US 11,808,832 B2
System and method for deep learning-based generation of true contrast images utilizing synthetic magnetic resonance imaging data
Sudhanya Chatterjee, Bangalore (IN); and Dattesh Dayanand Shanbhag, Bangalore (IN)
Assigned to GE Precision Healthcare LLC, Wauwatosa, WI (US)
Filed by GE PRECISION HEALTHCARE LLC, Wauwatosa, WI (US)
Filed on Jun. 10, 2021, as Appl. No. 17/344,274.
Prior Publication US 2022/0397627 A1, Dec. 15, 2022
Int. Cl. G06T 7/00 (2017.01); G01R 33/565 (2006.01); G06N 3/084 (2023.01)
CPC G01R 33/56554 (2013.01) [G06N 3/084 (2013.01); G06T 7/0012 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating an artifact corrected reconstructed contrast image from magnetic resonance imaging (MRI) data, comprising:
inputting into a trained deep neural network both a synthesized contrast image derived from multi-delay multi-echo (MDME) scan data or the MDME scan data acquired during a first scan of an object of interest utilizing a MDME sequence and a composite image, wherein the composite image is derived from both the MDME scan data and contrast scan data acquired during a second scan of the object of interest utilizing a contrast MRI sequence;
utilizing the trained deep neural network to generate the artifact corrected reconstructed contrast image based on both the synthesized contrast image or the MDME scan data and the composite image; and
outputting from the trained deep neural network the artifact corrected reconstructed contrast image.