Sparse-view computed tomography (CT)---using a small number of projections for
tomographic reconstruction---enables much lower radiation dose to patients and …

The recent Mamba model has shown remarkable adaptability for visual representation
learning, including in medical imaging tasks. This study introduces MambaMIR, a Mamba …

Existing deep learning (DL)-based magnetic resonance imaging (MRI) retrospective motion
correction (MoCo) models are typically task-specific, which makes them challenging to …

Deep learning has been extensively applied in medical image reconstruction, where
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the …

Sparse view (SV) computed tomography (CT) is a clinical diagnostic technique aimed at
reducing radiation dose to the human body from X-rays. However, SVCT reconstructed …

Purpose Numerous techniques based on deep learning have been utilized in sparse view
computed tomography (CT) imaging. Nevertheless, the majority of techniques are …

Abstract Background and Objective: Low-dose computed tomography (LDCT) scans
significantly reduce radiation exposure, but introduce higher levels of noise and artifacts that …

X-ray computed tomography (CT) imaging technology has become an indispensable
diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making …

Purpose Dual-energy computed tomography (DECT) enables the differentiation of different
materials. Additionally, DECT images consist of multiple scans of the same sample …

Sparse views X-ray computed tomography has emerged as a contemporary technique to
mitigate radiation dose. Because of the reduced number of projection views, traditional …