Abstract：
: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, the main 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.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Han Y,Ye JCdoi
10.1109/TMI.2018.2823768subject
Has Abstractpub_date
2018-06-01 00:00:00pages
1418-1429issue
6eissn
0278-0062issn
1558-254Xjournal_volume
37pub_type
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