磁粉成像
计算机科学
扫描仪
增采样
校准
图像质量
噪音(视频)
过程(计算)
人工智能
人工神经网络
基质(化学分析)
图像(数学)
计算机视觉
材料科学
磁性纳米粒子
统计
数学
纳米颗粒
复合材料
操作系统
纳米技术
作者
Lin Yin,Hongbo Guo,Peng Zhang,Yimeng Li,Hui Hui,Yang Du,Jie Tian
标识
DOI:10.1088/1361-6560/acaf47
摘要
Abstract Objective. Magnetic particle imaging (MPI) is an emerging tomography imaging technique with high specificity and temporal-spatial resolution. MPI reconstruction based on the system matrix (SM) is an important research content in MPI. However, SM is usually obtained by measuring the response of an MPI scanner at all positions in the field of view. This process is very time-consuming, and the scanner will overheat in a long period of continuous operation, which is easy to generate thermal noise and affects MPI imaging performance. Approach. In this study, we propose a deep image prior-based method that prominently decreases the time of SM calibration. It is an unsupervised method that utilizes the neural network structure itself to recover a high-resolution SM from a downsampled SM without the need to train the network using a large amount of training data. Main results. Experiments on the Open MPI data show that the time of SM calibration can be greatly reduced with only slight degradation of image quality. Significance. This study provides a novel method for obtaining SM in MPI, which shows the potential to achieve SM recovery at a high downsampling rate. It is expected that this study will increase the practicability of MPI in biomedical applications and promote the development of MPI in the future.
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