Share Email Print
cover

Proceedings Paper

Bone induced artifacts elimination using two-step convolutional neural network
Author(s): Bin Su; Yanyan Liu; Yifeng Jiang; Jianwei Fu; Guotao Quan
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Bone induced artifacts caused by spectral absorption of skull is intrinsic to head images in CT. Artifacts which blur the images and further temper with the diagnostic power of CT. Several algorithms have been proposed to address the artifacts, but most are complex and take long time to eliminate the artifacts. In the past decade, the deep learning (DL) approach has demonstrated excellent effects in image processing. In this work, we present a twostep convolutional neural networks (CNNs) that reduces the artifacts. First step uses the U-shape network (UNet) to learn and correct the low frequency artifacts. Second step uses residual network (ResNet) to extract the high frequency artifacts. Our proposed method is capable of eliminating the bone induced artifacts within a relatively low time cost. Promising results have been obtained in our experiment with a large number of CT head images.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107221 (28 May 2019); doi: 10.1117/12.2534965
Show Author Affiliations
Bin Su, United Imaging Healthcare (China)
Yanyan Liu, United Imaging Healthcare (China)
Yifeng Jiang, United Imaging Healthcare (China)
Jianwei Fu, United Imaging Healthcare (China)
Guotao Quan, United Imaging Healthcare (China)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray