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Integrated use of dual-energy computed tomography (DECT) with positronemission tomography (PET) has many potential clinical applications. However,the integration would either require costly hardware upgrade or increaseradiation dose on PET/CT scanners due to the need for an additional Xray CTscan. The recently proposed PET-enabled DECT method enables DECT imaging onPET/CT without requiring the second X-ray CT scan. It combines thealready-existing low-energy X-ray CT image with a 511 keV {\gamma}-ray CT (gCT)image reconstructed from time-of-flight PET emission data. A kernelizedattenuation and activity (KAA) estimation method has been developed forreconstructing the gCT image from PET but the method has not fully exploitedthe potential of image prior knowledge. In this work, we propose a neural KAAmethod by using neural network representation as a deep coefficient prior toimprove the existing KAA method. The resulting maximum-likelihood neuralnetwork-based reconstruction problem can be efficiently solved by utilizing thetheory of optimization transfer. Each iteration of the algorithm consists ofthree modular steps: PET activity image update, gCT image update, andneural-network learning in the image domain. This algorithm is guaranteed tomonotonically increase the data likelihood. The results from computersimulation and real phantom data have demonstrated that the proposed neural KAAmethod can significantly improve gCT image quality and consequentmulti-material decomposition as compared to other methods.

PET-Enabled Dual-Energy CT Using a Kernel Method with Neural Optimization Transfer