Advances in medical image analysis with vision transformers: a comprehensive review
The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …
has recently also triggered broad interest in Computer Vision. Among other merits …
Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
NeRP: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction
Image reconstruction is an inverse problem that solves for a computational image based on
sampled sensor measurement. Sparsely sampled image reconstruction poses additional …
sampled sensor measurement. Sparsely sampled image reconstruction poses additional …
Deep sinogram completion with image prior for metal artifact reduction in CT images
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and
therapy planning and guidance. In reality, CT images may be affected adversely in the …
therapy planning and guidance. In reality, CT images may be affected adversely in the …
ADN: artifact disentanglement network for unsupervised metal artifact reduction
Current deep neural network based approaches to computed tomography (CT) metal artifact
reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training …
reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training …
CLEAR: comprehensive learning enabled adversarial reconstruction for subtle structure enhanced low-dose CT imaging
X-ray computed tomography (CT) is of great clinical significance in medical practice
because it can provide anatomical information about the human body without invasion …
because it can provide anatomical information about the human body without invasion …
DuDoRNet: learning a dual-domain recurrent network for fast MRI reconstruction with deep T1 prior
MRI with multiple protocols is commonly used for diagnosis, but it suffers from a long
acquisition time, which yields the image quality vulnerable to say motion artifacts. To …
acquisition time, which yields the image quality vulnerable to say motion artifacts. To …
Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques
M Selles, JAC van Osch, M Maas, MF Boomsma… - European Journal of …, 2024 - Elsevier
Metal artifacts degrade CT image quality, hampering clinical assessment. Numerous metal
artifact reduction methods are available to improve the image quality of CT images with …
artifact reduction methods are available to improve the image quality of CT images with …
DIOR: Deep iterative optimization-based residual-learning for limited-angle CT reconstruction
Limited-angle CT is a challenging problem in real applications. Incomplete projection data
will lead to severe artifacts and distortions in reconstruction images. To tackle this problem …
will lead to severe artifacts and distortions in reconstruction images. To tackle this problem …