Abstract
BACKGROUND: Attenuation correction (AC) of PET data is usually performed using a second imaging for the generation of attenuation maps. In certain situations however-when CT- or MR-derived attenuation maps are corrupted or CT acquisition solely for the purpose of AC shall be avoided-it would be of value to have the possibility of obtaining attenuation maps only based on PET information. The purpose of this study was to thus develop, implement, and evaluate a deep learning-based method for whole body [(18)F]FDG-PET AC which is independent of other imaging modalities for acquiring the attenuation map. METHODS: The proposed method is investigated on whole body [(18)F]FDG-PET data using a Generative Adversarial Networks (GAN) deep learning framework. It is trained to generate pseudo CT images (CT(GAN)) based on paired training data of non-attenuation corrected PET data (PET(NAC)) and corresponding CT data. Generated pseudo CTs are then used for subsequent PET AC. One hundred data sets of whole body PET(NAC) and corresponding CT were used for training. Twenty-five PET/CT examinations were used as test data sets (not included in training). On these test data sets, AC of PET was performed using the acquired CT as well as CT(GAN) resulting in the corresponding PET data sets PET(AC) and PET(GAN). CT(GAN) and PET(GAN) were evaluated qualitatively by visual inspection and by visual analysis of color-coded difference maps. Quantitative analysis was performed by comparison of organ and lesion SUVs between PET(AC) and PET(GAN). RESULTS: Qualitative analysis revealed no major SUV deviations on PET(GAN) for most anatomic regions; visually detectable deviations were mainly observed along the diaphragm and the lung border. Quantitative analysis revealed mean percent deviations of SUVs on PET(GAN) of - 0.8 ± 8.6% over all organs (range [- 30.7%, + 27.1%]). Mean lesion SUVs showed a mean deviation of 0.9 ± 9.2% (range [- 19.6%, + 29.2%]). CONCLUSION: Independent AC of whole body [(18)F]FDG-PET is feasible using the proposed deep learning approach yielding satisfactory PET quantification accuracy. Further clinical validation is necessary prior to implementation in clinical routine applications.