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Found 7 papers, 3 papers with code
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation
In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing.
OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences
Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time.
Task-GAN for Improved GAN based Image Restoration
Deep Learning (DL) algorithms based on Generative Adversarial Network (GAN) have demonstrated great potentials in computer vision tasks such as image restoration.
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
The QSMnet maps of the test dataset were compared with those from TKD and MEDI for image quality and consistency in multiple head orientations.
Image and Video Processing
200x Low-dose PET Reconstruction using Deep Learning
Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose.
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance.
