Abstract

Objective This study evaluates the feasibility of direct scatter and attenuation correction of whole-body Ga-68-PSMA PET images in the image domain using deep learning. Methods Whole-body Ga-68-PSMA PET images of 399 subjects were used to train a residual deep learning model, taking PET non-attenuation-corrected images (PET-nonAC) as input and CT-based attenuation-corrected PET images (PET-CTAC) as target (reference). Forty-six whole-body Ga-68-PSMA PET images were used as an independent validation dataset. For validation, synthetic deep learning-based attenuation-corrected PET images were assessed considering the corresponding PET-CTAC images as reference. The evaluation metrics included the mean absolute error (MAE) of the SUV, peak signal-to-noise ratio, and structural similarity index (SSIM) in the whole body, as well as in different regions of the body, namely, head and neck, chest, and abdomen and pelvis. Results The deep learning-guided direct attenuation and scatter correction produced images of comparable visual quality to PET-CTAC images. It achieved an MAE, relative error (RE), SSIM, and peak signal-to-noise ratio of 0.91 +/- 0.29 (SUV), -2.46 +/- 10.10, 0.973 +/- 0.034, and 48.171 +/- 2.964, respectively, within whole-body images of the independent external validation dataset. The largest RE was observed in the head and neck region (-5.62 +/- 11.73), although this region exhibited the highest value of SSIM metric (0.982 +/- 0.024). The MAE (SUV) and RE within the different regions of the body were less than 2.0 and 6, respectively, indicating acceptable performance of the deep learning model. Conclusions This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body Ga-68-PSMA PET images in the image domain using deep learning with clinically tolerable errors. The technique has the potential of performing attenuation correction on stand-alone PET or PET/MRI systems.