Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging
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_ | 1866912652497780736 |
|---|---|
| author | Li, Mengzhou Niu, Chuang Wang, Ge Amma, Maya R Chapagain, Krishna M Gabrielson, Stefan Li, Andrew Jonker, Kevin de Ruiter, Niels Clark, Jennifer A Butler, Phil Butler, Anthony Yu, Hengyong |
| author_facet | Li, Mengzhou Niu, Chuang Wang, Ge Amma, Maya R Chapagain, Krishna M Gabrielson, Stefan Li, Andrew Jonker, Kevin de Ruiter, Niels Clark, Jennifer A Butler, Phil Butler, Anthony Yu, Hengyong |
| contents | X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR volumetric PCCT reconstruction has been challenged by the large memory burden, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed validated in a New Zealand clinical trial. Specifically, we design a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and clinical data. Our results in a reader study of 8 patients from the clinical trial demonstrate a great potential to cut the radiation dose to half that of the clinical PCCT standard without compromising image quality and diagnostic value. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12331 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging Li, Mengzhou Niu, Chuang Wang, Ge Amma, Maya R Chapagain, Krishna M Gabrielson, Stefan Li, Andrew Jonker, Kevin de Ruiter, Niels Clark, Jennifer A Butler, Phil Butler, Anthony Yu, Hengyong Medical Physics Computer Vision and Pattern Recognition X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR volumetric PCCT reconstruction has been challenged by the large memory burden, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed validated in a New Zealand clinical trial. Specifically, we design a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and clinical data. Our results in a reader study of 8 patients from the clinical trial demonstrate a great potential to cut the radiation dose to half that of the clinical PCCT standard without compromising image quality and diagnostic value. |
| title | Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging |
| topic | Medical Physics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.12331 |