Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. Academic Article uri icon

Overview

abstract

  • 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.

authors

  • Wang, Yan-Ran
  • 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

publication date

  • February 1, 2021

Research

keywords

  • Artificial Intelligence
  • Radiation Exposure

Identity

PubMed Central ID

  • PMC8266729

Scopus Document Identifier

  • 85100269191

Digital Object Identifier (DOI)

  • 10.1007/s00259-021-05197-3

PubMed ID

  • 33527176

Additional Document Info

volume

  • 48

issue

  • 9