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Hayat H, Wang R, Sun A, Mallett CL, Nigam S, Redman N, Bunn D, Gjelaj E, Talebloo N, Alessio A, Moore A, Zinn K, Wei GW, Fan J, Wang P. Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring. iScience 2023; 26:107083. [PMID: 37416468 PMCID: PMC10319838 DOI: 10.1016/j.isci.2023.107083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 02/10/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023] Open
Abstract
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.
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Affiliation(s)
- 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
| | - 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
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Mannheim JG, Kara F, Doorduin J, Fuchs K, Reischl G, Liang S, Verhoye M, Gremse F, Mezzanotte L, Huisman MC. Standardization of Small Animal Imaging-Current Status and Future Prospects. Mol Imaging Biol 2019; 20:716-731. [PMID: 28971332 DOI: 10.1007/s11307-017-1126-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The benefit of small animal imaging is directly linked to the validity and reliability of the collected data. If the data (regardless of the modality used) are not reproducible and/or reliable, then the outcome of the data is rather questionable. Therefore, standardization of the use of small animal imaging equipment, as well as of animal handling in general, is of paramount importance. In a recent paper, guidance for efficient small animal imaging quality control was offered and discussed, among others, the use of phantoms in setting up a quality control program (Osborne et al. 2016). The same phantoms can be used to standardize image quality parameters for multi-center studies or multi-scanners within center studies. In animal experiments, the additional complexity due to animal handling needs to be addressed to ensure standardized imaging procedures. In this review, we will address the current status of standardization in preclinical imaging, as well as potential benefits from increased levels of standardization.
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Affiliation(s)
- Julia G Mannheim
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany.
| | - Firat Kara
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
| | - Janine Doorduin
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Kerstin Fuchs
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany
| | - Gerald Reischl
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany
| | - Sayuan Liang
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
| | | | - Felix Gremse
- Institute for Experimental Molecular Imaging, RWTH Aachen University Clinic, Aachen, Germany
| | - Laura Mezzanotte
- Optical Molecular Imaging, Department of Radiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Marc C Huisman
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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4
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Shen B, Huang T, Sun Y, Jin Z, Li XF. Revisit 18F-fluorodeoxyglucose oncology positron emission tomography: "systems molecular imaging" of glucose metabolism. Oncotarget 2018; 8:43536-43542. [PMID: 28402949 PMCID: PMC5522167 DOI: 10.18632/oncotarget.16647] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 03/11/2017] [Indexed: 01/26/2023] Open
Abstract
18F-fluorodeoxyglucose (18F-FDG) positron emission tomography has become an important tool for detection, staging and management of many types of cancer. Oncology application of 18F-FDG bases on the knowledge that increase in glucose demand and utilization is a fundamental features of cancer. Pasteur effect, Warburg effect and reverse Warburg effect have been used to explain glucose metabolism in cancer. 18F-FDG accumulation in cancer is reportedly microenvironment-dependent, 18F-FDG avidly accumulates in poorly proliferating and hypoxic cancer cells, but low in well perfused (and proliferating) cancer cells. Cancer is a heterogeneous and complex “organ” containing multiple components, therefore, cancer needs to be investigated from systems biology point of view, we proposed the concept of “systems molecular imaging” for much better understanding systems biology of cancer. This article revisits 18F-FDG uptake mechanisms, its oncology applications and the role of 18F-FDG PET for “systems molecular imaging”.
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Affiliation(s)
- Baozhong Shen
- PET/CT/MRI Center, The Fourth Hospital of Harbin Medical University, Harbin, China.,Molecular Imaging Research Center, Harbin Medical University, Harbin, China
| | - Tao Huang
- Department of Radiology, The Fourth Hospital of Harbin Medical University, Harbin, China
| | - Yingying Sun
- PET/CT/MRI Center, The Fourth Hospital of Harbin Medical University, Harbin, China.,Molecular Imaging Research Center, Harbin Medical University, Harbin, China
| | - Zhongnan Jin
- PET/CT/MRI Center, The Fourth Hospital of Harbin Medical University, Harbin, China.,Molecular Imaging Research Center, Harbin Medical University, Harbin, China
| | - Xiao-Feng Li
- PET/CT/MRI Center, The Fourth Hospital of Harbin Medical University, Harbin, China.,Molecular Imaging Research Center, Harbin Medical University, Harbin, China
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Neveu MA, Bol V, Bol A, Bouzin C, Grégoire V, Feron O, Jordan BF, Gallez B. The increase in tumor oxygenation under carbogen breathing induces a decrease in the uptake of [(18)F]-fluoro-deoxy-glucose. Radiother Oncol 2015; 116:400-3. [PMID: 25981053 DOI: 10.1016/j.radonc.2015.04.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 04/14/2015] [Accepted: 04/29/2015] [Indexed: 11/27/2022]
Abstract
We investigated the impact of oxygenation status (measured by EPR oximetry) on the uptake of (18)F-FDG (measured by PET) in two different tumor models during a carbogen breathing challenge. We observed a significant drop in (18)F-FDG uptake under carbogen breathing that suggests a rapid metabolic adaptation to the oxygen environment.
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Affiliation(s)
- Marie-Aline Neveu
- Biomedical Magnetic Resonance Research Group, Université catholique de Louvain (UCL), Belgium
| | - Vanesa Bol
- Radiation Oncology Department & Center for Molecular Imaging, Université catholique de Louvain (UCL), Belgium
| | - Anne Bol
- Radiation Oncology Department & Center for Molecular Imaging, Université catholique de Louvain (UCL), Belgium
| | - Caroline Bouzin
- Pole of Pharmacology and Therapeutics, Université catholique de Louvain (UCL), Belgium
| | - Vincent Grégoire
- Radiation Oncology Department & Center for Molecular Imaging, Université catholique de Louvain (UCL), Belgium
| | - Olivier Feron
- Pole of Pharmacology and Therapeutics, Université catholique de Louvain (UCL), Belgium
| | - Benedicte F Jordan
- Biomedical Magnetic Resonance Research Group, Université catholique de Louvain (UCL), Belgium
| | - Bernard Gallez
- Biomedical Magnetic Resonance Research Group, Université catholique de Louvain (UCL), Belgium.
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