1 code implementation • 5 Aug 2023 • Frederic Wang, Han Qi, Alfredo De Goyeneche, Reinhard Heckel, Michael Lustig, Efrat Shimron
In each training iteration, rather than using the fully sampled k-space for computing gradients, we use only a small k-space portion.
no code implementations • 21 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
2 code implementations • 16 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.
1 code implementation • 27 Aug 2021 • Ke Wang, Jonathan I Tamir, Alfredo De Goyeneche, Uri Wollner, Rafi Brada, Stella Yu, Michael Lustig
By adding an additional loss function on the low-dimensional feature space during training, the reconstruction frameworks from under-sampled or corrupted data can reproduce more realistic images that are closer to the original with finer textures, sharper edges, and improved overall image quality.
no code implementations • 6 Mar 2021 • Ke Wang, Michael Kellman, Christopher M. Sandino, Kevin Zhang, Shreyas S. Vasanawala, Jonathan I. Tamir, Stella X. Yu, Michael Lustig
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI).
1 code implementation • 27 May 2020 • Michael Kellman, Michael Lustig, Laura Waller
The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Michael Kellman, Kevin Zhang, Jon Tamir, Emrah Bostan, Michael Lustig, Laura Waller
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 classical model-based reconstructions (termed physics-based networks).
no code implementations • 11 Dec 2019 • Michael Kellman, Jon Tamir, Emrah Boston, Michael Lustig, Laura Waller
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems.
1 code implementation • 30 Sep 2019 • Frank Ong, Xucheng Zhu, Joseph Y. Cheng, Kevin M. Johnson, Peder E. Z. Larson, Shreyas S. Vasanawala, Michael Lustig
We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory.
Medical Physics Image and Video Processing
1 code implementation • 25 Feb 2019 • 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 data.
Medical Physics
1 code implementation • 14 Nov 2018 • Siddharth Iyer, Frank Ong, Kawin Setsompop, Mariya Doneva, Michael Lustig
The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams.
Medical Physics
no code implementations • 11 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.
1 code implementation • 15 Sep 2017 • Frank Ong, Joseph Cheng, Michael Lustig
Purpose: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging.
no code implementations • 1 Jul 2017 • Patrick Virtue, Stella X. Yu, Michael Lustig
The task of MRI fingerprinting is to identify tissue parameters from complex-valued MRI signals.
1 code implementation • 29 Jun 2017 • H. Christian M. Holme, Sebastian Rosenzweig, Frank Ong, Robin N. Wilke, Michael Lustig, Martin Uecker
Robustness against data inconsistencies, imaging artifacts and acquisition speed are crucial factors limiting the possible range of applications for magnetic resonance imaging (MRI).
Medical Physics
no code implementations • 3 Oct 2016 • Patrick Virtue, Michael Lustig
The effects of lower SNR and the underdetermined system are coupled during reconstruction, making it difficult to isolate the impact of lower SNR on image quality.
2 code implementations • 31 Jul 2015 • Frank Ong, Michael Lustig
We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales.
Systems and Control Information Theory Numerical Analysis Information Theory Optimization and Control
1 code implementation • 17 Jul 2015 • Martin Uecker, Michael Lustig
Based on this method, a new post-processing step is proposed for the explicit computation of coil sensitivities that include the absolute phase of the image.