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MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction

文献类型：期刊论文

作者Zhao, Jing1,2; Shen, Yusong3; Liu, Xinyi1,2; Hou, Xiaoyuan4; Ding, Xuetong1,2; An, Yu1,2,5,6; Hui, Hui7; Tian, Jie1,2,3,5,6,7; Zhang, Hui1,2,5,6
刊名MEDICAL PHYSICS
出版日期2024-05-03
页码18
关键词direct reconstruction generative adversarial network high-resolution magnetic particle imaging simulation
ISSN号0094-2405
DOI10.1002/mp.17104
通讯作者Tian, Jie(jie.tian@ia.ac.cn) ; Zhang, Hui(hui.zhang@buaa.edu.cn)
英文摘要BackgroundMagnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images.PurposeCurrently, we proposed an end-to-end machine learning framework to reconstruct high-resolution MPI images from 1-D voltage signals directly and efficiently.MethodsThe proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291 597 pairs of high-resolution 2-D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high-resolution MPI image reconstruction.ResultsExperiment results showed that, MPIGAN exhibited remarkable abilities in high-resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X-space in recovering the fine-scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high-quality MPI images.ConclusionOur study provides a promising AI solution for end-to-end, efficient, and high-resolution magnetic particle imaging reconstruction.
WOS关键词FORMULATION ; TRACKING ; NETWORK ; CELLS
资助项目National Natural Science Foundation of China ; Beijing Hospital Authority Clinical Medicine Development[ZLRK202333] ; [32371152] ; [62027901]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:001217140200001
出版者WILEY
资助机构National Natural Science Foundation of China ; Beijing Hospital Authority Clinical Medicine Development
源URL[http://ir.ia.ac.cn/handle/173211/58410]  
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; Zhang, Hui
作者单位1.Beihang Univ, Sch Engn Med, Beijing 100191, Peoples R China
2.Beihang Univ, Sch Biol Sci & Med Engn, Beijing, Peoples R China
3.Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China
4.Peking Univ, Sch Psychol & Cognit Sci, Beijing, Peoples R China
5.Beihang Univ, Key Lab Biomech & Mechanobiol, Minist Educ, Beijing 100191, Peoples R China
6.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol Peoples Republ China, Beijing, Peoples R China
7.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Jing,Shen, Yusong,Liu, Xinyi,et al. MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction[J]. MEDICAL PHYSICS,2024:18.
APA Zhao, Jing.,Shen, Yusong.,Liu, Xinyi.,Hou, Xiaoyuan.,Ding, Xuetong.,...&Zhang, Hui.(2024).MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction.MEDICAL PHYSICS,18.
MLA Zhao, Jing,et al."MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction".MEDICAL PHYSICS (2024):18.

入库方式： OAI收割

来源：自动化研究所

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