Zobrazeno 1 - 10
of 199
pro vyhledávání: '"Ong, Frank"'
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
Autor:
Iyer, Siddharth Srinivasan, Ong, Frank, Cao, Xiaozhi, Liao, Congyu, Daniel, Luca, Tamir, Jonathan I., Setsompop, Kawin
This work aims to accelerate the convergence of proximal gradient methods used to solve regularized linear inverse problems. This is achieved by designing a polynomial-based preconditioner that targets the eigenvalue spectrum of the normal operator d
Externí odkaz:
http://arxiv.org/abs/2204.10252
The Shinnar-Le-Roux (SLR) algorithm is widely used to design frequency selective pulses with large flip angles. We improve its design process to generate pulses with lower energy (by as much as 26%) and more accurate phase profiles. Concretely, the S
Externí odkaz:
http://arxiv.org/abs/2103.07629
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work
Externí odkaz:
http://arxiv.org/abs/2010.13214
Autor:
Piedra, Edgar A. Rios, Mardani, Morteza, Ong, Frank, Nakarmi, Ukash, Cheng, Joseph Y., Vasanawala, Shreyas
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice. DCE and similar datasets of dynamic medical data tend to contain redundant information on the spatial and temp
Externí odkaz:
http://arxiv.org/abs/2010.00003
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:
Koundinyan, Srivathsan P., Baron, Corey A., Malave, Mario O., Ong, Frank, Addy, Nii Okai, Cheng, Joseph Y., Yang, Phillip C., Hu, Bob S., Nishimura, Dwight G.
Purpose: To develop a respiratory-resolved motion-compensation method for free-breathing, high-resolution coronary magnetic resonance angiography using a 3D cones trajectory. Methods: To achieve respiratory-resolved 0.98 mm resolution images in a cli
Externí odkaz:
http://arxiv.org/abs/1910.12199
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
Autor:
Ong, Frank, Zhu, Xucheng, Cheng, Joseph Y., Johnson, Kevin M., Larson, Peder E. Z., Vasanawala, Shreyas S., Lustig, Michael
Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast enhanced (DCE) imaging. Theory and Methods: The problem considered
Externí odkaz:
http://arxiv.org/abs/1909.13482
Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite difference
Externí odkaz:
http://arxiv.org/abs/1906.11410