Share Email Print

Proceedings Paper

Learning from our neighbours: a novel approach on sinogram completion using bin-sharing and deep learning to reconstruct high quality 4DCBCT
Author(s): Joel Beaudry; Pedro L. Esquinas; Chun-Chien Shieh
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Inspired by the success of deep learning applications on restoration of low-dose and sparse CT images, we propose a novel method to reconstruct high-quality 4D cone-beam CT (4DCBCT) images from sparse datasets. Our approach combines the idea of bin-sharing with a deep convolutional neural network (CNN) model. More specifically, for each respiratory bin, an initial estimate of the patient sinogram is obtained by taking projections from adjacent bins and performing linear interpolation. Subsequently, the estimated sinogram is propagated through a CNN that predicts a full, high-quality sinogram. Lastly, the predicted sinogram is reconstructed with an iterative CBCT algorithms such as the Conjugate Gradient (CG) method. The CNN model, which we referred to as the Sino-Net, was trained under different loss functions. We assessed the performance of the proposed method in terms of image quality metrics (mean square error, mean absolute error, peak signal-to-noise ratio and structural similarity) and tumor motion accuracy (tumor centroid deviation with respect to the ground truth). Lastly, we compared our approach against other state-of-the-art methods that compensate motion and reconstruct 4DCBCTs. Overall, the presented prototype model was able to substantially improve the quality of 4DCBCT images, removing most of the streak artifacts and decreasing the noise with respect to the standard CG reconstructions.

Paper Details

Date Published: 1 March 2019
PDF: 11 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094847 (1 March 2019); doi: 10.1117/12.2513168
Show Author Affiliations
Joel Beaudry, Univ. of Toronto (Canada)
Lakeridge Health (Canada)
Pedro L. Esquinas, The Univ. of British Columbia (Canada)
Chun-Chien Shieh, The Univ. of Sydney (Australia)

Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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