System matrix recovery based on deep image prior in magnetic particle imaging
文献类型：期刊论文
| 作者 | Yin, Lin4,5,6 ; Guo, Hongbo3; Zhang, Peng2; Li, Yimeng1; Hui, Hui4,5,6; Du, Yang4,5,6; Tian, Jie1,4,5,6
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| 刊名 | PHYSICS IN MEDICINE AND BIOLOGY
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| 出版日期 | 2023-02-07 |
| 卷号 | 68期号:3页码:14 |
| 关键词 | magnetic particle imaging deep image prior system matrix recovery |
| ISSN号 | 0031-9155 |
| DOI | 10.1088/1361-6560/acaf47 |
| 通讯作者 | Tian, Jie(tian@ieee.org) |
| 英文摘要 | 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. |
| WOS关键词 | RECONSTRUCTION ; RESOLUTION |
| 资助项目 | National Key Research and Development Program of China[2017YFA0205200] ; National Key Research and Development Program of China[2017YFA0700401] ; National Key Research and Development Program of China[2016YFC0103803] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[82230067] ; National Natural Science Foundation of China[62201570] ; CAS Youth Innovation Promotion Association[2018167] ; CAS Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703] |
| WOS研究方向 | Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| WOS记录号 | WOS:000918241900001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; CAS Youth Innovation Promotion Association ; CAS Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51329]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 通讯作者 | Tian, Jie |
| 作者单位 | 1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China 2.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 3.Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China 6.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, People's Republ China, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Lin,Guo, Hongbo,Zhang, Peng,et al. System matrix recovery based on deep image prior in magnetic particle imaging[J]. PHYSICS IN MEDICINE AND BIOLOGY,2023,68(3):14. |
| APA | Yin, Lin.,Guo, Hongbo.,Zhang, Peng.,Li, Yimeng.,Hui, Hui.,...&Tian, Jie.(2023).System matrix recovery based on deep image prior in magnetic particle imaging.PHYSICS IN MEDICINE AND BIOLOGY,68(3),14. |
| MLA | Yin, Lin,et al."System matrix recovery based on deep image prior in magnetic particle imaging".PHYSICS IN MEDICINE AND BIOLOGY 68.3(2023):14. |
入库方式： OAI收割
来源：自动化研究所
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