Zobrazeno 1 - 10
of 337
pro vyhledávání: '"Vasanawala, Shreyas S."'
Autor:
Zhang, Chi, Loecher, Michael, Alkan, Cagan, Yurt, Mahmut, Vasanawala, Shreyas S., Ennis, Daniel B.
In recent years, machine learning (ML) based reconstruction has been widely investigated and employed in cardiac magnetic resonance (CMR) imaging. ML-based reconstructions can deliver clinically acceptable image quality under substantially accelerate
Externí odkaz:
http://arxiv.org/abs/2411.10403
Autor:
Alkan, Cagan, Mardani, Morteza, Liao, Congyu, Li, Zhitao, Vasanawala, Shreyas S., Pauly, John M.
Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we
Externí odkaz:
http://arxiv.org/abs/2306.02888
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
Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep-learning framework, trained by radiologists' supervision, for
Externí odkaz:
http://arxiv.org/abs/2211.04703
In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality asse
Externí odkaz:
http://arxiv.org/abs/2111.03780
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
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is o
Externí odkaz:
http://arxiv.org/abs/2008.13065
Autor:
Dwork, Nicholas, Kerr, Adam B., Johnson, Ethan M. I., Baron, Corey A., Vasanawala, Shreyas S., Larson, Peder E. Z., Bush, Adam M., Pauly, John
We propose multiMap, a single scan that can generate several quantitative maps simultaneously. The sequence acquires multiple images in a time-efficient manner, which can be modeled for T_2, T2*, main- and transmit-field inhomogeneity, T_1:equilibriu
Externí odkaz:
http://arxiv.org/abs/2007.15495
Many real-world signal sources are complex-valued, having real and imaginary components. However, the vast majority of existing deep learning platforms and network architectures do not support the use of complex-valued data. MRI data is inherently co
Externí odkaz:
http://arxiv.org/abs/2004.01738
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