US 12,222,413 B2
Randomized dimension reduction for magnetic resonance image iterative reconstruction
Julio A. Oscanoa Aida, Stanford, CA (US); Frank Ong, Palo Alto, CA (US); Mert Pilanci, Palo Alto, CA (US); and Shreyas S. Vasanawala, Stanford, CA (US)
Assigned to The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
Filed by The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
Filed on May 13, 2022, as Appl. No. 17/744,299.
Claims priority of provisional application 63/188,618, filed on May 14, 2021.
Prior Publication US 2022/0381863 A1, Dec. 1, 2022
Int. Cl. G01R 33/561 (2006.01); A61B 5/055 (2006.01); G01R 33/54 (2006.01); G06T 11/00 (2006.01)
CPC G01R 33/5611 (2013.01) [A61B 5/055 (2013.01); G01R 33/543 (2013.01); G06T 11/006 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A method for magnetic resonance imaging comprising:
acquiring with multiple receiver coils of an MRI imaging apparatus pseudorandomly undersampled k-space imaging data;
performing MR image reconstruction to produce a reconstructed MR image from the pseudorandomly undersampled k-space imaging data;
wherein the reconstruction comprises iteratively solving sketched approximations of an original reconstruction problem, wherein the sketched approximations use a sketched model matrix As that is a lower-dimensional version of an original model matrix A of the original reconstruction problem;
wherein the sketched model matrix As preserves the Fourier structure of the MR reconstruction problem and reduces the number of coils actively used during reconstruction.