Open AccessLow‐dose computed tomography scheme incorporating residual learning‐based denoising with iterative reconstruction
Author(s) -
Ding Yong,
Hu Tuo
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2018.6449
Subject(s) - iterative reconstruction , residual , noise reduction , artificial intelligence , iterative method , projection (relational algebra) , computer science , radon transform , computer vision , artificial neural network , noise (video) , algorithm , pattern recognition (psychology) , image (mathematics)
Low‐dose computed tomography has been highly desirable because of the health concern about excessive radiation dose, but also challenging due to insufficient or noisy projection data. Compared with post‐processing methods by directly denoising filtered back‐projection images, iterative reconstruction achieves excellent performance but consumes a large number of iterations. In this Letter, a two‐stage method is proposed by incorporating residual learning‐based denoising with iterative reconstruction. First, an intermediate image is reconstructed by compressed sensing iterative reconstruction. Then, the image is denoised by a deep neural network. Specially, a network performing two‐level residual learning is designed to strengthen denoising effect. Experimental results show that the proposed method outperforms iterative reconstruction with better numeric results and comparable visual performance while consuming fewer iterations.