Super-Resolution and Sparse View CT Reconstruction 155 5.2 Super Resolution Experiments Now we run experiments to compare the new regularizer in a super-resolution setting. We chose the following algorithms for our comparison: – PSART-STP: this is our complete framework using the Structure Tensor ...
Background Sparse-view computed tomography (CT) reduces radiation exposure but suffers from severe artifacts caused by insufficient sampling and data scarcity, which compromise image fidelity. Recen...
We investigate an image recovery method for sparse-view computed tomography (CT) using an iterative shrinkage algorithm based on a second-order approach. The two-step iterative shrinkage-thresholding (TwIST) algorithm including a total variation regularization technique is elucidated to be more robust ...
Zang, G., Aly, M., Idoughi, R., Wonka, P., Heidrich, W. (2018). Super-Resolution and Sparse View CT Reconstruction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science(), vol 11220. ...
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT 4 Feb 2025 · Matt Y. Cheung, Sophia Zorek, Tucker J. Netherton, Laurence E. Court, Sadeer Al-Kindi, Ashok Veeraraghavan, Guha Balakrishnan · Edit social preview Diffusion models demonstrate state-of-the...
Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learn...
IntroductionLow-dose computed tomography (CT) image reconstruc-tion has been widely used in industrial CT and medical CT [1]. In the medical CT fi eld, sparse-view sampling has prac-tical implications in reducing ionizing radiation, which is harmful to people’s health, decreasing the scanning...
Linear measurement processes for sparse-view CT. Visualization of the intermediate reconstruction process of GMSD. As the level of artificial noise becomes smaller, the reconstruction results tend to ground-truth data. The proposed unsupervised deep learning in sinogram domain for sparse-view CT. ...
Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showing promising results. However, two fundamental problems with these deep...