Search Results for author:
Found 20 papers, 7 papers with code
AutoSamp: Autoencoding MRI Sampling via Variational Information Maximization
Experiments on public MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling.
Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-stack Attention Neural Network
We propose a deep-learning framework, trained by radiologists' supervision, for automating FOV prescription.
Artifact- and content-specific quality assessment for MRI with image rulers
We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers.
Greedy Learning for Large-Scale Neural MRI Reconstruction
Model-based deep learning approaches have recently shown state-of-the-art performance for accelerated MRI reconstruction.
Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple Contrasts
To address this issue, we propose multi-task learning (MTL) schemes that can jointly reconstruct multiple datasets.
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric
Self-supervised learning (SSL) has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data for downstream tasks.
Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform
The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities.
Risk Quantification in Deep MRI Reconstruction
Reliable medical image recovery is crucial for accurate patient diagnoses, but little prior work has centered on quantifying uncertainty when using non-transparent deep learning approaches to reconstruct high-quality images from limited measured data.
Learning to Sample MRI via Variational Information Maximization
Accelerating MRI scans requires optimal sampling of k-space data.
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications.
Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization
In this manuscript, we present a generalization of several existing iterative model based algorithms.
Fast Variable Density Poisson-Disc Sample Generation with Directional Variation
We present a fast method for generating random samples according to a variable density Poisson-disc distribution.
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
Many real-world signal sources are complex-valued, having real and imaginary components.
Utilizing the Wavelet Transform's Structure in Compressed Sensing
In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result.
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts.
Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality.
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Data-driven learning provides a solution to address these challenges.
Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering.
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.
