Unpaired Deep Learning Synthesis of Photon-Counting CT Material Basis Maps from Non-contrast Energy-Integrating Abdominal CT Images

Abstract

Photon-counting Computed Tomography (PCCT) is the most advanced Computed Tomography (CT) technology, offering significant improvements in image quality and diagnostic capabilities. However, since PCCT has only recently been adopted in the clinc, there are no publicly available PCCT image datasets for study. We therefore aim to synthesize PCCT spectral material-basis images from publicly available EID CT images. We propose a two-step deep learning model designed to synthesize photon-counting spectral material basis images from public Energy-Integrating Detector (EID) CT images. In the first step, we use a Denoising Diffusion Implicit Model (DDIM) to generate EID CT images from PCCT images. In the second step we use a U-Net with a Domain-Adversarial Neural Network to predict water and iodine maps from generated EID CT images. We also reconstruct basis images and virtual monoenergetic images (VMIs) from the predicted material-basis maps for evaluation. We evaluated the generated water and iodine maps as well as the 40 and 70 keV PCCT images in terms of Hounsfield Unit accuracy, modulation transfer function and noise power spectrum as well as qualitative image appearance. The reconstructed 40 and 70 keV PCCT images exhibit higher spatial resolution while preserving the anatomical structures and textures of the original EID CT images, thereby demonstrating the feasibility of the proposed approach. The proposed framework provides a feasible approach for synthesizing PCCT spectral material-basis images from conventional EID CT without requiring paired images. This method has the potential to provide large sets of synthetic training and evaluation data for PCCT algorithm development in data-limited environments.