Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring
Hasaan Hayat,
Rui Wang,
Aixia Sun,
Christiane L. Mallett,
Saumya Nigam,
Nathan Redman,
Demarcus Bunn,
Elvira Gjelaj,
Nazanin Talebloo,
Adam Alessio,
Anna Moore,
Kurt Zinn,
Guo-Wei Wei,
Jinda Fan,
Ping Wang
Affiliations
Hasaan Hayat
Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA; Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA; College of Human Medicine, Michigan State University, East Lansing, MI, USA
Rui Wang
Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, MI, USA
Aixia Sun
Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA; Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
Christiane L. Mallett
Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA; Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
Saumya Nigam
Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA; Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
Nathan Redman
Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA; Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
Demarcus Bunn
Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA; Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
Elvira Gjelaj
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
Nazanin Talebloo
Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA; Department of Chemistry, College of Natural Science, Michigan State University, East Lansing, MI, USA
Adam Alessio
Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA; Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA; Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA; Departments of Computational Mathematics, Science, and Engineering (CMSE), College of Natural Science, Michigan State University, East Lansing, MI, USA
Anna Moore
Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA; Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
Kurt Zinn
Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA; Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
Guo-Wei Wei
Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, MI, USA; Departments of Computational Mathematics, Science, and Engineering (CMSE), College of Natural Science, Michigan State University, East Lansing, MI, USA; Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
Jinda Fan
Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA; Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA; Department of Chemistry, College of Natural Science, Michigan State University, East Lansing, MI, USA; Corresponding author
Ping Wang
Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI 48823, USA; Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA; Corresponding author
Summary: Current methods of in vivo imaging islet cell transplants for diabetes using magnetic resonance imaging (MRI) are limited by their low sensitivity. Simultaneous positron emission tomography (PET)/MRI has greater sensitivity and ability to visualize cell metabolism. However, this dual-modality tool currently faces two major challenges for monitoring cells. Primarily, the dynamic conditions of PET such as signal decay and spatiotemporal change in radioactivity prevent accurate quantification of the transplanted cell number. In addition, selection bias from different radiologists renders human error in segmentation. This calls for the development of artificial intelligence algorithms for the automated analysis of PET/MRI of cell transplantations. Here, we combined K-means++ for segmentation with a convolutional neural network to predict radioactivity in cell-transplanted mouse models. This study provides a tool combining machine learning with a deep learning algorithm for monitoring islet cell transplantation through PET/MRI. It also unlocks a dynamic approach to automated segmentation and quantification of radioactivity in PET/MRI.
