The X-Space Formulation of the Magnetic Particle Imaging Process: 1-D Signal, Resolution, Bandwidth, SNR, SAR, and Magnetostimulation

2010 ◽  
Vol 29 (11) ◽  
pp. 1851-1859 ◽  
Author(s):  
Patrick W Goodwill ◽  
Steven M Conolly
Keyword(s):  
Magnetic Particle ◽  
Magnetic Particle Imaging ◽  
Space Formulation ◽  
Particle Imaging ◽  
Resolution Bandwidth

The x-space formulation of magnetic particle imaging including non-negligible relaxation effects

2011 ◽  
Author(s):  
Laura R. Croft ◽  
Patrick Goodwill ◽  
Arbi Tamrazian ◽  
Kannan Krishnan ◽  
Steven Conolly
Keyword(s):  
Magnetic Particle ◽  
Relaxation Effects ◽  
Magnetic Particle Imaging ◽  
Space Formulation ◽  
Particle Imaging

scholarly journals Flow velocity quantification by exploiting the principles of the Doppler effect and magnetic particle imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dennis Pantke ◽  
Florian Mueller ◽  
Sebastian Reinartz ◽  
Fabian Kiessling ◽  
Volkmar Schulz
Keyword(s):  
Flow Velocity ◽  
Magnetic Particle ◽  
Imaging Techniques ◽  
Radiation Risk ◽  
Medical Application ◽  
Velocity Estimation ◽  
Magnetic Particle Imaging ◽  
Measurable Velocity ◽  
Depth Dependency ◽  
Particle Imaging

AbstractChanges in blood flow velocity play a crucial role during pathogenesis and progression of cardiovascular diseases. Imaging techniques capable of assessing flow velocities are clinically applied but are often not accurate, quantitative, and reliable enough to assess fine changes indicating the early onset of diseases and their conversion into a symptomatic stage. Magnetic particle imaging (MPI) promises to overcome these limitations. Existing MPI-based techniques perform velocity estimation on the reconstructed images, which restricts the measurable velocity range. Therefore, we developed a novel velocity quantification method by adapting the Doppler principle to MPI. Our method exploits the velocity-dependent frequency shift caused by a tracer motion-induced modulation of the emitted signal. The fundamental theory of our method is deduced and validated by simulations and measurements of moving phantoms. Overall, our method enables robust velocity quantification within milliseconds, with high accuracy, no radiation risk, no depth-dependency, and extended range compared to existing MPI-based velocity quantification techniques, highlighting the potential of our method as future medical application.

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.

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