Non-invasive Localized Heating and Temperature Monitoring based on a Cavity Applicator for Hyperthermia

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Magnetic Particle Imaging Using Discrete Sampling and Image Reconstruction with Few Orthogonal Bases Obtained by Singular Value Decomposition of Selected Delta Responses

The conventional magnetic particle imaging reconstruction methods use observed signals and system functions, which result in an enormous amount of data and long processing time being required to reconstruct large image matrices. We propose a new image reconstruction method that uses less data and a limited number of orthogonal bases obtained via the singular value decomposition (SVD) of selected point spread functions (PSFs). By using the features of the diagonal and nondiagonal elements of a singular value matrix, image blurring and artifacts can be reduced in the reconstructed image. This is because the diagonal components commonly indicate the similarities between each orthogonal basis for the system function and an observed signal, whereas the nondiagonal components indicate the differences of both them. In this paper, we use numerical analyses to demonstrate that image reconstruction is possible by using effective orthogonal bases obtained through the SVD of a limited number of PSFs selected from a general system function. The reconstruction time is reduced to 1/12th to 1/50th of that of the conventional method.   Int. J. Mag. Part. Imag. 6(1), 2020, Article ID: 2003003, DOI: 10.18416/IJMPI.2020.200300

Preliminary experiments for detection of reflected signals from magnetic nanoparticles by ultrasound

The conventional magnetic particle imaging (MPI) requires large coils and high power inputs that generate a steep magnetic field gradient to reduce image artifacts, without which the blurring of reconstructed images occur. Therefore, a new imaging system based on vibrating magnetic nanoparticles (MNPs) that does not use an alternating magnetic field was proposed to solve this problem. It was observed that the reflection signals from the MNPs could be detected using focused ultrasound. Preliminary experiments were performed to clarify the reflection signal corresponding to the MNPs concentration with an image using a diagnostic ultrasound imaging system. As a result, the MNPs reflection signal’s ultrasound image was not acquired at an MNP concentration of 0.6 mol/L(Fe) or less. Therefore, a detailed examination of the concentration and particle size was required to detect the signal from MNPs.   Int. J. Mag. Part. Imag. 6(2), Suppl. 1, 2020, Article ID: 2009014, DOI: 10.18416/IJMPI.2020.200901

Basic Study of Image Reconstruction Method Using Neural Networks with Additional Learning for Magnetic Particle Imaging

In magnetic particle imaging (MPI), image blurring and artifacts occur in a reconstructed image because the magnetization signals generated from magnetic nanoparticles (MNPs) at the field free point (FFP) are similar to those around the FFP regions. In order to overcome these problems, we proposed a new reconstruction method using neural networks. In this method, a data set of magnetization signals and MNP location pairs is used for learning in neural networks. If all possible combinations of the data sets are learned, an accurate estimated result is obtained. However, it is difficult to learn all the combinations in a reasonable period of time. In this study, the number of data sets learned in the first stage was minimized, and additional learning using the appropriate data sets, which reduces the error between observed signals and estimated signals, was performed. By learning the minimum number of required data sets, it is expected that image blurring and artifacts will be suppressed even when the MNP’s magnetization is insufficient, e.g., when an applied alternative magnetic field and/or a gradient magnetic field are/is weak. We performed numerical experiments to confirm the effectiveness of our proposed method. From the experimental results, it was confirmed that image blurring and artifacts were suppressed using our proposed method even when the MNP’s magnetization was insufficient. However, it may be difficult to reconstruct an accurate image when appropriate data sets are not selected for learning. Hence, in the future, we will improve the method for selecting the data sets