prediction model.…”

Section: Introductionmentioning

confidence: 99%

An improved hidden Markov model with magnetic Barkhausen noise and optimized Gaussian mixture feature for fatigue prediction

Li,

Guo,

Deng

et al. 2024

Meas. Sci. Technol.

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Evaluating fatigue states of metallic materials is essential for predicting their failures and ensuring structural safety. Magnetic Barkhausen noise (MBN) analysis, a non-destructive testing method, provides efficient and reliable methods for identifying and categorising material parameters such as hardness and residual stresses. To establish a quantitative relationship between MBN signals and fatigue states, an improved hidden Markov model (HMM) is proposed based on optimised Gaussian mixture features (GMFs) and the Kullback-Leibler (KL) divergence measure for fatigue prediction. The MBN-GMFs replicate the probability characteristics of MBN signals and track the fatigue degradation trend throughout the fatigue life; thus, they are superior to some widely used statistical features. A Gaussian component optimisation (GCO) algorithm is proposed to automatically adjust the appropriate number of components in the Gaussian mixture model (GMM) and enhance the representation of MBN-GMFs. Then, the KL divergence is introduced to quantify the similarity and accurately classify the degree of MBN-GMF migration. The HMM is constructed to obtain the probability transfer relationship between the observations and states and obtain accurate fatigue predictions. Experiments on two 20R metallic materials at three excitation frequencies are conducted to collect the MBN signals. The experimental results and comparisons indicate that the proposed HMM can accurately predict fatigue states and provide a practical and robust analysis tool for MBN-based fatigue predictions.

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Influence of signal bandwidth and phase delay on complex susceptibility based magnetic particle imaging

Cited by 6 publications

References 49 publications

Magnetic nanoparticle-based biomolecule imaging with a scanning magnetic particle spectrometer

Magnetic nanoparticle-based biomolecule imaging with a scanning magnetic particle spectrometer

An improved point spread function for complex susceptibility-based magnetic particle imaging

An improved hidden Markov model with magnetic Barkhausen noise and optimized Gaussian mixture feature for fatigue prediction

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