Sparse2Inverse: Self-supervised inversion of sparse-view CT data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929257731588096 |
|---|---|
| author | Gruber, Nadja Schwab, Johannes Gizewski, Elke Haltmeier, Markus |
| author_facet | Gruber, Nadja Schwab, Johannes Gizewski, Elke Haltmeier, Markus |
| contents | Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing. In sparse-view CT, only a few projection views are acquired, causing standard reconstructions to suffer from severe artifacts and noise. To address these issues, we propose a self-supervised image reconstruction strategy. Specifically, in contrast to the established Noise2Inverse, our proposed training strategy uses a loss function in the projection domain, thereby bypassing the otherwise prescribed nullspace component. We demonstrate the effectiveness of the proposed method in reducing stripe-artifacts and noise, even from highly sparse data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16921 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse2Inverse: Self-supervised inversion of sparse-view CT data Gruber, Nadja Schwab, Johannes Gizewski, Elke Haltmeier, Markus Image and Video Processing Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing. In sparse-view CT, only a few projection views are acquired, causing standard reconstructions to suffer from severe artifacts and noise. To address these issues, we propose a self-supervised image reconstruction strategy. Specifically, in contrast to the established Noise2Inverse, our proposed training strategy uses a loss function in the projection domain, thereby bypassing the otherwise prescribed nullspace component. We demonstrate the effectiveness of the proposed method in reducing stripe-artifacts and noise, even from highly sparse data. |
| title | Sparse2Inverse: Self-supervised inversion of sparse-view CT data |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2402.16921 |