Direct Patlak Reconstruction for Low-Dose Dynamic PET Using Unsupervised Deep Learning

575 Objectives: 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 patient’s own anatomical prior image, which is readily available from PET/CT or PET/MR scans. Methods: In this proposed method, the main idea is to represent the last n dynamic images by a neural network’s n-channel output: [xt,xt+1,...,xt+n-1]=f(m|z), where f(∙) is the neural network, m represents the unknown network parameters and z denotes the network input. The Unet structure was coupled with the Patlak convolutional layer to generate the last n dynamic images. The two-channel output of Unet itself is the Patlak slope and intercept images. The Patlak layer converts the Patlak slope and intercept images into the tracer activity [xt,xt+1,...,xt+n-1]. It is implemented using a 1x1 convolution with weights determined by the blood input function based on the Patlak model. The input to the network is the pateint’s simultaneously acquired T1-weighted MR image. Through this representation, the task of reconstructing unknown dynamic images is transferred to optimizing the network parameters that maximize the likelihood function. The final network structure is shown in Fig 1(a) and the whole optimization flowchart based on ADMM algorithm is shown in Fig 1(b). To validate the proposed method, a 70-minutes dynamic FDG PET scan of a human subject was acquired on the Siemens Brain MR-PET scanner after a 5 mCi injection. The listmode data was down-sampled to 1/5th of the original count to create a low-dose dynamic dataset equivalent to 1mCi injection. The dynamic PET data were divided into 25 frames: 4x20 s, 4x40 s, 4x60 s, 4x180 s, 8x300 s and 1x600 s. For quantitative comparison in the case where MRI and PET information does not match, an artificial spherical lesion of diameter 12.5 mm was inserted to the PET data (invisible in the MRI image). For the direct Patlak reconstruction, the last six frames were used. The data were reconstructed into an image array of 256x256x153 voxels with a voxel size of 1.25x1.25x1.25 mm3. To obtain the blood input function, blood regions were segmented from the T1-weighted MRI image. The blood input function was extracted from the frame-by-frame OSEM reconstruction. Direct reconstruction using the nested EM algorithm with Gaussian filtering (EM + filter), and the kernel-method-based direct reconstruction using MR as priors (KMRI) are used as baseline methods for comparison. Results: Fig 2 shows the reconstructed Patlak slope images using different methods. The image obtained by the proposed method shows the highest lesion contrast with clear cortical structure. Furthermore, the proposed method correctly recovered the spherical lesion shape compared with the KMRI methods. Conclusions: We have proposed a novel unsupervised-deep-learning-based method for direct Patlak reconstruction from low-dose dynamic PET. Reconstruction results based on a clinical PET/MR dataset shows that the tumor intensity, tumor shape and cortex details are better recovered in the proposed method compared to other methods. Acknowledgements: This work was supported by NIH grants R01 AG052653 and P41 EB022544.