  Patlak model is widely used in 18F-FDG dynamic positron emission tomography
(PET) imaging, where the estimated parametric images reveal important
biochemical and physiology information. Because of better noise modeling and
more information extracted from raw sinogram, direct Patlak reconstruction
gains its popularity over the indirect approach which utilizes reconstructed
dynamic PET images alone. As the prerequisite of direct Patlak methods, raw
data from dynamic PET are rarely stored in clinics and difficult to obtain. In
addition, the direct reconstruction is time-consuming due to the bottleneck of
multiple-frame reconstruction. All of these impede the clinical adoption of
direct Patlak reconstruction.In this work, we proposed a data-driven framework
which maps the dynamic PET images to the high-quality motion-corrected direct
Patlak images through a convolutional neural network. For the patient motion
during the long period of dynamic PET scan, we combined the correction with the
backward/forward projection in direct reconstruction to better fit the
statistical model. Results based on fifteen clinical 18F-FDG dynamic brain PET
datasets demonstrates the superiority of the proposed framework over Gaussian,
nonlocal mean and BM4D denoising, regarding the image bias and
contrast-to-noise ratio.
