no code implementations • 10 May 2023 • Julio A. Oscanoa, Frank Ong, Siddharth S. Iyer, Zhitao Li, Christopher M. Sandino, Batu Ozturkler, Daniel B. Ennis, Mert Pilanci, Shreyas S. Vasanawala
Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non-Cartesian datasets.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Christopher Michael Sandino, Frank Ong, Siddharth Srinivasan Iyer, Adam Bush, Shreyas Vasanawala
Model-based deep learning approaches, such as unrolled neural networks, have been shown to be effective tools for efficiently solving inverse problems.
1 code implementation • 25 Oct 2020 • Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong, Gordon Wetzstein
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems.
no code implementations • 30 Sep 2020 • Edgar A. Rios Piedra, Morteza Mardani, Frank Ong, Ukash Nakarmi, Joseph Y. Cheng, Shreyas Vasanawala
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice.
1 code implementation • 29 Aug 2020 • Elizabeth K. Cole, John M. Pauly, Shreyas S. Vasanawala, Frank Ong
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications.
no code implementations • 24 Oct 2019 • Mario O. Malavé, Corey A. Baron, Srivathsan P. Koundinyan, Christopher M. Sandino, Frank Ong, Joseph Y. Cheng, Dwight G. Nishimura
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).
1 code implementation • 30 Sep 2019 • Frank Ong, Xucheng Zhu, Joseph Y. Cheng, Kevin M. Johnson, Peder E. Z. Larson, Shreyas S. Vasanawala, Michael Lustig
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
1 code implementation • 25 Feb 2019 • Frank Ong, Martin Uecker, Michael Lustig
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
1 code implementation • 14 Nov 2018 • Siddharth Iyer, Frank Ong, Kawin Setsompop, Mariya Doneva, Michael Lustig
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
no code implementations • 9 Mar 2018 • Frank Ong, Peyman Milanfar, Pascal Getreuer
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.
no code implementations • 29 Nov 2017 • Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, Peyman Milanfar
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.
1 code implementation • 15 Sep 2017 • Frank Ong, Joseph Cheng, Michael Lustig
Purpose: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging.
1 code implementation • 29 Jun 2017 • H. Christian M. Holme, Sebastian Rosenzweig, Frank Ong, Robin N. Wilke, Michael Lustig, Martin Uecker
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
no code implementations • 19 Sep 2015 • Frank Ong, Sameer Pawar, Kannan Ramchandran
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
2 code implementations • 31 Jul 2015 • Frank Ong, Michael Lustig
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