Direct patlak reconstruction from dynamic PET using unsupervised deep learning

Abstract

Direct reconstruction methods have been developed to estimate parametric images directly from the measured sinogram by combining the PET imaging model and tracer kinetics in an integrated framework. Due to limited counts received, especially for low-dose scenarios, SNR and resolution of parametric images produced by direct reconstruction frameworks are still limited. Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number of high-quality training labels are available. For static PET imaging, high-quality training labels can be acquired by extending scanning time. However, this is not feasible for dynamic PET imaging, where the scanning time is already long enough. In this work, we present a novel unsupervised deep learning method for direct Patlak reconstruction from low-dose dynamic PET. The training label is measured sinogram itself and the only requirement is the patients own anatomical prior image, which is readily available from PET/CT or PET/MR scans. Experiment evaluation based on a low-dose dynamic dataset shows that the proposed method can outperform Gaussian post-smoothing and anatomically-guided direct reconstruction using the kernel method.