Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Sandino, Christopher M."'
Autor:
Oscanoa, Julio A., Ong, Frank, Iyer, Siddharth S., Li, Zhitao, Sandino, Christopher M., Ozturkler, Batu, Ennis, Daniel B., Pilanci, Mert, Vasanawala, Shreyas S.
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for 3D non-Cartesian acquisitions. One common approach is to reduce th
Externí odkaz:
http://arxiv.org/abs/2305.06482
Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for E
Externí odkaz:
http://arxiv.org/abs/2211.02625
Autor:
Ozturkler, Batu, Sahiner, Arda, Ergen, Tolga, Desai, Arjun D, Sandino, Christopher M, Vasanawala, Shreyas, Pauly, John M, Mardani, Morteza, Pilanci, Mert
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. However
Externí odkaz:
http://arxiv.org/abs/2207.08393
Autor:
Desai, Arjun D, Schmidt, Andrew M, Rubin, Elka B, Sandino, Christopher M, Black, Marianne S, Mazzoli, Valentina, Stevens, Kathryn J, Boutin, Robert, Ré, Christopher, Gold, Garry E, Hargreaves, Brian A, Chaudhari, Akshay S
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to ti
Externí odkaz:
http://arxiv.org/abs/2203.06823
Autor:
Desai, Arjun D, Ozturkler, Batu M, Sandino, Christopher M, Boutin, Robert, Willis, Marc, Vasanawala, Shreyas, Hargreaves, Brian A, Ré, Christopher M, Pauly, John M, Chaudhari, Akshay S
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particular
Externí odkaz:
http://arxiv.org/abs/2110.00075
Autor:
Wang, Ke, Kellman, Michael, Sandino, Christopher M., Zhang, Kevin, Vasanawala, Shreyas S., Tamir, Jonathan I., Yu, Stella X., Lustig, Michael
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 leverage high-dimensional data (e.g. 3D, 2D+time, 3D+time) to further
Externí odkaz:
http://arxiv.org/abs/2103.04003
A novel neural network architecture, known as DL-ESPIRiT, is proposed to reconstruct rapidly acquired cardiac MRI data without field-of-view limitations which are present in previously proposed deep learning-based reconstruction frameworks. Additiona
Externí odkaz:
http://arxiv.org/abs/1911.05845
Autor:
Malavé, Mario O., Baron, Corey A., Koundinyan, Srivathsan P., Sandino, Christopher M., Ong, Frank, Cheng, Joseph Y., Nishimura, Dwight G.
Purpose: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model for non-rigid motion correction in coronary magnetic resonance angiography (CMRA). Methods: An unrolled network is
Externí odkaz:
http://arxiv.org/abs/1910.11414
Akademický článek
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Autor:
Oscanoa, Julio A., Ong, Frank, Iyer, Siddharth S., Li, Zhitao, Sandino, Christopher M., Ozturkler, Batu, Ennis, Daniel B., Pilanci, Mert, Vasanawala, Shreyas S.
Publikováno v:
Magnetic Resonance in Medicine; Feb2024, Vol. 91 Issue 2, p784-802, 19p