US 12,223,433 B2
Unsupervised learning method for general inverse problem and apparatus therefor
JongChul Ye, Daejeon (KR); Byeongsu Sim, Daejeon (KR); and Gyutaek Oh, Daejeon (KR)
Assigned to Korea Advanced Institute of Science and Technology, Daejeon (KR)
Filed by KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY, Daejeon (KR)
Filed on May 27, 2021, as Appl. No. 17/332,696.
Claims priority of application No. 10-2020-0089410 (KR), filed on Jul. 20, 2020.
Prior Publication US 2022/0027741 A1, Jan. 27, 2022
Int. Cl. G06N 3/088 (2023.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01)
CPC G06N 3/088 (2013.01) [G06F 18/214 (2023.01); G06N 3/045 (2023.01)] 9 Claims
OG exemplary drawing
 
1. An unsupervised learning method applicable to inverse problems, comprising:
receiving a training data set; and
training an unsupervised learning-based neural network generated based on an optimal transport theory and a penalized least square (PLS) approach using the training data set, wherein the neural network includes:
a first neural network configured to convert a first magnetic resonance image (MRI) obtained, as an input, from an intermittent Fourier spatial coefficient into a second MRI corresponding to a complete Fourier spatial coefficient;
a Fourier transform unit configured to output a third MRI corresponding to the first MRI by applying a Fourier transform and an inverse Fourier transform to the second MRI; and
a second neural network configured to discriminate between the second MRI and an actual MRI for the second MRI.