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Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model

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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 linear trend. Upon segmenting the ROI of ex vivo data, a linear trend was observed in which increasing intensity of the ROI yielded increasing TIV of the islets. Through statistical evaluation of the algorithm performance via intraclass correlation coefficient validation, we observed excellent performance of K-means++-based model on segmentation and quantification analysis of MPI data.

Conclusions

We have demonstrated the ability of the K-means++-based model to provide a standardized method of segmentation and quantification of MPI scans in an islet transplantation mouse model.

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Acknowledgments

Human pancreatic islets were provided by the NIDDK-funded Integrated Islet Distribution Program (IIDP) at City of Hope, NIH Grant # 2UC4DK098085 and the JDRF-funded IIDP Islet Award Initiative to P.W. The project was also partly funded by the 1R03EB028349 from NIH/NIBIB to P.W. The authors would like to thank Dr. Jeffrey M. Gaudet (Magnetic Insight Inc.) and Dr. Christopher H. Contag (The Institute for Quantitative Health Science & Engineering, Michigan State University) for their great support and helpful discussions.

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Author notes

  1. Hasaan Hayat and Aixia Sun contributed equally to this work.

Authors and Affiliations

  1. Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI, 48823, USA

    Hasaan Hayat, Aixia Sun, Sihai Liu, Nazanin Talebloo, Jack Owen Bishop, Mithil Gudi, Bennett Francis Dwan, Xiaohong Ma, Yanfeng Zhao, Anna Moore & Ping Wang

  2. Lyman Briggs College, Michigan State University, East Lansing, MI, USA

    Hasaan Hayat, Hanaan Hayat & Mithil Gudi

  3. Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA

    Aixia Sun, Sihai Liu, Xiaohong Ma, Yanfeng Zhao, Anna Moore & Ping Wang

  4. Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA

    Hanaan Hayat & Cody Pinger

  5. Department of Orthopedics, Beijing Charity Hospital, Capital Medical University, Beijing, China

    Sihai Liu

  6. Department of Chemistry, College of Natural Science, Michigan State University, East Lansing, MI, USA

    Nazanin Talebloo

  7. Department of Neuroscience, College of Natural Science, Michigan State University, East Lansing, MI, USA

    Jack Owen Bishop

  8. College of Natural Science, Michigan State University, East Lansing, MI, USA

    Bennett Francis Dwan

  9. Department of Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Xiaohong Ma & Yanfeng Zhao

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  1. Hasaan Hayat
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  2. Aixia Sun
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Contributions

H.H., A.S., H.H., S.L., and N.T. researched the data. C. P. did the phantom 3D printing. H.H and A.S. led the data analysis. J.B., M.G., B.D., X.M., and Y.Z participated in data analysis. H.H. and A.M. participated in drafting the manuscript. P.W. conceived the idea, designed the study, and drafted the manuscript. P.W. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding author

Correspondence to Ping Wang.

Ethics declarations

All animal experiments were approved by the Institutional Animal Care and Use Committee at Michigan State University.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Hayat, H., Sun, A., Hayat, H. et al. Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model. Mol Imaging Biol 23, 18–29 (2021). https://doi.org/10.1007/s11307-020-01533-5

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  • DOI: https://doi.org/10.1007/s11307-020-01533-5

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