Independent brain <sup>18</sup>F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks.
Karim Armanious, Thomas Küstner, Matthias Reimold, Konstantin Nikolaou, Christian la Fougère, Bin Yang, Sergios Gatidis
Abstract
OBJECTIVE: F-FDG) PET images only based on PETNAC using Generative Adversarial Networks (GAN). SUBJECTS AND METHODS: of 40 validation patients, of which 20 were used for technical validation and 20 stemming from patients with CNS disorders were used for clinical validation. Pseudo-CT was used for subsequent AC of these validation data sets resulting in independently attenuation-corrected PET data. RESULTS: Visual inspection revealed a high degree of resemblance of generated pseudo-CT images compared to the acquired CT images in all validation data sets, with minor differences in individual anatomical details. Quantitative analyses revealed minimal underestimation below 5% of standardized uptake value (SUV) in all brain regions in independently attenuation-corrected PET data compared to the reference PET images. Color-coded error maps showed no regional bias and only minimal average errors around ±0%. Using independently attenuation-corrected PET data, no differences in image-based diagnoses were observed in 20 patients with neurological disorders compared to the reference PET images. CONCLUSION: F-FDG PET is feasible with high accuracy using the proposed, easy to implement deep learning framework. Further evaluation in clinical cohorts will be necessary to assess the clinical performance of this method.