no code implementations • 17 Oct 2022 • Dave Van Veen, Rogier van der Sluijs, Batu Ozturkler, Arjun Desai, Christian Bluethgen, Robert D. Boutin, Marc H. Willis, Gordon Wetzstein, David Lindell, Shreyas Vasanawala, John Pauly, Akshay S. Chaudhari
We propose using a coordinate network decoder for the task of super-resolution in MRI.
1 code implementation • 18 Jul 2022 • Batu Ozturkler, Arda Sahiner, Tolga Ergen, Arjun D Desai, Christopher M Sandino, Shreyas Vasanawala, John M Pauly, Morteza Mardani, Mert Pilanci
However, they require several iterations of a large neural network to handle high-dimensional imaging tasks such as 3D MRI.
1 code implementation • 21 Apr 2022 • Beliz Gunel, Arda Sahiner, Arjun D. Desai, Akshay S. Chaudhari, Shreyas Vasanawala, Mert Pilanci, John Pauly
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.
1 code implementation • 3 Nov 2021 • Arjun D Desai, Beliz Gunel, Batu M Ozturkler, Harris Beg, Shreyas Vasanawala, Brian A Hargreaves, Christopher Ré, John M Pauly, Akshay S Chaudhari
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Christopher Michael Sandino, Frank Ong, Siddharth Srinivasan Iyer, Adam Bush, Shreyas Vasanawala
Model-based deep learning approaches, such as unrolled neural networks, have been shown to be effective tools for efficiently solving inverse problems.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Batu Ozturkler, Arda Sahiner, Tolga Ergen, Arjun D Desai, John M. Pauly, Shreyas Vasanawala, Morteza Mardani, Mert Pilanci
Model-based deep learning approaches have recently shown state-of-the-art performance for accelerated MRI reconstruction.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Philip M Adamson, Beliz Gunel, Jeffrey Dominic, Arjun D Desai, Daniel Spielman, Shreyas Vasanawala, John M. Pauly, Akshay Chaudhari
Self-supervised learning (SSL) has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data for downstream tasks.
1 code implementation • NeurIPS Workshop Deep_Invers 2021 • Victoria Liu, Kanghyun Ryu, Cagan Alkan, John M. Pauly, Shreyas Vasanawala
To address this issue, we propose multi-task learning (MTL) schemes that can jointly reconstruct multiple datasets.
1 code implementation • 30 Sep 2021 • Arjun D Desai, Batu M Ozturkler, Christopher M Sandino, Robert Boutin, Marc Willis, Shreyas Vasanawala, Brian A Hargreaves, Christopher M Ré, John M Pauly, Akshay S Chaudhari
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction.
no code implementations • 23 Oct 2020 • Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala, John M. Pauly
Reliable medical image recovery is crucial for accurate patient diagnoses, but little prior work has centered on quantifying uncertainty when using non-transparent deep learning approaches to reconstruct high-quality images from limited measured data.
no code implementations • 23 Oct 2020 • Cagan Alkan, Morteza Mardani, Shreyas Vasanawala, John M. Pauly
Accelerating MRI scans requires optimal sampling of k-space data.
no code implementations • 30 Sep 2020 • Edgar A. Rios Piedra, Morteza Mardani, Frank Ong, Ukash Nakarmi, Joseph Y. Cheng, Shreyas Vasanawala
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice.
no code implementations • 5 Dec 2019 • Jeffrey Ma, Ukash Nakarmi, Cedric Yue Sik Kin, Christopher Sandino, Joseph Y. Cheng, Ali B. Syed, Peter Wei, John M. Pauly, Shreyas Vasanawala
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts.
no code implementations • 10 Jun 2019 • Morteza Mardani, Qingyun Sun, Vardan Papyan, Shreyas Vasanawala, John Pauly, David Donoho
Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks.
no code implementations • 31 Jan 2019 • Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala, John Pauly
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks.
no code implementations • 27 Nov 2017 • Morteza Mardani, Hatef Monajemi, Vardan Papyan, Shreyas Vasanawala, David Donoho, John Pauly
Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination.
2 code implementations • 31 May 2017 • Morteza Mardani, Enhao Gong, Joseph Y. Cheng, Shreyas Vasanawala, Greg Zaharchuk, Marcus Alley, Neil Thakur, Song Han, William Dally, John M. Pauly, Lei Xing
A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.