A novel software framework for magnetic particle imaging reconstruction

Shen, Yusong; Hu, C. Y.; Zhang, Peng; Tian, Jie; Hui, Hui

doi:10.1002/ima.22707

Cited by 22 publications

(7 citation statements)

References 28 publications

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“…These data are difficult to obtain with current MPI scanners. Therefore, we converted the Digital Retinal Images for Vessel Extraction (DRIVE) into MPI images (Shen et al 2022). In the data preprocessing stage, we divided the DRIVE data into units of 50 ´50 pixels.…”

Section: Simulated Datamentioning

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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Section: Simulated Datamentioning

confidence: 99%

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

Shang¹,

Liu²,

Liu³

et al. 2023

Phys. Med. Biol.

Self Cite

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“…(4) Our study focuses solely on the hardware environment in our experiment, so results could slightly differ in other hardware environment. In the future, we aim to integrate our AMPIM into open source MPI reconstruction frameworks (Shen et al 2022) to be applied to real-time MPI engineering directly.…”

Section: ( )mentioning

confidence: 99%

An adaptive multi-frame parallel iterative method for accelerating real-time magnetic particle imaging reconstruction

Shen,

Zhang,

Shang

et al. 2023

Phys. Med. Biol.

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Objective. Real-time reconstruction of magnetic particle imaging (MPI) shows promising clinical applications. However, prevalent reconstruction methods are mainly based on serial iteration, which causes large delay in real-time reconstruction. In order to achieve lower latency in real-time MPI reconstruction, we propose a parallel method for accelerating the speed of reconstruction methods. Approach. The proposed method, named adaptive multi-frame parallel iterative method (AMPIM), enables the processing of multi-frame signals to multi-frame MPI images in parallel. To facilitate parallel computing, we further propose an acceleration strategy for parallel computation to improve the computational efficiency of our AMPIM. Main results. OpenMPIData was used to evaluate our AMPIM, and the results show that our AMPIM improves the reconstruction frame rate per second of real-time MPI reconstruction by two orders of magnitude compared to prevalent iterative algorithms including the Kaczmarz algorithm, the conjugate gradient normal residual algorithm, and the alternating direction method of multipliers algorithm. The reconstructed image using AMPIM has high contrast-to-noise with reducing artifacts. Significance. The AMPIM can parallelly optimize least squares problems with multiple right-hand sides by exploiting the dimension of the right-hand side. AMPIM has great potential for application in real-time MPI imaging with high imaging frame rate.

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“…They set up an MPI website (MPILabs, http://mpilab.net/en/simulation ) that exhibits relevant academic research, open-source experimental data, an algorithm framework, and a software platform. Notably, the software platform MPIRF developed in Python 3.8 integrates SM-based and x-space algorithms to realize MPI reconstruction from the voltage signal of SPIOs to the image [ 74 ].…”

Section: Current Open Sources For Mpimentioning

confidence: 99%

Recent developments of the reconstruction in magnetic particle imaging

Yin

et al. 2022

Vis. Comput. Ind. Biomed. Art

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Magnetic particle imaging (MPI) is an emerging molecular imaging technique with high sensitivity and temporal-spatial resolution. Image reconstruction is an important research topic in MPI, which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration distribution. MPI reconstruction primarily involves system matrix- and x-space-based methods. In this review, we provide a detailed overview of the research status and future research trends of these two methods. In addition, we review the application of deep learning methods in MPI reconstruction and the current open sources of MPI. Finally, research opinions on MPI reconstruction are presented. We hope this review promotes the use of MPI in clinical applications.

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A novel software framework for magnetic particle imaging reconstruction

Cited by 22 publications

References 28 publications

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

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

An adaptive multi-frame parallel iterative method for accelerating real-time magnetic particle imaging reconstruction

Recent developments of the reconstruction in magnetic particle imaging

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