- Huixiang Zhuang hz2538@COLUMBIA.EDU
These instructions will provide you a guideline for our basic functions as well as how to running on your machine for development and testing purposes.
The objective is to create a network that can take a low-resolution MRI scan and turn it into a super resolution scan.
This repo aims at reproducing the results of the paper https://arxiv.org/abs/1803.01417. The network architecture is shown below:
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Data Augmentation method
I had to make Low Resolution image data for input data. I tried fftshift filter, Gausian filter and mixing some filters and so on. What I choose as the best method is taking fft and inverse fftshift with random coefficient and bigger zeroing size (i.e. in this model, 160) -
Decreasing number of paparameters
I tried to decrease number of parameters of discriminator. The method which I used is replacing 3x3 CONV by 1x1 CONV and fully-connected layers by global average pooling. I decreased the number of paramrters from 31.156M to 17.629M. -
Gradient Penalty
I used Gradient Penalty term to Wasserstein-GAN. Since It is replaced with weight clipping, I assumed this trem will help to get better performance.
The dataset is a large and publicly accessible brain structural MRI database called Human Connectome Project (HCP): https://www.humanconnectomeproject.org. The data contains 3D T1-weighted images from a total of 1,113 subjects that were acquired via a Siemens 3T platform using 32-channel head coils on multiple centers. The images come in high spatial resolution as 0.7 mm isotropic in a matrix size of 256x320x320. You need to register and log in the website: https://db.humanconnectome.org. You can either download full dataset from that website or request access to their Amazon S3.
pip install numpy matplotlib scipy nibabel pandas skimage
See full details of the environment requirements in requirements.txt.
|-- data_prep
|-- CSV_reader.ipynb
|-- DataProvider.py
|-- LRbyFFT.ipynb
|-- idx_mine.mat
|-- nii2npy.ipynb
|-- patchloader.py
|-- my_models
|-- mdcsrn_fragment.py
|-- result
|-- eval_metrics.pt
|-- main.ipynb
|-- training_pre_.py
|-- wgan_gp_v3.py
|-- README.md