Search Results for author:
Found 15 papers, 8 papers with code
Coil Sketching for computationally-efficient MR iterative reconstruction
Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non-Cartesian datasets.
Deep subspace learning for efficient reconstruction of spatiotemporal imaging data
Model-based deep learning approaches, such as unrolled neural networks, have been shown to be effective tools for efficiently solving inverse problems.
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems.
Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice.
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications.
Reconstruction of Undersampled 3D Non-Cartesian Image-Based Navigators for Coronary MRA Using an Unrolled Deep Learning Model
Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of $\textit{l}_{1}$-ESPIRiT (20x and 3x speed increases, respectively).
Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions
We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory.
Medical Physics Image and Video Processing
Accelerating Non-Cartesian MRI Reconstruction Convergence using k-space Preconditioning
We propose a k-space preconditioning formulation for accelerating the convergence of iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data.
Medical Physics
SURE-based Automatic Parameter Selection For ESPIRiT Calibration
The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams.
Medical Physics
Local Kernels that Approximate Bayesian Regularization and Proximal Operators
In this work, we broadly connect kernel-based filtering (e. g. approaches such as the bilateral filters and nonlocal means, but also many more) with general variational formulations of Bayesian regularized least squares, and the related concept of proximal operators.
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters.
General Phase Regularized Reconstruction using Phase Cycling
Purpose: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging.
ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging
Robustness against data inconsistencies, imaging artifacts and acquisition speed are crucial factors limiting the possible range of applications for magnetic resonance imaging (MRI).
Medical Physics
Fast and Efficient Sparse 2D Discrete Fourier Transform using Sparse-Graph Codes
For the case when the spatial-domain measurements are corrupted by additive noise, our 2D-FFAST framework extends to a noise-robust version in sub-linear time of O(k log4 N ) using O(k log3 N ) measurements.
Information Theory Multimedia Systems and Control Information Theory
Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition
We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales.
Systems and Control Information Theory Numerical Analysis Information Theory Optimization and Control
