Gradient-Based Pulsed Excitation and Relaxation Encoding in Magnetic Particle Imaging

Jia, Guang; Huang, Liyu; Wang, Ze; Liang, Xiaofeng; Zhang, Yifei; Miao, Qiguang; Hu, Kai; Li, Tanping; Wang, Ying; Xi, Li; Feng, Xizeng; Hui, Hui

doi:10.1109/tmi.2022.3193219

Cited by 8 publications

(3 citation statements)

References 51 publications

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“…In this study, the frequency of the trapezoidal waveform was fixed to 2 kHz with a 5% rise phase and a 5% decrease phase per period, corresponding to a 90% flat portion to guarantee a strong signal. 30 The samples were excited at different field amplitudes rang-ing from 0.5 to 10 mT at intervals of 0.5 mT. All the experiments were conducted at 25 • C. An infrared thermometer was used to monitor the temperature of the sample.…”

Section: Trapezoidal-waveform Relaxometrymentioning

confidence: 99%

Relaxation spectral analysis in multi‐contrast vascular magnetic particle imaging

Feng,

Jia,

Peng

et al. 2023

Medical Physics

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Background: Magnetic nanoparticles (MNPs) are used as tracers without ionizing radiation in vascular imaging, molecular imaging, and neuroimaging. The relaxation mechanisms of magnetization in response to excitation magnetic fields are important features of MNPs. The basic relaxation mechanisms include internal rotation (Néel relaxation) and external physical rotation (Brownian relaxation). Accurate measurement of these relaxation times may provide high sensitivity for predicting MNP types and viscosity-based hydrodynamic states. It is challenging to separately measure the Néel and Brownian relaxation components using sinusoidal excitation in conventional MPI. Purpose: We developed a multi-exponential relaxation spectral analysis method to separately measure the Néel and Brownian relaxation times in the magnetization recovery process in pulsed vascular MPI. Methods: Synomag-D samples with different viscosities were excited using pulsed excitation in a trapezoidal-waveform relaxometer. The samples were excited at different field amplitudes ranging from 0.5 to 10 mT at intervals of 0.5 mT. The inverse Laplace transform-based spectral analysis of the relaxationinduced decay signal in the field-flat phase was performed by using PDCO, a primal-dual interior method for convex objectives. Néel and Brownian relaxation peaks were elucidated and measured on samples with various glycerol and gelatin concentrations. The sensitivity of viscosity prediction of the decoupled relaxation times was evaluated. A digital vascular phantom was designed to mimic a plaque with viscous MNPs and a catheter with immobilized MNPs. Spectral imaging of the digital vascular phantom was simulated by combining a field-free point with homogeneous pulsed excitation. The relationship between the Brownian relaxation time from different tissues and the number of periods for signal averages was evaluated for a scan time estimation in the simulation.Xin Feng and Guang Jia contributed equally to the work.

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Section: Trapezoidal-waveform Relaxometrymentioning

confidence: 99%

Relaxation spectral analysis in multi‐contrast vascular magnetic particle imaging

Feng,

Jia,

Peng

et al. 2023

Medical Physics

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“…Magnetic Particle Imaging (MPI) is a novel modality of functional and molecular tomography imaging technology that allows direct detection of the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles (Gleich and Weizenecker 2005, Biederer et al 2009, Dieckhoff et al 2017, Billings et al 2021. MPI provides real-time imaging capabilities that show great potential in terms of temporal and spatial resolution (Ludwig et al 2012, Jia et al 2022. MPI has no background tissue signal, which realizes high contrast imaging.…”

Section: Introductionmentioning

confidence: 99%

Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging

Shang¹,

Liu²,

Liu³

et al. 2023

Phys. Med. Biol.

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Objective: Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide (SPIO) nanoparticles distributions in MPI. However, the spatial resolution of unidirectional Cartesian trajectory MPI exhibits anisotropy, which blurs the boundaries of MPI images and makes precise localization difficult. In this paper, we propose an anisotropic edge-preserving network (AEP-net) to alleviate the anisotropic resolution of MPI. Methods: AEP-net resolve the resolution anisotropy by constructing an asymmertic convolution. To recover the edge information, we design the uncertainty region module. In addition, we evaluated the performance of the proposed AEP-net model by using simulations and experimental data. Results: The results show that the AEP-net model alleviates the anisotropy of the unidirectional Cartesian trajectory and preserves edge details in the MPI image. By comparing the visualization results and the metrics, we demonstrate that our method is superior to other methods. Significance: The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.

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“…The traditional fuzzy C-means algorithm, which constructs the objective function using the error sum-of-squares criterion, is only suitable for samples with spherical or sphere-like distributions, limiting the effectiveness of clustering samples with different shape distributions [4][5][6][7]. To overcome this drawback, many scholars have attempted to modify the criterion function using new methods, such as the kernel function method, to accommodate clustering of multiple sample shape distributions [1,2].…”

Section: Introductionmentioning

confidence: 99%

Image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems

Yuan¹,

Sun²,

Wang³

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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Improved fuzzy c-means (FCM) clustering algorithms have been widely used for image recognition and localization. However, in industrial assembly systems, the unsatisfactory pixel merging and segmentation results between local adjacent windows, combined with the differences in the shape, size, and material of parts, as well as variations in lighting conditions, make target image recognition and localization a challenge. Most algorithms struggle to achieve the expected results and have high computational complexity. In this study, we propose a super-resolution-based FCM clustering algorithm that is faster and more accurate for image recognition and localization in industrial assembly systems with irregular part sizes. We first use multiscale morphological gradient operations to obtain high-resolution images. Then, we use the fast FCM clustering algorithm to achieve the recognition and extraction of specific target images. Finally, we use the Sobel operator to determine the target's position. The experimental results demonstrate that the proposed algorithm shows higher accuracy and efficiency in image recognition and localization for industrial assembly systems.

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Gradient-Based Pulsed Excitation and Relaxation Encoding in Magnetic Particle Imaging

Cited by 8 publications

References 51 publications

Relaxation spectral analysis in multi‐contrast vascular magnetic particle imaging

Relaxation spectral analysis in multi‐contrast vascular magnetic particle imaging

Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging

Image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems

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