A Model-based Deep Learning Reconstruction for X-ray CT

Autor:	Liang, Kaichao, Zhang, Li, Yang, Yirong, Yang, HongKai, Xing, Yuxiang
Rok vydání:	2019
Předmět:	Physics - Medical Physics Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Low dose CT is of great interest in these days. Dose reduction raises noise level in projections and decrease image quality in reconstructions. Model based image reconstruction can combine statistical noise model together with prior knowledge into an Bayesian optimization problem so that significantly reduce noise and artefacts. In this work, we propose a model-base deep learning for CT reconstruction so that a reconstruction network can be trained with no ground-truth images needed. Instead of minimizing cost function for each image, the network learns to minimize an ensemble cost function for the whole training set. No iteration will be needed for real data reconstruction using such a trained network. We experimented with a penalized weighted least-squares (PWLS) cost function for low dose CT reconstruction and tested on data from a practical dental CT. Very encouraging results with great noise reductions are obtained. Comment: 9 pages, 3 figures, conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.06940 Zobrazit plný text záznamu View this record from Arxiv