Deep learning for tomographic image reconstruction
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Purpose To advance research in the field of machine learning for MR image reconstruction
with an open challenge. Methods We provided participants with a dataset of raw k‐space …
with an open challenge. Methods We provided participants with a dataset of raw k‐space …
DRONE: Dual-domain residual-based optimization network for sparse-view CT reconstruction
Deep learning has attracted rapidly increasing attention in the field of tomographic image
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
Deep generative adversarial neural networks for compressive sensing MRI
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed
linear inverse task. The time and resource intensive computations require tradeoffs between …
linear inverse task. The time and resource intensive computations require tradeoffs between …
FISTA-Net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging
Inverse problems are essential to imaging applications. In this letter, we propose a model-
based deep learning network, named FISTA-Net, by combining the merits of interpretability …
based deep learning network, named FISTA-Net, by combining the merits of interpretability …
Framing U-Net via deep convolutional framelets: Application to sparse-view CT
X-ray computed tomography (CT) using sparse projection views is a recent approach to
reduce the radiation dose. However, due to the insufficient projection views, an analytic …
reduce the radiation dose. However, due to the insufficient projection views, an analytic …
Image reconstruction is a new frontier of machine learning
Over past several years, machine learning, or more generally artificial intelligence, has
generated overwhelming research interest and attracted unprecedented public attention. As …
generated overwhelming research interest and attracted unprecedented public attention. As …
Convolutional neural network based metal artifact reduction in X-ray computed tomography
In the presence of metal implants, metal artifacts are introduced to x-ray computed
tomography CT images. Although a large number of metal artifact reduction (MAR) methods …
tomography CT images. Although a large number of metal artifact reduction (MAR) methods …
Multi-channel optimization generative model for stable ultra-sparse-view CT reconstruction
Score-based generative model (SGM) has risen to prominence in sparse-view CT
reconstruction due to its impressive generation capability. The consistency of data is crucial …
reconstruction due to its impressive generation capability. The consistency of data is crucial …
Image reconstruction: From sparsity to data-adaptive methods and machine learning
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …