Neural Network Image Reconstruction for Magnetic Particle Imaging

Chae, Byung-Gyu

doi:10.4218/etrij.2017-0094

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…TheDEQ-MPI (Güngör et al 2023) demonstrates improved reconstruction quality and comparable reconstruction speed to ADMM L1 by using a novel deep equilibrium reconstruction with learned consistency. Additionally, while direct image reconstruction approaches based on deep learning have demonstrated superiority in reconstructing simulated data (Chae 2017, von Gladiss et al 2022, they encounter limitations when it comes to real phantom data. Besides supervised learning algorithms, contrastive learning has also been used in MPI reconstruction (Schrank and Schulz 2023).…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging

Peng,

Yin,

Sun

et al. 2023

Phys. Med. Biol.

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Objective. Magnetic particle imaging (MPI) shows potential for contributing to biomedical research and clinical practice. However, MPI images are effectively affected by noise in the signal as its reconstruction is an ill-posed inverse problem. Thus, effective reconstruction method is required to reduce the impact of the noise while mapping signals to MPI images. Traditional methods rely on the hand-crafted data-consistency (DC) term and regularization term based on spatial priors to achieve noise-reducing and reconstruction. While these methods alleviate the ill-posedness and reduce noise effects, they may be difficult to fully capture spatial features. Approach. In this study, we propose a deep neural network for end-to-end reconstruction (DERnet) in MPI that emulates the DC term and regularization term using the feature mapping subnetwork and post-processing subnetwork, respectively, but in a data-driven manner. By doing so, DERnet can better capture signal and spatial features without relying on hand-crafted priors and strategies, thereby effectively reducing noise interference and achieving superior reconstruction quality. Main results. Our data-driven method outperforms the state-of-the-art algorithms with an improvement of 0.9–8.8 dB in terms of peak signal-to-noise ratio under various noise levels. The result demonstrates the advantages of our approach in suppressing noise interference. Furthermore, DERnet can be employed for measured data reconstruction with improved fidelity and reduced noise. In conclusion, our proposed method offers performance benefits in reducing noise interference and enhancing reconstruction quality by effectively capturing signal and spatial features. Significance. DERnet is a promising candidate method to improve MPI reconstruction performance and facilitate its more in-depth biomedical application.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging

Peng,

Yin,

Sun

et al. 2023

Phys. Med. Biol.

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“…In 2017, Chae [ 67 ] proposed the reconstruction of MPI images using a single-layer fully connected network (Fig. 6 A).…”

Section: Ai Methods For Mpi Reconstructionmentioning

confidence: 99%

“…The development of deep learning technology based on AI has inspired new approaches to MPI reconstruction. Currently, AI is primarily applied in MPI reconstruction [67,68], SM recovery [69], and image postprocessing [70].…”

Section: Ai Methods For Mpi Reconstructionmentioning

confidence: 99%

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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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“…Chae investigated the MPI reconstruction based on a neural network, and confirmed that a multi-layer with one hidden layer could improve the reconstruction performance [ 64 ]. DIP networks have been recently introduced in deep learning for applications in image processing [ 65 ].…”

Section: Current Sm-based Mpi Reconstruction Methodsmentioning

confidence: 99%

The Reconstruction of Magnetic Particle Imaging: Current Approaches Based on the System Matrix

et al. 2021

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Magnetic particle imaging (MPI) is a novel non-invasive molecular imaging technology that images the distribution of superparamagnetic iron oxide nanoparticles (SPIONs). It is not affected by imaging depth, with high sensitivity, high resolution, and no radiation. The MPI reconstruction with high precision and high quality is of enormous practical importance, and many studies have been conducted to improve the reconstruction accuracy and quality. MPI reconstruction based on the system matrix (SM) is an important part of MPI reconstruction. In this review, the principle of MPI, current construction methods of SM and the theory of SM-based MPI are discussed. For SM-based approaches, MPI reconstruction mainly has the following problems: the reconstruction problem is an inverse and ill-posed problem, the complex background signals seriously affect the reconstruction results, the field of view cannot cover the entire object, and the available 3D datasets are of relatively large volume. In this review, we compared and grouped different studies on the above issues, including SM-based MPI reconstruction based on the state-of-the-art Tikhonov regularization, SM-based MPI reconstruction based on the improved methods, SM-based MPI reconstruction methods to subtract the background signal, SM-based MPI reconstruction approaches to expand the spatial coverage, and matrix transformations to accelerate SM-based MPI reconstruction. In addition, the current phantoms and performance indicators used for SM-based reconstruction are listed. Finally, certain research suggestions for MPI reconstruction are proposed, expecting that this review will provide a certain reference for researchers in MPI reconstruction and will promote the future applications of MPI in clinical medicine.

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Neural Network Image Reconstruction for Magnetic Particle Imaging

Cited by 10 publications

References 23 publications

DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging

DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging

Recent developments of the reconstruction in magnetic particle imaging

The Reconstruction of Magnetic Particle Imaging: Current Approaches Based on the System Matrix

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