Artificial Intelligence enables whole body Positron Emission Tomography Scans with minimal radiation exposure
| Autor: | Sergios Gatidis, Avnesh S. Thakor, Yan-Ran Joyce Wang, Santosh Gummidipundi, Lucia Baratto, Rong Lu, Jordi Garcia-Diaz, K. Elizabeth Hawk, Ashok J. Theruvath, Heike E. Daldrup-Link, Allison Pribnow, Daniel L. Rubin |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Wilcoxon signed-rank test
Image quality Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Young Adult 0302 clinical medicine Cohen's kappa Artificial Intelligence Fluorodeoxyglucose F18 medicine Image noise Humans Radiology Nuclear Medicine and imaging Whole Body Imaging Child medicine.diagnostic_test business.industry General Medicine Radiation Exposure Pediatric cancer Magnetic Resonance Imaging Radiation exposure Positron emission tomography 030220 oncology & carcinogenesis Positron-Emission Tomography Artificial intelligence Whole body business |
| Zdroj: | Eur J Nucl Med Mol Imaging |
| Popis: | PURPOSE: To generate diagnostic (18)F-FDG PET images of pediatric cancer patients from ultra-low dose (18)F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. METHODS: We used whole body (18)F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3–30 years) to developed a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose (18)F-FDG PET scans and simultaneously acquired MRI scans to produce a standard dose (18)F-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 significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p |
| Databáze: | OpenAIRE |
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