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Found 4 papers, 1 papers with code
Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT
Specifically, in this study we transform the cutting-edge Denoising Diffusion Probabilistic Model (DDPM) into a parallel framework for sub-volume-based sparse-view breast CT image reconstruction in projection and image domains.
Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences.
Deep-learning-based Breast CT for Radiation Dose Reduction
In this study, we propose a deep-learning-based method to establish a residual neural network model for the image reconstruction, which is applied for few-view breast CT to produce high quality breast CT images.
Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging
Few-view CT image reconstruction is an important topic to reduce the radiation dose.
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