2 research outputs found
Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging
X-ray computed tomography (CT) reconstructs cross-sectional images from
projection data. However, ionizing X-ray radiation associated with CT scanning
might induce cancer and genetic damage. Therefore, the reduction of radiation
dose has attracted major attention. Few-view CT image reconstruction is an
important topic to reduce the radiation dose. Recently, data-driven algorithms
have shown great potential to solve the few-view CT problem. In this paper, we
develop a dual network architecture (DNA) for reconstructing images directly
from sinograms. In the proposed DNA method, a point-based fully-connected layer
learns the backprojection process requesting significantly less memory than the
prior arts do. Proposed method uses O(C*N*N_c) parameters where N and N_c
denote the dimension of reconstructed images and number of projections
respectively. C is an adjustable parameter that can be set as low as 1. Our
experimental results demonstrate that DNA produces a competitive performance
over the other state-of-the-art methods. Interestingly, natural images can be
used to pre-train DNA to avoid overfitting when the amount of real patient
images is limited.Comment: 11 pages, 5 figures, 2019 SPIE Optical Engineering + Application
Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
Breast CT provides image volumes with isotropic resolution in high contrast,
enabling detection of small calcification (down to a few hundred microns in
size) and subtle density differences. Since breast is sensitive to x-ray
radiation, dose reduction of breast CT is an important topic, and for this
purpose, few-view scanning is a main approach. In this article, we propose a
Deep Efficient End-to-end Reconstruction (DEER) network for few-view breast CT
image reconstruction. The major merits of our network include high dose
efficiency, excellent image quality, and low model complexity. By the design,
the proposed network can learn the reconstruction process with as few as O(N)
parameters, where N is the side length of an image to be reconstructed, which
represents orders of magnitude improvements relative to the state-of-the-art
deep-learning-based reconstruction methods that map raw data to tomographic
images directly. Also, validated on a cone-beam breast CT dataset prepared by
Koning Corporation on a commercial scanner, our method demonstrates a
competitive performance over the state-of-the-art reconstruction networks in
terms of image quality. The source code of this paper is available at:
https://github.com/HuidongXie/DEER