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 …
Deep convolutional framelets: A general deep learning framework for inverse problems
Recently, deep learning approaches with various network architectures have achieved
significant performance improvement over existing iterative reconstruction methods in …
significant performance improvement over existing iterative reconstruction methods in …
Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
Industry 4.0 promotes the transformation of manufacturing industry to intelligence, which
demands advances in materials, devices, and systems of the construction industry …
demands advances in materials, devices, and systems of the construction industry …
Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions
Computed tomography (CT) is widely used in medical diagnosis and non-destructive
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
[图书][B] Machine learning for tomographic imaging
The area of machine learning, especially deep learning, has exploded in recent years,
producing advances in everything from speech recognition and gaming to drug discovery …
producing advances in everything from speech recognition and gaming to drug discovery …
System and method for multi-architecture computed tomography pipeline
A system and method for reconstructing an image of a subject acquired using a tomographic
imaging system includes at least one computer processor configured to form an image …
imaging system includes at least one computer processor configured to form an image …
End-to-end deep learning for interior tomography with low-dose x-ray CT
Objective. There are several x-ray computed tomography (CT) scanning strategies used to
reduce radiation dose, such as (1) sparse-view CT,(2) low-dose CT and (3) region-of …
reduce radiation dose, such as (1) sparse-view CT,(2) low-dose CT and (3) region-of …
One network to solve all ROIs: Deep learning CT for any ROI using differentiated backprojection
Purpose Computed tomography for the reconstruction of region of interest (ROI) has
advantages in reducing the x‐ray dose and the use of a small detector. However, standard …
advantages in reducing the x‐ray dose and the use of a small detector. However, standard …
Real-time photoacoustic projection imaging using deep learning
Photoacoustic tomography (PAT) is an emerging and non-invasive hybrid imaging modality
for visualizing light absorbing structures in biological tissue. The recently invented PAT …
for visualizing light absorbing structures in biological tissue. The recently invented PAT …
Deep learning-based solvability of underdetermined inverse problems in medical imaging
Recently, with the significant developments in deep learning techniques, solving
underdetermined inverse problems has become one of the major concerns in the medical …
underdetermined inverse problems has become one of the major concerns in the medical …