US 12,436,217 B2
Magnetic resonance imaging reconstruction using machine learning and motion compensation
Marcel Dominik Nickel, Herzogenaurach (DE)
Assigned to Siemens Healthineers AG, Erlangen (DE)
Filed by Siemens Healthcare GmbH, Erlangen (DE)
Filed on Aug. 25, 2021, as Appl. No. 17/411,461.
Claims priority of application No. 102020210775.0 (DE), filed on Aug. 26, 2020.
Prior Publication US 2022/0065970 A1, Mar. 3, 2022
Int. Cl. G06T 7/00 (2017.01); G01R 33/56 (2006.01); G01R 33/565 (2006.01); G06T 3/18 (2024.01); G06T 3/4046 (2024.01)
CPC G01R 33/56509 (2013.01) [G01R 33/5608 (2013.01); G06T 3/18 (2024.01); G06T 3/4046 (2013.01); G06T 7/0012 (2013.01); G06T 7/0016 (2013.01); G06T 7/0014 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of reconstructing a sequence of magnetic resonance imaging (MRI) images, comprising:
obtaining, via one or more processors, a sequence of MRI measurement datasets that have been acquired during a measurement time duration during which patient movement occurs, each respective one of MRI measurement datasets of the sequence of MRI measurement datasets being acquired using a respective under-sampling trajectory in k-space and a receiver coil array; and
performing, via the one or more processors, an iterative process to obtain a sequence of reconstructed MRI images based on the MRI measurement datasets,
wherein the iterative process comprises, for each iteration of multiple iterations of the iterative process, a regularization operation and a data-consistency operation to obtain a respective current MRI image,
wherein the data-consistency operation is based on differences between the MRI measurement datasets and synthesized MRI measurement datasets, the synthesized MRI measurement datasets being based on a k-space representation of a prior MRI image of the multiple iterations, an undersampling trajectory, and a sensitivity map associated with the receiver coil array, and
wherein an input to the regularization operation comprises, for each iteration of the multiple iterations, a concatenation of multiple prior images obtained from a previous iteration of the multiple iterations, the multiple prior images being associated with multiple motion states of the patient movement and with multiple points in time throughout the measurement time duration, the multiple prior images being warped from their respective motion states to a reference motion state.