Zobrazeno 1 - 10
of 346
pro vyhledávání: '"Cheng, Joseph Y."'
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:
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
Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challenges, we propose a self-supervised approach based on contrastive learni
Externí odkaz:
http://arxiv.org/abs/2007.04871
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
Autor:
Ma, Jeffrey, Nakarmi, Ukash, Kin, Cedric Yue Sik, Sandino, Christopher, Cheng, Joseph Y., Syed, Ali B., Wei, Peter, Pauly, John M., Vasanawala, Shreyas
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by exp
Externí odkaz:
http://arxiv.org/abs/1912.02907
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:
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:
Koundinyan, Srivathsan P., Cheng, Joseph Y., Malave, Mario O., Yang, Phillip C., Hu, Bob S., Nishimura, Dwight G., Baron, Corey A.
Purpose: To study the accuracy of motion information extracted from beat-to-beat 3D image-based navigators (3D iNAVs) collected using a variable-density cones trajectory with different combinations of spatial resolutions and scan acceleration factors
Externí odkaz:
http://arxiv.org/abs/1910.12185
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