Relaxation-Based Super-Resolution Method in Pulsed Magnetic Particle Imaging

IF 4.2 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Lei Li;Haohao Yan;Yuge Li;Yidong Liao;Yanjun Liu;Ruili Zhang;Zhongliang Wang;Xin Feng;Jie Tian
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引用次数: 0

Abstract

Spatial resolution is one of the most critical indicators for magnetic particle imaging (MPI). Due to factors such as relaxation effects and suboptimal magnetization response, MPI has not yet reached the promised spatial resolution. Pulsed MPI is a method that enables MPI to achieve the resolution predicted by the Langevin function, which thereby enables larger magnetic particles (MNPs) to enhance resolution. To further exceed this resolution, we propose a relaxation-based super-resolution method which leverages the principle that MNPs at different positions exhibit varying relaxation times due to the different DC fields provided by the gradient field. This principle allows the super-resolution method to extract signals from the center of the field free region (FFR) to enhance spatial resolution. The super-resolution method first truncates the exponential decay signal during the plateau phase of the excitation field. Then, the truncated signals are decomposed based on their relaxation times. Finally, signals from the center position of the FFR are retained, and signals from the periphery of the FFR are discarded. Using this retained signal for reconstruction results in a higher spatial resolution. We validate this method via both simulation and experimental measurements. The results indicate that, compared with sinusoidal MPI and pulsed MPI without super-resolution, the super-resolution method has two-fold improvement in resolution.
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来源期刊
IEEE Transactions on Computational Imaging
IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics
CiteScore
8.20
自引率
7.40%
发文量
59
期刊介绍: The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.
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