A review on deep learning in medical image reconstruction
Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most fundamental and …
treatment of diseases. Medical image reconstruction is one of the most fundamental and …
MetaInv-Net: Meta inversion network for sparse view CT image reconstruction
X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis
and image-guided interventions. In this paper, we propose a new deep learning based …
and image-guided interventions. In this paper, we propose a new deep learning based …
An efficient iterative thresholding method for image segmentation
We proposed an efficient iterative thresholding method for multi-phase image segmentation.
The algorithm is based on minimizing piecewise constant Mumford–Shah functional in …
The algorithm is based on minimizing piecewise constant Mumford–Shah functional in …
Image restoration: Wavelet frame shrinkage, nonlinear evolution pdes, and beyond
In the past few decades, mathematics based approaches have been widely adopted in
various image restoration problems; the partial differential equation (PDE) based approach …
various image restoration problems; the partial differential equation (PDE) based approach …
Generative modeling in sinogram domain for sparse-view CT reconstruction
B Guan, C Yang, L Zhang, S Niu… - … on Radiation and …, 2023 - ieeexplore.ieee.org
The radiation dose in computed tomography (CT) examinations is harmful for patients but
can be significantly reduced by intuitively decreasing the number of projection views …
can be significantly reduced by intuitively decreasing the number of projection views …
Wavelet-inspired multi-channel score-based model for limited-angle CT reconstruction
J Zhang, H Mao, X Wang, Y Guo… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Score-based generative model (SGM) has demonstrated great potential in the challenging
limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of …
limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of …
Image-domain material decomposition for spectral CT using a generalized dictionary learning
The spectral computed tomography (CT) has huge advantages by providing accurate
material information. Unfortunately, due to the instability or overdetermination of the material …
material information. Unfortunately, due to the instability or overdetermination of the material …
Image restoration: a wavelet frame based model for piecewise smooth functions and beyond
In this paper, we propose a new wavelet frame based image restoration model that explicitly
treats images as piecewise smooth functions. It estimates both the image to be restored and …
treats images as piecewise smooth functions. It estimates both the image to be restored and …
SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction
Low-dose CT plays a significant role in reducing radiation risks to patients. The main
challenge is to achieve better image quality while lowering the imaging dose. In this work …
challenge is to achieve better image quality while lowering the imaging dose. In this work …
Learned full-sampling reconstruction from incomplete data
W Cheng, Y Wang, H Li, Y Duan - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Sparse-view and limited-angle Computed Tomography (CT) are very challenging problems
in real applications. Due to the high ill-posedness, both analytical and iterative …
in real applications. Due to the high ill-posedness, both analytical and iterative …