Simulation of reconstruction based on the system matrix for magnetic particle imaging

2022 ◽  
Vol 71 ◽  
pp. 103171
Author(s):  
Xiaojun Chen ◽  
Xiao Han ◽  
Xiaolin Wang ◽  
Weifeng Liu ◽  
Tianxin Gao ◽  
...  
Keyword(s):  
Magnetic Particle ◽  
System Matrix ◽  
Magnetic Particle Imaging ◽  
Particle Imaging

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

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 773
Author(s):  
Xiaojun Chen ◽  
Zhenqi Jiang ◽  
Xiao Han ◽  
Xiaolin Wang ◽  
Xiaoying Tang
Keyword(s):  
Magnetic Particle ◽  
Clinical Medicine ◽  
Practical Importance ◽  
Superparamagnetic Iron Oxide ◽  
Matrix Transformations ◽  
System Matrix ◽  
Magnetic Particle Imaging ◽  
Reconstruction Methods ◽  
Spatial Coverage ◽  
Particle Imaging

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.

Reconstruction Enhancement by Denoising the Magnetic Particle Imaging System Matrix Using Frequency Domain Filter

2015 ◽  
Vol 51 (2) ◽  
pp. 1-5 ◽  
Author(s):  
Alexander Weber ◽  
Jurgen Weizenecker ◽  
Ulrich Heinen ◽  
Michael Heidenreich ◽  
Thorsten M. Buzug
Keyword(s):  
Frequency Domain ◽  
Magnetic Particle ◽  
Imaging System ◽  
System Matrix ◽  
Magnetic Particle Imaging ◽  
Particle Imaging

scholarly journals Efficient hybrid 3D system calibration for magnetic particle imaging systems using a dedicated device

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Anselm von Gladiss ◽  
Matthias Graeser ◽  
André Behrends ◽  
Xin Chen ◽  
Thorsten M. Buzug
Keyword(s):  
Image Reconstruction ◽  
Hybrid System ◽  
Magnetic Particle ◽  
Signal To Noise Ratio ◽  
Measurement Time ◽  
Time Signal ◽  
Signal Acquisition ◽  
System Matrix ◽  
Magnetic Particle Imaging ◽  
Particle Imaging

Abstract Image reconstruction in magnetic particle imaging is often performed using a system matrix based approach. The acquisition of a system matrix is a time-consuming calibration which may take several weeks and thus, is not feasible for a clinical device. Due to hardware characteristics of the receive chain, a system matrix may not even be used in similar devices but has to be acquired for each imager. In this work, a dedicated device is used for measuring a hybrid system matrix. It is shown that the measurement time of a 3D system matrix is reduced by 96%. The transfer function of the receive chains is measured, which allows the use of the same system matrix in multiple devices. Equivalent image reconstruction results are reached using the hybrid system matrix. Furthermore, the inhomogeneous sensitivity profile of receive coils is successfully applied to a hybrid system matrix. It is shown that each aspect of signal acquisition in magnetic particle imaging can be taken into account using hybrid system matrices. It is favourable to use a hybrid system matrix for image reconstruction in terms of measurement time, signal-to-noise ratio and discretisation.

scholarly journals Local System Matrix Compression for Efficient Reconstruction in Magnetic Particle Imaging

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
T. Knopp ◽  
A. Weber
Keyword(s):  
Magnetic Particles ◽  
Magnetic Particle ◽  
Particle Distribution ◽  
Calibration Procedure ◽  
System Matrix ◽  
Compression Technique ◽  
Reconstruction Process ◽  
Magnetic Particle Imaging ◽  
Matrix Compression ◽  
Particle Imaging

Magnetic particle imaging (MPI) is a quantitative method for determining the spatial distribution of magnetic nanoparticles, which can be used as tracers for cardiovascular imaging. For reconstructing a spatial map of the particle distribution, the system matrix describing the magnetic particle imaging equation has to be known. Due to the complex dynamic behavior of the magnetic particles, the system matrix is commonly measured in a calibration procedure. In order to speed up the reconstruction process, recently, a matrix compression technique has been proposed that makes use of a basis transformation in order to compress the MPI system matrix. By thresholding the resulting matrix and storing the remaining entries in compressed row storage format, only a fraction of the data has to be processed when reconstructing the particle distribution. In the present work, it is shown that the image quality of the algorithm can be considerably improved by using a local threshold for each matrix row instead of a global threshold for the entire system matrix.

System Matrix Recording and Phantom Measurements with a Single-Sided Magnetic Particle Imaging Device

2015 ◽  
Vol 51 (2) ◽  
pp. 1-3 ◽  
Author(s):  
Ksenija Grafe ◽  
Gael Bringout ◽  
Matthias Graeser ◽  
Timo F. Sattel ◽  
Thorsten M. Buzug
Keyword(s):  
Magnetic Particle ◽  
System Matrix ◽  
Imaging Device ◽  
Magnetic Particle Imaging ◽  
Phantom Measurements ◽  
Particle Imaging

scholarly journals Reconstruction of the Magnetic Particle Imaging System Matrix Using Symmetries and Compressed Sensing

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
A. Weber ◽  
T. Knopp
Keyword(s):  
Compressed Sensing ◽  
Magnetic Particle ◽  
Imaging System ◽  
Superparamagnetic Iron Oxide ◽  
Calibration Procedure ◽  
Field Of View ◽  
System Matrix ◽  
Magnetic Particle Imaging ◽  
Calibration Measurement ◽  
Particle Imaging

Magnetic particle imaging (MPI) is a tomographic imaging technique that allows the determination of the 3D spatial distribution of superparamagnetic iron oxide nanoparticles. Due to the complex dynamic nature of these nanoparticles, a time-consuming calibration measurement has to be performed prior to image reconstruction. During the calibration a small delta sample filled with the particle suspension is measured at all positions in the field of view where the particle distribution will be reconstructed. Recently, it has been shown that the calibration procedure can be significantly shortened by sampling the field of view only at few randomly chosen positions and applying compressed sensing to reconstruct the full MPI system matrix. The purpose of this work is to reduce the number of necessary calibration scans even further. To this end, we take into account symmetries of the MPI system matrix and combine this knowledge with the compressed sensing method. Experiments on 2D MPI data show that the combination of symmetry and compressed sensing allows reducing the number of calibration scans compared to the pure compressed sensing approach by a factor of about three.

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