US 11,756,160 B2
ML-based methods for pseudo-CT and HR MR image estimation
Chunjoo (Justin) Park, St. Louis, MO (US); Sasa Mutic, St. Louis, MO (US); Hao Zhang, St. Louis, MO (US); and Olga Green, St. Louis, MO (US)
Assigned to Washington University, St. Louis, MO (US)
Filed by Chunjoo (Justin) Park, St. Louis, MO (US); Sasa Mutic, St. Louis, MO (US); Hao Zhang, St. Louis, MO (US); and Olga Green, St. Louis, MO (US)
Filed on Jul. 29, 2019, as Appl. No. 16/525,562.
Claims priority of provisional application 62/818,993, filed on Mar. 15, 2019.
Claims priority of provisional application 62/711,023, filed on Jul. 27, 2018.
Prior Publication US 2020/0034948 A1, Jan. 30, 2020
Int. Cl. G06T 3/40 (2006.01); G06T 5/00 (2006.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06F 17/18 (2006.01); A61B 6/00 (2006.01); A61N 5/10 (2006.01); G06N 20/20 (2019.01); G06N 3/045 (2023.01)
CPC G06T 3/4053 (2013.01) [A61B 6/5258 (2013.01); A61N 5/1039 (2013.01); G06F 17/18 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/20 (2019.01); G06T 5/002 (2013.01); G06T 2207/10088 (2013.01)] 6 Claims
OG exemplary drawing
 
1. A computer-implemented method of transforming a low-resolution MR image into a super-resolution MR image using an MRI SR deep CNN system comprising a deep CNN-based de-noising auto-encoder (DAE) network and a deep CNN-based super-resolution generative network (SRG), the method comprising:
receiving, using a computing device, a low-resolution MR image;
transforming, using the computing device, the low-resolution MR image into a de-noised MR image using the DAE network, the DAE network comprising six convolutional encoder layers with 4×4 filters and six de-convolutional decoder layers with 4×4 filters, wherein each convolutional encoder layer comprises a single convolutional filter with stride 2, each de-convolution decoder layer comprises a single deconvolutional filter with stride 2, and each convolutional encoder layer and each de-convolution decoder layer ends with a leaky and standard rectified linear unit (ReLU); and,
transforming, using the computing device, the de-noised MR image into the super-resolution MR image using the SRG network.