A novel deep learning-based method for monochromatic image synthesis from spectral CT using photon-counting detectors

TL;DR

This paper introduces a new deep learning approach for synthesizing monochromatic images from spectral CT data obtained with photon-counting detectors, addressing issues caused by detector non-idealities.

Contribution

It proposes a novel neural network architecture based on the physical model of detector cross talk, improving monochromatic image synthesis accuracy in spectral CT.

Findings

More accurate monochromatic images with less noise.

Effective correction of detector non-idealities in spectral CT.

Feasibility demonstrated on a cone-beam CT system.

Abstract

With the growing technology of photon-counting detectors (PCD), spectral CT is a widely concerned topic which has the potential of material differentiation. However, due to some non-ideal factors such as cross talk and pulse pile-up of the detectors, direct reconstruction from detected spectrum without any corrections will get a wrong result. Conventional methods try to model these factors using calibration and make corrections accordingly, but depend on the preciseness of the model. To solve this problem, in this paper, we proposed a novel deep learning-based monochromatic image synthesis method working in sinogram domain. Different from previous deep learning-based methods aimed at this problem, we designed a novel network architecture according to the physical model of cross talk, and it can solve this problem better in an ingenious way. Our method was tested on a cone-beam CT (CBCT) system equipped with a PCD. After using FDK algorithm on the corrected projection, we got quite more accurate results with less noise, which showed the feasibility of monochromatic image synthesis by our method.