Cycle-consistent learning-based hybrid iterative reconstruction for whole-body PET imaging

Objective. To develop a cycle-consistent learning-based hybrid iterative reconstruction (IR) method that takes only slightly longer than analytic reconstruction, while pursuing the image resolution and tumor quantification achievable by IR for whole-body PET imaging. Approach. We backproject the raw positron emission tomography (PET) data to generate a blurred activity distribution. From the backprojection to the IR label, a reconstruction mapping that approximates the deblurring filters for the point spread function and the physical effects of the PET system is unrolled to a neural network with stacked convolutional layers. By minimizing the cycle-consistent loss, we train the reconstruction and inverse mappings simultaneously. Main results. In phantom study, the proposed method results in an absolute relative error (RE) of the mean activity of 4.0% +/- 0.7% in the largest hot sphere, similar to the RE of the full-count IR and significantly smaller than that obtained by CycleGAN postprocessing. Achieving a noise reduction of 48.1% +/- 0.5% relative to the low-count IR, the proposed method demonstrates advantages over the low-count IR and CycleGAN in terms of resolution maintenance, contrast recovery, and noise reduction. In patient study, the proposed method obtains a noise reduction of 44.6% +/- 8.0% for the lung and the liver, while maintaining the regional mean activity in both simulated lesions and real tumors. The run time of the proposed method is only half that of the conventional IR. Significance. The proposed cycle-consistent learning from the backprojection rather than the raw PET data or an IR result enables improved reconstruction accuracy, reduced memory requirements, and fast implementation speeds for clinical whole-body PET imaging.