DuoLift-GAN:Reconstructing CT from Single-view and Biplanar X-Rays with Generative Adversarial Networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913607101448192 |
|---|---|
| author | Zhang, Zhaoxi Ying, Yueliang |
| author_facet | Zhang, Zhaoxi Ying, Yueliang |
| contents | Computed tomography (CT) provides highly detailed three-dimensional (3D) medical images but is costly, time-consuming, and often inaccessible in intraoperative settings (Organization et al. 2011). Recent advancements have explored reconstructing 3D chest volumes from sparse 2D X-rays, such as single-view or orthogonal double-view images. However, current models tend to process 2D images in a planar manner, prioritizing visual realism over structural accuracy. In this work, we introduce DuoLift Generative Adversarial Networks (DuoLift-GAN), a novel architecture with dual branches that independently elevate 2D images and their features into 3D representations. These 3D outputs are merged into a unified 3D feature map and decoded into a complete 3D chest volume, enabling richer 3D information capture. We also present a masked loss function that directs reconstruction towards critical anatomical regions, improving structural accuracy and visual quality. This paper demonstrates that DuoLift-GAN significantly enhances reconstruction accuracy while achieving superior visual realism compared to existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07941 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DuoLift-GAN:Reconstructing CT from Single-view and Biplanar X-Rays with Generative Adversarial Networks Zhang, Zhaoxi Ying, Yueliang Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Computed tomography (CT) provides highly detailed three-dimensional (3D) medical images but is costly, time-consuming, and often inaccessible in intraoperative settings (Organization et al. 2011). Recent advancements have explored reconstructing 3D chest volumes from sparse 2D X-rays, such as single-view or orthogonal double-view images. However, current models tend to process 2D images in a planar manner, prioritizing visual realism over structural accuracy. In this work, we introduce DuoLift Generative Adversarial Networks (DuoLift-GAN), a novel architecture with dual branches that independently elevate 2D images and their features into 3D representations. These 3D outputs are merged into a unified 3D feature map and decoded into a complete 3D chest volume, enabling richer 3D information capture. We also present a masked loss function that directs reconstruction towards critical anatomical regions, improving structural accuracy and visual quality. This paper demonstrates that DuoLift-GAN significantly enhances reconstruction accuracy while achieving superior visual realism compared to existing methods. |
| title | DuoLift-GAN:Reconstructing CT from Single-view and Biplanar X-Rays with Generative Adversarial Networks |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.07941 |