78 citations · 302 across the 22 of their papers we have counts for
5 papers · 1 filter
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop
Michael Kellman, Jon Tamir, Emrah Boston +2
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems. Recently, critica…
Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data
Jonathan I. Tamir, Stella X. Yu, Michael Lustig
Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints. Here we use a deep neural network prior instead of l1-regul…
Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions
Frank Ong, Xucheng Zhu, Joseph Y. Cheng +4
Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contr…
Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging
Jonathan I. Tamir, Frank Ong, Suma Anand +3
Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often t…
Accelerating Non-Cartesian MRI Reconstruction Convergence using k-space Preconditioning
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 d…