Deep learning for PET image reconstruction
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for
the reconstruction of images in positron emission tomography (PET). Deep learning can be …
the reconstruction of images in positron emission tomography (PET). Deep learning can be …
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 …
Deep learning techniques for inverse problems in imaging
Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …
wide variety of inverse problems arising in computational imaging. We explore the central …
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 …
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 …
Deep embedding-attention-refinement for sparse-view CT reconstruction
Tomographic image reconstruction with deep learning is an emerging field of applied
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …
Neumann networks for linear inverse problems in imaging
Many challenging image processing tasks can be described by an ill-posed linear inverse
problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all …
problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all …
Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement
Image processing plays a crucial role in maximising diagnostic quality of positron emission
tomography (PET) images. Recently, deep learning methods developed across many fields …
tomography (PET) images. Recently, deep learning methods developed across many fields …
Momentum-Net: Fast and convergent iterative neural network for inverse problems
Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in
imaging, image processing, and computer vision. INNs combine regression NNs and an …
imaging, image processing, and computer vision. INNs combine regression NNs and an …
Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …
information. However, it has a fundamental challenge that is time consuming to acquire …