Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI

no code implementations • 16 Oct 2021 • Abhijit Baul, Nian Wang, Choyi Zhang, Leslie Ying, Yuchou Chang, Ukash Nakarmi

Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure.

Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images

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.

Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case

no code implementations • 27 Feb 2020 • Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying

This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem.

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

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.

General Classification Image Quality Assessment

Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery

no code implementations • 27 Dec 2018 • Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying

This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI).

Dimensionality Reduction