Search Results for author: Jonathan I. Tamir

Found 15 papers, 8 papers with code

Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models

1 code implementation5 Jun 2023 Sriram Ravula, Brett Levac, Ajil Jalal, Jonathan I. Tamir, Alexandros G. Dimakis

Diffusion-based generative models have been used as powerful priors for magnetic resonance imaging (MRI) reconstruction.

MRI Reconstruction

Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data

no code implementations2 May 2023 Asad Aali, Marius Arvinte, Sidharth Kumar, Jonathan I. Tamir

We present SURE-Score: an approach for learning score-based generative models using training samples corrupted by additive Gaussian noise.


MRI Reconstruction with Side Information using Diffusion Models

no code implementations26 Mar 2023 Brett Levac, Ajil Jalal, Kannan Ramchandran, Jonathan I. Tamir

This leads to an improvement in image reconstruction fidelity over generative models that rely only on a marginal prior over the image contrast of interest.

Anatomy MRI Reconstruction

High-fidelity Direct Contrast Synthesis from Magnetic Resonance Fingerprinting

no code implementations21 Dec 2022 Ke Wang, Mariya Doneva, Jakob Meineke, Thomas Amthor, Ekin Karasan, Fei Tan, Jonathan I. Tamir, Stella X. Yu, Michael Lustig

Here we propose a supervised learning-based method that directly synthesizes contrast-weighted images from the MRF data without going through the quantitative mapping and spin-dynamics simulation.

Generative Adversarial Network Magnetic Resonance Fingerprinting +1

Accelerated Motion Correction with Deep Generative Diffusion Models

1 code implementation1 Nov 2022 Brett Levac, Sidharth Kumar, Ajil Jalal, Jonathan I. Tamir

In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using diffusion based generative models.

Image Reconstruction

FSE Compensated Motion Correction for MRI Using Data Driven Methods

no code implementations1 Jul 2022 Brett Levac, Sidharth Kumar, Sofia Kardonik, Jonathan I. Tamir

Magnetic Resonance Imaging (MRI) is a widely used medical imaging modality boasting great soft tissue contrast without ionizing radiation, but unfortunately suffers from long acquisition times.

Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning

1 code implementation21 Jun 2022 Ali Lotfi Rezaabad, Sidharth Kumar, Sriram Vishwanath, Jonathan I. Tamir

Pretraining on a large source data set and fine-tuning on the target samples is prone to overfitting in the few-shot regime, where only a small number of target samples are available.

Contrastive Learning Domain Adaptation +3

An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data

no code implementations3 May 2022 Kalina P. Slavkova, Julie C. DiCarlo, Viraj Wadhwa, Chengyue Wu, John Virostko, Sidharth Kumar, Thomas E. Yankeelov, Jonathan I. Tamir

We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.


Wideband and Entropy-Aware Deep Soft Bit Quantization

1 code implementation18 Oct 2021 Marius Arvinte, Jonathan I. Tamir

Deep learning has been recently applied to physical layer processing in digital communication systems in order to improve end-to-end performance.


Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results

2 code implementations16 Sep 2021 Efrat Shimron, Jonathan I. Tamir, Ke Wang, Michael Lustig

We demonstrate this phenomenon for inverse problem solvers and show how their biased performance stems from hidden data preprocessing pipelines.

Dictionary Learning MRI Reconstruction

Robust Compressed Sensing MRI with Deep Generative Priors

2 code implementations NeurIPS 2021 Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alexandros G. Dimakis, Jonathan I. Tamir

The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.

Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization

1 code implementation2 Mar 2021 Marius Arvinte, Sriram Vishwanath, Ahmed H. Tewfik, Jonathan I. Tamir

Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning.

MRI Reconstruction

Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging

no code implementations11 Sep 2018 Michael J. Anderson, Jonathan I. Tamir, Javier S. Turek, Marcus T. Alley, Theodore L. Willke, Shreyas S. Vasanawala, Michael Lustig

Our improvements to the pipeline on a single machine provide a 3x overall reconstruction speedup, which allowed us to add algorithmic changes improving image quality.

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