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 |
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.
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