Improved 2.5D-UNet pseudo-CT generation for attenuation correction in PET imaging using 18F-fluorodeoxyglucose and 11C-acetoacetate radiotracers

Attenuation in PET imaging can lead to quantification errors, underestimation of lesions and misinterpretation of the extent of disease. Commonly, this correction is made using a CT image that provides additional anatomical information. Unfortunately, using CT significantly increases the radiation dose received by patients, increases the cost of imaging equipment, and can cause misregistration errors. To overcome these limitations, deep-learning based methods were proposed to generate a pseudo-CT from the non-attenuation-corrected (NAC) PET image, which can then be used to correct for attenuation. In this work, an improved UNet 2.5D architecture has been developed in which three consecutive PET image slices are used to provide more contextual information to generate a single CT slice. A dataset of 189 NAC head PET scans from patients injected with \\(^{18}\\) F-fluorodeoxyglucose or \\(^{11}\\) C-acetoacetate, and the corresponding CT images were used for training. The generated pseudo-CT of all patients showed an overall improvement with the proposed UNet 2.5D architecture. Compared to a standard UNet 2D architecture, the soft tissue dice score increased from (0.905 ± 0.015) to (0.922 ± 0.017), along with the SSIM score increasing from (0.889 ± 0.023) to (0.921 ± 0.019). 3D forward projections comparing the CT and pseudo-CT total attenuation projection values also highlight the improved attenuation maps generated by the proposed 2.5D deep learning architecture. The proposed 2.5D UNet, using adjacent PET image slices for added context, outperforms the 2D UNet in generating accurate pseudo-CTs from NAC PET images, enabling effective and fast attenuation correction without additional radiation or imaging.