22 citations · 76 across the 16 of their papers we have counts for
17 papers
Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm
Moritz Blumenthal, Tina Holliber, Jonathan I. Tamir +1
Purpose: The Unadjusted Langevin Algorithm (ULA) in combination with diffusion models can generate high quality MRI reconstructions with uncertainty estimation from highly undersam…
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…
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…
MIMO Channel Estimation using Score-Based Generative Models
Marius Arvinte, Jonathan I Tamir
Channel estimation is a critical task in multiple-input multiple-output (MIMO) digital communications that substantially effects end-to-end system performance. In this work, we int…
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…
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results
Efrat Shimron, Jonathan I. Tamir, Ke Wang +1
While open databases are an important resource in the Deep Learning (DL) era, they are sometimes used "off-label": data published for one task are used for training algorithms for…
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…
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction
Heng Yu, Zijing Dong, Yamin Arefeen +3
RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each specific scan. As it trains a separate model for each indivi…
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…
Wave-encoding and Shuffling Enables Rapid Time Resolved Structural Imaging
Siddharth Iyer, Daniel Polak, Congyu Liao +6
T2-Shuffling reconstructs multiple sharp T2-weighted images from a single volumetric fast spin-echo (3D-FSE) scan. Wave-CAIPI is a parallel imaging technique that achieves good rec…
Memory-efficient Learning for Large-scale Computational Imaging
Michael Kellman, Kevin Zhang, Jon Tamir +3
Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classic…
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…
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…
Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging
Michael J. Anderson, Jonathan I. Tamir, Javier S. Turek +4
Magnetic resonance imaging is capable of producing volumetric images without ionizing radiation. Nonetheless, long acquisitions lead to prohibitively long exams. Compressed sensing…