PET-enabled Dual-Energy CT: Image Reconstruction and A Proof-of-Concept Computer Simulation Study

TL;DR

This paper introduces a novel dual-energy CT imaging method leveraging PET data to generate gamma-ray attenuation images, enabling material decomposition without additional X-ray scans or hardware upgrades.

Contribution

The study proposes a PET-enabled dual-energy CT technique using MLAA algorithms and prior X-ray data, facilitating simultaneous PET and dual-energy CT imaging.

Findings

Simulation results demonstrate effective material decomposition.

The method can be implemented on existing PET/CT scanners.

The approach reduces the need for hardware upgrades or additional scans.

Abstract

Standard dual-energy computed tomography (CT) uses two different X-ray energies to obtain energy-dependent tissue attenuation information to allow quantitative material decomposition. The combined use of dual-energy CT and positron emission tomography (PET) may provide a more comprehensive characterization of disease states in cancer and many other diseases. However, the integration of dual-energy CT with PET is not trivial, either requiring costly hardware upgrade or increasing radiation dose. This paper proposes a novel dual-energy CT imaging method that is enabled by the already-available PET data on PET/CT. Instead of using a second X-ray CT scan with a different energy, this method exploits time-of-flight PET image reconstruction via the maximum likelihood attenuation and activity (MLAA) algorithm to obtain a 511 keV gamma-ray attenuation image from PET emission data. The high-energy gamma-ray CT image is then combined with the low-energy X-ray CT of PET/CT to provide a pair of dual-energy CT images. A major challenge with the standard MLAA reconstruction is the high noise present in the reconstructed 511 keV attenuation map, which does not compromise the PET activity reconstruction too much but significantly affects the performance of the gamma-ray CT for material decomposition. To overcome the problem, we further propose a kernel MLAA algorithm to exploit the prior information from the available X-ray CT image. We conducted a computer simulation to test the concept and algorithm for the task of material decomposition. The simulation results indicate that this PET-enabled dual-energy CT method is promising for quantitative material decomposition. The proposed method can be readily implemented on time-of-flight PET/CT scanners to enable simultaneous PET and dual-energy CT imaging.