Single-Subject Deep-Learning Image Reconstruction with a Neural Optimization Transfer Algorithm for PET-enabled Dual-Energy CT Imaging

TL;DR

This paper introduces a novel single-subject deep learning approach for PET-enabled dual-energy CT imaging that enhances image quality without requiring large training datasets, using neural network-based optimization transfer.

Contribution

It develops a neural network-based single-subject reconstruction method for PET-enabled DECT that does not rely on population training data, improving image quality and material decomposition.

Findings

Significantly improved gCT image quality in simulations and real data.

Effective neural network optimization transfer algorithm guarantees monotonic likelihood increase.

Enhanced multi-material decomposition compared to existing methods.

Abstract

Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-ray CT scan. The recent PET-enabled DECT method allows DECT imaging on PET/CT without requiring a second X-ray CT scan. It combines the already existing X-ray CT image with a 511 keV \gamma-ray CT (gCT) image reconstructed from time-of-flight PET emission data. A kernelized framework has been developed for reconstructing gCT image but this method has not fully exploited the potential of prior knowledge. Use of deep neural networks may explore the power of deep learning in this application. However, common approaches require a large database for training, which is impractical for a new imaging method like PET-enabled DECT. Here, we propose a single-subject method by using neural-network representation as a deep coefficient prior to improving gCT image reconstruction without population-based pre-training. The resulting optimization problem becomes the tomographic estimation of nonlinear neural-network parameters from gCT projection data. This complicated problem can be efficiently solved by utilizing the optimization transfer strategy with quadratic surrogates. Each iteration of the proposed neural optimization transfer algorithm includes: PET activity image update; gCT image update; and least-square neural-network learning in the gCT image domain. This algorithm is guaranteed to monotonically increase the data likelihood. Results from computer simulation, real phantom data and real patient data have demonstrated that the proposed method can significantly improve gCT image quality and consequent multi-material decomposition as compared to other methods.