High-dimensional fast convolutional framework (HICU) for calibrationless MRI
AssigneeOhio State Innovation Foundation
InventorsShen Zhao, Rizwan Ahmad, Lee Potter
Life of this patent
Estimating the term…
Abstract
The present disclosure is directed to a computational procedure for accelerated, calibrationless magnetic resonance image (CI-MRI) reconstruction that is fast, memory efficient, and scales to high dimensional imaging. The computational procedure, High-dimensional fast ConvolUtional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points.
Claims20
A method for Magnetic Resonance Imaging (MRI) reconstruction comprising:
- acquiring undersampled k-space MRI data;
- interpolating and extrapolating the undersampled k-space MRI data to form a completed k-space data array;
- computing, by convergent iteration, the completed data array to minimize a cost function defined as the weighted distance of a multi-level block Hankel matrix to a manifold of matrices with a rank, r, that is an integer value associated with a cost function;
- converting the completed data array into reconstructed images;
- and
- displaying the reconstructed images.
A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for Magnetic Resonance Imaging (MRI) reconstruction, comprising:
- acquiring undersampled k-space MRI data;
- interpolating and extrapolating the undersampled k-space MRI data to form a completed k-space data array;
- computing, by convergent iteration, the completed data array to minimize a cost function defined as the weighted distance of a multi-level block Hankel matrix to a manifold of matrices with a rank, r, that is an integer value associated with a cost function:
- converting the completed data array into reconstructed images;
- and
- displaying the reconstructed images.
Dependencies are inferred from the claim text.