Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network

TL;DR

This paper introduces a unified generative adversarial network (uGAN) that converts standard energy-integrating CT images into energy-resolved CT images at multiple energies simultaneously, enabling quantitative material imaging without specialized detectors.

Contribution

The novel uGAN model performs multi-energy ErCT image synthesis from EiCT images in a single training process, unlike previous methods that generate images at one specific energy.

Findings

uGAN accurately estimates ErCT images at multiple energies.

The model was validated on over 1380 CT slices with promising results.

Quantitative and qualitative evaluations show high fidelity of generated images.

Abstract

Energy-resolving computed tomography (ErCT) has the ability to acquire energy-dependent measurements simultaneously and quantitative material information with improved contrast-to-noise ratio. Meanwhile, ErCT imaging system is usually equipped with an advanced photon counting detector, which is expensive and technically complex. Therefore, clinical ErCT scanners are not yet commercially available, and they are in various stage of completion. This makes the researchers less accessible to the ErCT images. In this work, we investigate to produce ErCT images directly from existing energy-integrating CT (EiCT) images via deep neural network. Specifically, different from other networks that produce ErCT images at one specific energy, this model employs a unified generative adversarial network (uGAN) to concurrently train EiCT datasets and ErCT datasets with different energies and then performs image-to-image translation from existing EiCT images to multiple ErCT image outputs at various energy bins. In this study, the present uGAN generates ErCT images at 70keV, 90keV, 110keV, and 130keV simultaneously from EiCT images at140kVp. We evaluate the present uGAN model on a set of over 1380 CT image slices and show that the present uGAN model can produce promising ErCT estimation results compared with the ground truth qualitatively and quantitatively.