To Recurse or not to Recurse,a Low Dose CT Study

Autor:	Bazrafkan, Shabab, Van Nieuwenhove, Vincent, Sijbers, Jan
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Restoring high-quality CT images from low dose CT counterparts is an ill-posed, nonlinear problem to which Deep Learning approaches have been giving superior solutions compared to classical model-based approaches. In this article, a framework is presented wherein a Recurrent Neural Network (RNN) is utilized to remove the streaking artefacts from low projection number CT imaging. The results indicate similar image restoration performance for the RNN compared to the feedforward network in low noise cases while in high noise levels the RNN returns better results. The computational costs are also compared between RNN and feedforward networks. Comment: Sections II.A to II.D is taken from sections II.A to II.D of arXiv:1904.03908 which is an unpublished article from the same authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.06565 Zobrazit plný text záznamu View this record from Arxiv