Artificial intelligence in image reconstruction: the change is here
Innovations in CT have been impressive among imaging and medical technologies in both
the hardware and software domain. The range and speed of CT scanning improved from the …
the hardware and software domain. The range and speed of CT scanning improved from the …
A review of deep learning ct reconstruction from incomplete projection data
Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …
industrial applications. However, accurate CT reconstruction requires complete projection …
Similarity-informed self-learning and its application on seismic image denoising
Seismic image denoising is essential to enhance the signal-to-noise ratio (SNR) of seismic
images and facilitate seismic processing and geological structure interpretation. With the …
images and facilitate seismic processing and geological structure interpretation. With the …
Deep learning based spectral CT imaging
Spectral computed tomography (CT) has attracted much attention in radiation dose
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …
IDOL-Net: An interactive dual-domain parallel network for CT metal artifact reduction
Due to the presence of metallic implants, the imaging quality of computed tomography (CT)
would be heavily degraded. With the rapid development of deep learning, several neural …
would be heavily degraded. With the rapid development of deep learning, several neural …
[HTML][HTML] Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction
Background Multi-energy computed tomography (CT) provides multiple channel-wise
reconstructed images, and they can be used for material identification and k-edge imaging …
reconstructed images, and they can be used for material identification and k-edge imaging …
Convolutional neural network-based diabetes diagnostic system via iridology technique
MN Önal, GE Güraksin, R Duman - Multimedia tools and Applications, 2023 - Springer
Iridology is a sort of complementary medicine using the patterns, colors, and other properties
of the iris to gather systemic information about a person's health status. To put it another …
of the iris to gather systemic information about a person's health status. To put it another …
Detector shifting and deep learning based ring artifact correction method for low‐dose CT
Y Liu, C Wei, Q Xu - Medical Physics, 2023 - Wiley Online Library
Background In x‐ray computed tomography (CT), the gain inconsistency of detector units
leads to ring artifacts in the reconstructed images, seriously destroys the image structure …
leads to ring artifacts in the reconstructed images, seriously destroys the image structure …
Dictionary learning based image-domain material decomposition for spectral CT
The potential huge advantage of spectral computed tomography (CT) is that it can provide
accurate material identification and quantitative tissue information by material …
accurate material identification and quantitative tissue information by material …
Ring artifacts correction for computed tomography image using unsupervised contrastive learning
Objective. Computed tomography (CT) is a widely employed imaging technology for disease
detection. However, CT images often suffer from ring artifacts, which may result from …
detection. However, CT images often suffer from ring artifacts, which may result from …