Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT

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X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, themain goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U-Net variants such as dual frame and tight frame U-Nets satisfy the so-called frame condition which makes them better for effective recovery of high frequency edges in sparse-view CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-06
Language
English
Article Type
Article
Keywords

LOW-DOSE CT; HANKEL MATRIX ALOHA; NEURAL-NETWORK; IMAGE-RECONSTRUCTION; PENALTY

Citation

IEEE TRANSACTIONS ON MEDICAL IMAGING, v.37, no.6, pp.1418 - 1429

ISSN
0278-0062
DOI
10.1109/TMI.2018.2823768
URI
http://hdl.handle.net/10203/243705
Appears in Collection
BiS-Journal Papers(저널논문)
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