Hayat Hasaan, Wang Rui, Sun Aixia, Mallett Christiane L, Nigam Saumya, Redman Nathan, Bunn Demarcus, Gjelaj Elvira, Talebloo Nazanin, Alessio Adam, Moore Anna, Zinn Kurt, Wei Guo-Wei, Fan Jinda, Wang Ping
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.
iScience. 2023 Jun 9;26(7):107083. doi: 10.1016/j.isci.2023.107083. eCollection 2023 Jul 21.
Current methods of 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 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.
目前使用磁共振成像（MRI）对糖尿病胰岛细胞移植进行成像的方法受到其低灵敏度的限制。同时正电子发射断层扫描（PET）/MRI具有更高的灵敏度和可视化细胞代谢的能力。然而，这种双模态工具目前在监测细胞方面面临两个主要挑战。首先，PET的动态条件，如信号衰减和放射性的时空变化，妨碍了对移植细胞数量的准确量化。此外，不同放射科医生的选择偏差导致分割中的人为误差。这就需要开发人工智能算法来自动分析细胞移植的PET/MRI。在这里，我们将用于分割的Kmeans++与卷积神经网络相结合，以预测细胞移植小鼠模型中的放射性。本研究提供了一种将机器学习与深度学习算法相结合的工具，用于通过PET/MRI监测胰岛细胞移植。它还开启了一种对PET/MRI中的放射性进行自动分割和量化的动态方法。
