US 12,241,953 B2
Systems and methods for accelerated magnetic resonance imaging (MRI) reconstruction and sampling
Jeffrey Allen Fessler, Ann Arbor, MI (US); Douglas Clair Noll, Ann Arbor, MI (US); and Guanhua Wang, Ann Arbor, MI (US)
Assigned to REGENTS OF THE UNIVERSITY OF MICHIGAN, Ann Arbor, MI (US)
Filed by REGENTS OF THE UNIVERSITY OF MICHIGAN, Ann Arbor, MI (US)
Filed on Jan. 17, 2023, as Appl. No. 18/097,632.
Claims priority of provisional application 63/301,944, filed on Jan. 21, 2022.
Prior Publication US 2023/0236271 A1, Jul. 27, 2023
Int. Cl. G01V 3/00 (2006.01); G01R 33/48 (2006.01); G06T 5/20 (2006.01); G06T 5/70 (2024.01); G06T 11/00 (2006.01)
CPC G01R 33/4826 (2013.01) [G06T 5/20 (2013.01); G06T 5/70 (2024.01); G06T 11/008 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2210/41 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for designing a non-Cartesian sampling trajectory for either a prespecified image reconstructor or an optimized image reconstructor for producing a magnetic resonance imaging (MRI) image, the method comprising:
training, via one or more processors, a MRI machine learning model to design a non-Cartesian MRI sampling trajectory for either the prespecified image reconstructor or for the optimized image reconstructor for producing an MRI image;
parameterizing, by the one or more processors, the non-Cartesian sampling trajectory using a basis function set;
generating, by the one or more processors, the non-Cartesian sampling trajectory for imaging a patient using the MRI machine learning model;
generating, by the one or more processors, MRI data for the patient using the non-Cartesian sampling trajectory;
reconstructing, by the one or more processors, the MRI data using either prespecified reconstructor or the optimized image reconstructor; and
storing, by the one or more processors, the reconstructed MRI data in a memory.