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CT image denoising and deblurring with deep learning: current status and perspectives
This article reviews the deep learning methods for computed tomography image denoising
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
Dolce: A model-based probabilistic diffusion framework for limited-angle ct reconstruction
Abstract Limited-Angle Computed Tomography (LACT) is a non-destructive 3D imaging
technique used in a variety of applications ranging from security to medicine. The limited …
technique used in a variety of applications ranging from security to medicine. The limited …
Unsupervised polychromatic neural representation for ct metal artifact reduction
Emerging neural reconstruction techniques based on tomography (eg, NeRF, NeAT, and
NeRP) have started showing unique capabilities in medical imaging. In this work, we …
NeRP) have started showing unique capabilities in medical imaging. In this work, we …
Physics-Inspired Generative Models in Medical Imaging: A Review
D Hein, A Bozorgpour, D Merhof, G Wang - ar** using implicit neural representation
Quantitative susceptibility map** (QSM) is an MRI-based technique that estimates the
underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based …
underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based …
Tiavox: Time-aware attenuation voxels for sparse-view 4d dsa reconstruction
Four-dimensional Digital Subtraction Angiography (4D DSA) plays a critical role in the
diagnosis of many medical diseases, such as Arteriovenous Malformations (AVM) and …
diagnosis of many medical diseases, such as Arteriovenous Malformations (AVM) and …
Highly accelerated MRI via implicit neural representation guided posterior sampling of diffusion models
J Chu, C Du, X Lin, X Zhang, L Wang, Y Zhang… - Medical Image …, 2025 - Elsevier
Reconstructing high-fidelity magnetic resonance (MR) images from under-sampled k-space
is a commonly used strategy to reduce scan time. The posterior sampling of diffusion models …
is a commonly used strategy to reduce scan time. The posterior sampling of diffusion models …