59 citations · 215 across the 16 of their papers we have counts for
18 papers
Coil Sketching for computationally-efficient MR iterative reconstruction
Julio A. Oscanoa, Frank Ong, Siddharth S. Iyer +6
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially…
Polynomial Preconditioners for Regularized Linear Inverse Problems
Siddharth Srinivasan Iyer, Frank Ong, Xiaozhi Cao +4
This work aims to accelerate the convergence of proximal gradient methods used to solve regularized linear inverse problems. This is achieved by designing a polynomial-based precon…
SLfRank: Shinnar-Le-Roux Pulse Design with Reduced Energy and Accurate Phase Profiles using Rank Factorization
Frank Ong, Zheng Zhong, Congyu Liao +3
The Shinnar-Le-Roux (SLR) algorithm is widely used to design frequency selective pulses with large flip angles. We improve its design process to generate pulses with lower energy (…
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate
Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong +1
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand b…
Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images
Edgar A. Rios Piedra, Morteza Mardani, Frank Ong +3
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice. DCE and similar datasets of dynamic medi…
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Elizabeth K. Cole, John M. Pauly, Shreyas S. Vasanawala +1
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground…