Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916126656561152 |
|---|---|
| author | Lantz, Megan Sidky, Emil Y. Reiser, Ingrid S. Pan, Xiaochuan Ongie, Gregory |
| author_facet | Lantz, Megan Sidky, Emil Y. Reiser, Ingrid S. Pan, Xiaochuan Ongie, Gregory |
| contents | Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images. However, networks trained with such pixel-wise losses are prone to wipe out small, low-contrast features that are critical for screening and diagnosis. To remedy this issue, we introduce a novel training loss inspired by the model observer framework to enhance the detectability of weak signals in the reconstructions. We evaluate our approach on the reconstruction of synthetic sparse-view breast CT data, and demonstrate an improvement in signal detectability with the proposed loss. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10010 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function Lantz, Megan Sidky, Emil Y. Reiser, Ingrid S. Pan, Xiaochuan Ongie, Gregory Medical Physics Computer Vision and Pattern Recognition Image and Video Processing Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images. However, networks trained with such pixel-wise losses are prone to wipe out small, low-contrast features that are critical for screening and diagnosis. To remedy this issue, we introduce a novel training loss inspired by the model observer framework to enhance the detectability of weak signals in the reconstructions. We evaluate our approach on the reconstruction of synthetic sparse-view breast CT data, and demonstrate an improvement in signal detectability with the proposed loss. |
| title | Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function |
| topic | Medical Physics Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2402.10010 |