A review of deep learning ct reconstruction from incomplete projection data
Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …
industrial applications. However, accurate CT reconstruction requires complete projection …
Wavelet-improved score-based generative model for medical imaging
The score-based generative model (SGM) has demonstrated remarkable performance in
addressing challenging under-determined inverse problems in medical imaging. However …
addressing challenging under-determined inverse problems in medical imaging. However …
Deep embedding-attention-refinement for sparse-view CT reconstruction
Tomographic image reconstruction with deep learning is an emerging field of applied
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …
CLEAR: comprehensive learning enabled adversarial reconstruction for subtle structure enhanced low-dose CT imaging
X-ray computed tomography (CT) is of great clinical significance in medical practice
because it can provide anatomical information about the human body without invasion …
because it can provide anatomical information about the human body without invasion …
Convex optimization algorithms in medical image reconstruction—in the age of AI
The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …
algorithms, which are often applications or adaptations of convex optimization algorithms …
Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction
Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak
artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin …
artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin …
Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided …
Tumor grading and interpretability of laryngeal cancer is a key yet challenging task in the
clinical diagnosis, mainly because of the commonly used low-magnification pathological …
clinical diagnosis, mainly because of the commonly used low-magnification pathological …
Iterative residual optimization network for limited-angle tomographic reconstruction
Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems,
leading to edge divergence with degraded image quality. Recently, deep learning has been …
leading to edge divergence with degraded image quality. Recently, deep learning has been …
Low-dose CT image synthesis for domain adaptation imaging using a generative adversarial network with noise encoding transfer learning
M Li, J Wang, Y Chen, Y Tang, Z Wu… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Deep learning (DL) based image processing methods have been successfully applied to
low-dose x-ray images based on the assumption that the feature distribution of the training …
low-dose x-ray images based on the assumption that the feature distribution of the training …
Deep learning based spectral CT imaging
Spectral computed tomography (CT) has attracted much attention in radiation dose
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …