Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.
Purpose: To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. Methods: We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3–30 years) to develop...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 48; no. 9; pp. 2771 - 2782 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2021
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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=151291750&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151291750 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Aug2021 vid: 48 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151291750 148417942 10.1007/s00259-021-05197-3 151291750 ppf: 2771 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. aug: au: Wang, Yan-Ran (Joyce) Baratto, Lucia Hawk, K. Elizabeth Theruvath, Ashok J. Pribnow, Allison Thakor, Avnesh S. Gatidis, Sergios Lu, Rong Gummidipundi, Santosh E. Garcia-Diaz, Jordi Rubin, Daniel Daldrup-Link, Heike E. affil: Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Road, 94304, Stanford, CA, USA sug: ab: Purpose: To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. Methods: We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3–30 years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. Results: The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers' diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). Conclusions: Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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