A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET

PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers. METHODS: Brain [(18)F]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [(18)F]FDG PET images of 45 patients scanned with three different scanners, [(18)F]FET PET images of 18 patients scanned with two different scanners, as well as [(18)F]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting. RESULTS: The improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = −0.71, p < 0.05) and normalized dose acquisition (r = −0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05). CONCLUSION: The deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-021-05644-1

Column-averaged dry-air mole fractions of CO2, CH4 and CO recorded during an urban measurement campaign in Munich in August 2018 with five solar-tracking Fourier transform spectrometers (EM27/SUN)

In August 2018, we deployed five solar-tracking Fourier transform spectrometers (EM27/SUN) in the urban area of Munich to measure the column-averaged dry-air mole fraction of the three gases carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO). The setup is characterized by a central station and a station in each compass direction, spaced about 20 km apart, to measure the inflow and outflow of the gas concentrations under any wind conditions. With this setup, it is possible to determine urban greenhouse gas emissions when an appropriate atmospheric model is applied to these concentration data in conjunction with additional meteorological parameters such as wind