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 …
AI-based reconstruction for fast MRI—A systematic review and meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
A robust volumetric transformer for accurate 3D tumor segmentation
We propose a Transformer architecture for volumetric segmentation, a challenging task that
requires keeping a complex balance in encoding local and global spatial cues, and …
requires keeping a complex balance in encoding local and global spatial cues, and …
Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning
Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled
data is important in many clinical applications. In recent years, deep learning-based …
data is important in many clinical applications. In recent years, deep learning-based …
CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions
Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of
cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular …
cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular …
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data
B Yaman, SAH Hosseini, S Moeller… - Magnetic resonance …, 2020 - Wiley Online Library
Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues
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 …
based machine-learning techniques have received interest as a means of accelerating …
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 …
MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI
Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that
can accurately reconstruct images from undersampled k-space data with a much lower …
can accurately reconstruct images from undersampled k-space data with a much lower …
A review on deep learning MRI reconstruction without fully sampled k-space
Background Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method
in clinical medicine, but it has always suffered from the problem of long acquisition time …
in clinical medicine, but it has always suffered from the problem of long acquisition time …