Name
Abstract | ||
|---|---|---|
We propose a new convolutional neural network architecture for image reconstruction in sparse view computed tomography. The proposed network consists of a cascade of U-nets and data consistency layers. While the U-nets address the undersampling artifacts, the data consistency layers model the specific scanner geometry and make direct use of measured data. We train the network cascade end-to-end on sparse view cardiac CT images. The proposed network's performance is evaluated according to different quantitative measures and compared to the one of a cascade with fully convolutional neural networks with residual connections and to the one of a single U-net with approximately the same number of trainable parameters. While in both experiments the methods show similar performance in terms of quantitative measures, our proposed U-nets cascade yields superior visual results and better preserves the overall image structure as well as fine diagnostic details, e.g. the coronary arteries. The latter is also confirmed by a statistically significant increase of the Haar-wavelet-based perceptual similarity index measure in all the experiments. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00129-2_11 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Deep learning,Convolutional neural networks,Data consistency,Computed tomography,Sparse sampling | Conference | 11074 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Kofler | 1 | 0 | 1.69 |
| Markus Haltmeier | 2 | 74 | 14.16 |
| Christoph Kolbitsch | 3 | 0 | 0.34 |
| Marc Kachelrieß | 4 | 0 | 0.34 |
| Marc Dewey | 5 | 0 | 0.34 |