A novel software framework for magnetic particle imaging reconstruction

Magnetic particle imaging is a novel tomographic imaging technique that enables noninvasive and highly sensitive imaging of superparamagnetic iron oxide nanoparticles distributed in living subjects. Several studies have reported on the development of reconstruction algorithms; however, a unified sof...

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Published inInternational journal of imaging systems and technology Vol. 32; no. 4; pp. 1119 - 1132
Main Authors Shen, Yusong, Hu, Chaoen, Zhang, Peng, Tian, Jie, Hui, Hui
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.07.2022
Wiley Subscription Services, Inc
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Online AccessGet full text
ISSN0899-9457
1098-1098
DOI10.1002/ima.22707

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Summary:Magnetic particle imaging is a novel tomographic imaging technique that enables noninvasive and highly sensitive imaging of superparamagnetic iron oxide nanoparticles distributed in living subjects. Several studies have reported on the development of reconstruction algorithms; however, a unified software framework for magnetic particle imaging reconstruction has yet to be developed. Herein, we propose a high‐performance, flexible, and easy‐to‐use magnetic particle imaging reconstruction framework using the Python programming language. The magnetic particle imaging reconstruction framework consists of the data access, preprocessing, image reconstruction, and postprocessing phases. We used the proposed framework to simulate the x‐space and system matrix‐based reconstruction methods with Cartesian and Lissajous scan trajectories. The reconstruction results of an open magnetic particle imaging dataset and a numerically simulated phantom demonstrated that the magnetic particle imaging reconstruction framework provides a reliable and accessible environment for magnetic particle imaging reconstruction, which can be extended and customized.
Bibliography:Funding information
Yusong Shen and Chaoen Hu contributed equally to this work.
National Key Research and Development Program of China, Grant/Award Numbers: 2016YFC0103803, 2017YFA0205200, 2017YFA0700401; National Natural Science Foundation of China, Grant/Award Numbers: 62027901, 81527805, 81671851, 81827808; CAS Youth Innovation Promotion Association, Grant/Award Number: 2018167; CAS Key Technology Talent Program; The Project of High‐Level Talents Team Introduction in Zhuhai City, Grant/Award Number: HLHPTP201703
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ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22707