Ultra-fast/Low-dose PET/CT imaging Using Transformers

This work aims to explore the performance of novel deep learning approaches in PET administrated dose reduction. Dose reduction in nuclear medicine modalities would lead to image quality degradation and low diagnostic value. This study enrolled 76 clinical PET images from a Biograph mCT PET/CT scanner acquired in the list-mode format. Retrospectively collected data were used to propose a low-dose(LD) to full-dose(FD) translation model. All patients underwent the standard scan protocol with a 20 min acquisition time. 5% of the collected List-mode events were utilized to simulate the corresponding LD counterparts. A vision transformer network comprised of transformer and convolutional blocks were implemented to predict FD data from the LD images in the image space. The quality of synthetic PET images generated by the transformer model was assessed by some standard quantitative metrics like peak signal-to-noise ratio (PSNR), root means squared error (RMSE), and structural similarity index measure (SSIM), and Correlation parameter. Moreover, a joint histogram analysis was performed on the LD and predicted FD images with respect to the reference FD images. Regarding the quantitative analysis, the SSIM, PSNR, and Correlation rose considerably by 25%, 87%, and 7.6%, respectively, as opposed to an 80% decline for RMSE. The joint histogram plotted for the generated FD images resulted in a superior agreement with standard FD data in comparison with the LD data. Altogether, based on the visual illustration, the noise was suppressed effectively and the underlying information was preserved appropriately in the predicted FD images.