Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

To reduce the radiation dose, sparse-view computed tomography (CT) reconstruction has
been proposed, aiming to recover high-quality CT images from sparsely sampled sinogram …

Computed tomography (CT) technology is widely used in clinical medical diagnosis thanks
to the excellent visualization of the CT imaging technology for the internal cross-sectional …

Computed tomography (CT) provides non-invasive anatomical structures of the human body
and is also widely used for clinical diagnosis, but excessive ionizing radiation in X-rays can …

Sparse-view CT is an efficient way for low dose scanning but degrades image quality.
Inspired by the successful use of non-local attention in natural image denoising and …

Inverse problems span across diverse fields. In medical contexts computed tomography (CT)
plays a crucial role in reconstructing a patient's internal structure presenting challenges due …

In computed tomography (CT), although sparse sampling of projections effectively mitigates
radiation problems, the quality of CT images is severely compromised. Recovering high …

The computed tomography (CT) technique is extensively employed as an imaging modality
in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising …

We propose a novel Learned Alternating Minimization Algorithm (LAMA) for dual-domain
sparse-view CT image reconstruction. LAMA is naturally induced by a variational model for …

Sparse view computed tomography (SVCT) aims to reduce the number of X-ray projection
views required for reconstructing the cross-sectional image of an object. While SVCT …