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

Autor:	Zheng, Ao, Yang, Hongkai, Zhang, Li, Xing, Yuxiang
Rok vydání:	2020
Předmět:	Physics - Medical Physics Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 9 pages, 4 figures, submitted to the 2020 IEEE Nuclear Science Symposium (NSS) and Medical Imaging Conference (MIC)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.09870 Zobrazit plný text záznamu View this record from Arxiv