Iterative reconstruction
View article
Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss Open
The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifac…
View article
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction Open
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to …
View article
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction Open
Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce …
View article
aLow-dose CT via convolutional neural network Open
In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction m…
View article
On instabilities of deep learning in image reconstruction and the potential costs of AI Open
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we demonstrate a crucial phenomenon: Deep learning …
View article
State of the Art: Iterative CT Reconstruction Techniques Open
Owing to recent advances in computing power, iterative reconstruction (IR) algorithms have become a clinically viable option in computed tomographic (CT) imaging. Substantial evidence is accumulating about the advantages of IR algorithms o…
View article
Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss Open
Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still …
View article
XD‐GRASP: Golden‐angle radial MRI with reconstruction of extra motion‐state dimensions using compressed sensing Open
Purpose To develop a novel framework for free‐breathing MRI called XD‐GRASP, which sorts dynamic data into extra motion‐state dimensions using the self‐navigation properties of radial imaging and reconstructs the multidimensional dataset u…
View article
Medical Image Synthesis with Deep Convolutional Adversarial Networks Open
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can …
View article
Intrinsic dependencies of <span>CT</span> radiomic features on voxel size and number of gray levels Open
Purpose Many radiomics features were originally developed for non‐medical imaging applications and therefore original assumptions may need to be reexamined. In this study, we investigated the impact of slice thickness and pixel spacing (or…
View article
Deep Generative Adversarial Neural Networks for Compressive Sensing MRI Open
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed s…
View article
Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal Open
Photoacoustic imaging is an emerging imaging modality that is based upon the photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure waves are measured by an array of detectors and used to reconstruct an imag…
View article
A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications Open
Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on…
View article
A Perspective on Deep Imaging Open
The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction. The latter aspect is considered in this perspective article with an emphas…
View article
A Survey of Surface Reconstruction from Point Clouds Open
The area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where…
View article
Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography Open
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 have been proposed in the past decades, MAR is still one of the maj…
View article
Noise2Noise: Learning image restoration without clean data Open
We apply basic statistical reasoning to signal reconstruction by machine learning - learning to map corrupted observations to clean signals - with a simple and powerful conclusion: It is possible to learn to restore images by only looking …
View article
<span>KIKI</span>‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images Open
Purpose To demonstrate accurate MR image reconstruction from undersampled k‐space data using cross‐domain convolutional neural networks (CNNs) Methods Cross‐domain CNNs consist of 3 components: (1) a deep CNN operating on the k‐space (KCNN…
View article
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Open
3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of m…
View article
LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT Open
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomogra…
View article
Rank Minimization for Snapshot Compressive Imaging Open
Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. T…
View article
DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering Open
Nonlinear electromagnetic (EM) inverse scattering is a quantitative and\nsuper-resolution imaging technique, in which more realistic interactions\nbetween the internal structure of scene and EM wavefield are taken into account\nin the imag…
View article
Further improvements to the ptychographical iterative engine Open
Ptychography is a form of phase imaging that uses iterative algorithms to reconstruct an image of a specimen from a series of diffraction patterns.It is swiftly developing into a mainstream technique, with a growing list of applications ac…
View article
Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era Open
3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. Since 2015, image-based 3D reconstruction using convolutional neural ne…
View article
Events-To-Video: Bringing Modern Computer Vision to Event Cameras Open
Event cameras are novel sensors that report brightness changes in the form of asynchronous “events” instead of intensity frames. They have significant advantages over conventional cameras: high temporal resolution, high dynamic range, and …
View article
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks Open
Image reconstruction from undersampled k-space data has been playing an important role in fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and has also shown potential in significantly accelerating MR…
View article
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues Open
Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deep…
View article
Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography Open
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically desig…
View article
Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks Open
The mathematical problem for electrical impedance tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rig…
View article
TIGRE: a MATLAB-GPU toolbox for CBCT image reconstruction Open
In this article the Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox, a MATLAB/CUDA toolbox for fast and accurate 3D x-ray image reconstruction, is presented. One of the key features is the implementation of a wide variety of…