Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model
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RESEARCH ARTICLE
Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model Hasaan Hayat,1,2 Aixia Sun,1,3 Hanaan Hayat,2,4 Sihai Liu,1,3,5 Nazanin Talebloo,1,6 Cody Pinger,4 Jack Owen Bishop,1,7 Mithil Gudi,1,2 Bennett Francis Dwan,1,8 Xiaohong Ma,1,3,9 Yanfeng Zhao,1,3,9 Anna Moore,1,3 Ping Wang 1,3 1
Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI, 48823, USA Lyman Briggs College, Michigan State University, East Lansing, MI, USA 3 Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA 4 Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA 5 Department of Orthopedics, Beijing Charity Hospital, Capital Medical University, Beijing, China 6 Department of Chemistry, College of Natural Science, Michigan State University, East Lansing, MI, USA 7 Department of Neuroscience, College of Natural Science, Michigan State University, East Lansing, MI, USA 8 College of Natural Science, Michigan State University, East Lansing, MI, USA 9 Department of Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China 2
Abstract Purpose: Current approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of artificial intelligence (AI) systems for MPI analysis. Procedures: We utilize a canonical algorithm in the domain of unsupervised machine learning, known as K-means++, to segment the regions of interest (ROI) of images and perform iron quantification analysis using a standard curve model. We generated in vitro, in vivo, and ex vivo data using islets and mouse models and applied the AI algorithm to gain insight into segmentation and iron prediction on these MPI data. In vitro models included imaging the VivoTrax-labeled islets in varying numbers. In vivo mouse models were generated through transplantation of increasing numbers of the labeled islets under the kidney capsule of mice. Ex vivo data were obtained from the MPI images of excised kidney grafts. Results: The K-means++ algorithms segmented the ROI of in vitro phantoms with minimal noise. A linear correlation between the islet numbers and the increasing prediction of total iron value (TIV) in the islets was observed. Segmentation results of the ROI of the in vivo MPI scans showed that with increasing number of transplanted islets, the signal intensity increased with
Hasaan Hayat and Aixia Sun contributed equally to this work. Electronic supplementary material The online version of this article (https:// doi.org/10.1007/s11307-020-01533-5) contains supplementary material, which is available to authorized users. Correspondence to: Ping Wang; e-mail: [email protected]
Hayat H. et al.: Artificial Intelligen
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