22 citations · 76 across the 16 of their papers we have counts for
8 papers · 1 filter
High-fidelity Direct Contrast Synthesis from Magnetic Resonance Fingerprinting
Ke Wang, Mariya Doneva, Jakob Meineke +6
Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI technique that can extract important tissue and system parameters such as T1, T2, B0, and B1 from a single…
Accelerated Motion Correction with Deep Generative Diffusion Models
Brett Levac, Sidharth Kumar, Ajil Jalal +1
Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to…
An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data
Kalina P. Slavkova, Julie C. DiCarlo, Viraj Wadhwa +5
The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerat…
Differentiable Rendering for Synthetic Aperture Radar Imagery
Michael Wilmanski, Jonathan Tamir
There is rising interest in differentiable rendering, which allows explicitly modeling geometric priors and constraints in optimization pipelines using first-order methods such as…
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss
Ke Wang, Jonathan I Tamir, Alfredo De Goyeneche +4
Purpose: To improve reconstruction fidelity of fine structures and textures in deep learning (DL) based reconstructions. Methods: A novel patch-based Unsupervised Feature Loss (UFL…
Memory-efficient Learning for High-Dimensional MRI Reconstruction
Ke Wang, Michael Kellman, Christopher M. Sandino +5
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can…
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…
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…