Ultralow-dose CT imaging with deep learning noise reduction on EXPLORER total-body PET/CT scanner

3044 Introduction: The EXPLORER PET-CT scanner offers ultrahigh sensitivity for fast and low-dose PET scans and the ability of covering the entire body simultaneously for dynamic PET imaging. It has been routinely used for both clinical scans and research studies. In many situations such imaging of healthy subjects, longitudinal tracking of subjects with chronic diseases and imaging of pediatric patients, ultralow-dose CT imaging is desirable in order to minimize the radiation exposure from a combined PET/ CT scan. While an ultralow-dose CT scan can be sufficient for PET attenuation correction, it may compromise the CT image quality for anatomic localization and potentially quantification such as in the liver for steatosis evaluation. The objective of this paper is to demonstrate the potential of deep learning noise reduction for ultralow-dose CT imaging on the EXPLORER with an evaluation emphasis on liver CT quantification using clinical patient scans. Methods: Thirty patients who underwent a dual-time-point PET/CT scan were retrospectively included in this study. Each patient received the first PET/CT scan at 60 minutes post FDG injection and the second PET/CT scan at 90 minutes. The first attenuation correction (AC) CT scans were all performed with regular low-dose (RLD) technique factors (140 kVp, 50 mAs; effective dose estimate: 10 mSv). The second AC-CT scans were performed using ultralow-dose (ULD) technique factors (140 kVp, 5 mAs; effective dose estimate: 1 mSv). The patient datasets were further divided into separate analysis groups based on their scan position (arms-up: 24 subjects vs. arms-down: 6 subjects). The deep learning (DL) denoising model was trained using the Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) model based on the AAPM Mayo CT datasets. The ULD images and their deep learning denoised version (ULD-DL) were compared with the corresponding RLD CT scan of the same patient as the reference. Quantitative comparison was conducted for CT liver quantification (in Hounsfield units) using spherical liver ROI’s (50 mm in diameter) across the three approaches (ULD, ULD-DL, RLD). Results: The deep learning denoising was successfully applied in all patient scans. Figure 1 shows the comparisons for a patient from the arms-up group and a patient from the arms-down group. Compared with the regular low-dose scan, the ULD scan induced high noise across the majority of body sections. In addition, streak artifacts appeared around the shoulder and neck in the arms-up group and in the liver in the arms-down group. The ULD-DL approach dramatically reduced both noise and artifacts as compared to ULD. The ULD-DL approach reduced the noise standard deviation of the liver ROIs by a factor of 5 as compared to the ULD approach (Figure 2). ROI quantification by ULD and ULD-DL was highly correlated with the regular low-dose scans (Figure 3). The correlation was much higher in the arms-up group than in the arms-down group. Conclusion: Deep learning noise reduction is promising to improve ultralow-dose CT scans on the EXPLORER. Our study indicated that the ultralow-dose protocol provides comparable performance to regular low-dose protocol for CT liver quantification. Our results also indicate that compared with the arms-up group, the arms-down scanning position may result in severe artifacts which compromise liver CT quantification in both regular low-dose and ultralow-dose scans. Furthermore, deep learning can significantly reduce image noise without compromising liver CT quantification in the arms-up group. However, in the arms-down group, while deep learning can greatly reduce artifacts, improvement in liver quantification may still be limited. Acknowledgements: Support for this work includes NIH grant R01 CA206187, R21 EB027346 and a UC Davis Cancer Center Pediatric Oncology Pilot Grant.