Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain ¹⁸F-FDG PET

Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diagnosis of neurodegenerative diseases. However, when a dedicated brain PET scanner does not come with a combined CT or transmission source, there is no direct solution for accurate attenuation and scatter correction, both of which are critical for quantitative PET. To address this problem, we propose joint attenuation and scatter correction (ASC) in image space for non-corrected PET (PET_NC) using deep convolutional neural networks (DCNNs). This approach is a one-step process, distinct from conventional methods that rely on generating attenuation maps first that are then applied to iterative scatter simulation in sinogram space. For training and validation, time-of-flight PET/MR scans and additional helical CTs were performed for 35 subjects (25/10 split for training and test dataset). A DCNN model was proposed and trained to convert PET_NC to DCNN-based ASC PET (PET_DCNN) directly in image space. For quantitative evaluation, uptake differences between PET_DCNN and reference CT-based ASC PET (PET_CT-ASC) were computed for 116 automated anatomical labels (AALs) across 10 test subjects (1160 regions in total). MR-based ASC PET (PET_MR-ASC), a current clinical protocol in PET/MR imaging, was another reference for comparison. Statistical significance was assessed using a paired t test. The performance of PET_DCNN was comparable to that of PET_MR-ASC, in comparison to reference PET_CT-ASC. The mean SUV differences (mean ± SD) from PET_CT-ASC were 4.0% ± 15.4% (P < 0.001) and -4.2% ± 4.3% (P < 0.001) for PET_DCNN and PET_MR-ASC, respectively. The overall larger variation of PET_DCNN (15.4%) was prone to the subject with the highest mean difference (48.5% ± 10.4%). The mean difference of PET_DCNN excluding the subject was substantially improved to -0.8% ± 5.2% (P < 0.001), which was lower than that of PET_MR-ASC (-5.07% ± 3.60%, P < 0.001). In conclusion, we demonstrated the feasibility of directly producing PET images corrected for attenuation and scatter using a DCNN (PET_DCNN) from PET_NC in image space without requiring conventional attenuation map generation and time-consuming scatter correction. Additionally, our DCNN-based method provides a possible alternative to MR-ASC for simultaneous PET/MRI.