Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model.
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 analys...
| Publicado en: | Molecular Imaging & Biology Vol. 23; no. 1; pp. 18 - 30 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147908005&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147908005 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15361632 KJU jtl: Molecular Imaging & Biology issn: 15361632 maglogo: N pubinfo: dt: 2021 vid: 23 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 147908005 147908005 NLM32833112 147908005 10.1007/s11307-020-01533-5 NLM32833112 147908005 ppf: 18 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model. aug: au: Hayat, Hasaan Sun, Aixia Hayat, Hanaan Liu, Sihai Talebloo, Nazanin Pinger, Cody Bishop, Jack Owen Gudi, Mithil Dwan, Bennett Francis Ma, Xiaohong Zhao, Yanfeng Moore, Anna Wang, Ping affil: Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, 48823, East Lansing, MI, USA sug: subj: Artificial Intelligence Physical Sciences Islets of Langerhans Transplantation Molecular Imaging Algorithms Tomography, X-Ray Computed Imaging, Three-Dimensional Mice Animal Studies Models, Biological Islets of Langerhans Kidney Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Clinical Assessment Tools ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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