Reconstruction of total-body multi parametric images with shortened-duration dynamic [⁶⁸Ga]Ga-PSMA-11 and [⁶⁸Ga]Ga-FAPI-04 PET scans

Objective: The lengthy one-hour dynamic PET scans discomfort patients, add motion artifacts, and inflate costs, highlighting the need for tech advancements to reduce scan times. Therefore, we attempted to reconstruct multi-parametric imaging with only 20 min dynamic PET frames through deep learning techniques. Methods: Total-body PET data from 49 subjects (18 [68Ga]Ga-FAPI-04 and 31 [68Ga]Ga-PSMA-11) at Renji Hospital were used. We employed the dynamic PET frames prediction, parametric linear fitting and GAN to form the whole deep learning framework accomplishing reconstruction of multi-parametric images ([68Ga]Ga-FAPI-04:K1, DV and vB; [68Ga]Ga-PSMA-11:K1, Ki and vB) from 20 min dynamic PET frames. The PSNR and SSIM were used to evaluate the quality of the synthesized results. Statistical analyses , including PCC and violin plot, were also used for the reconstruction assessments. Results: The reconstructed multi-parametric images using the proposed technique closely resembled their full-time parametric counterparts, demonstrating high image quality across various tissue types. The reconstructed images resulting from the [68Ga]Ga-FAPI-04 and [68Ga]Ga-PSMA-11 tracers outperform 5 comparison methods, yielding average PSNR values of 35.5535 dB and 36.4040 dB, along with SSIM scores of 0.9798 and 0.9630, respectively. Additionally, the PCCs within the ROIs exhibited strong correlations (above 0.92) with the ground truth. Moreover, the imaging results in the pancreatic and prostate areas matched well with full-time parametric images. Conclusion: Our approach shortens the scan duration of dynamic PET from above one hour to 20 minutes, which greatly improves the experience for patients and reduces motion artifacts and costs.