Coil2Coil

• The code is for denoising of MR images using deep learning referred to as Coil2Coil (C2C). C2C generated paired noise-corrupted images from phased-array coil data to train a deep neural network, and the paried images were modified to satisfy the conditions of Noise2Noise (N2N; Lehtinen, et al), enabling network training using N2N algorithm.
• last update : 2022. 08. 07

Reference

• will be updated

Overview

Requirements

• Python 3.7

• pytorch=1.9.0

• NVIDIA GPU

Usage

Installation

First, download the codes using the command below.

git clone https://github.com/SNU-LIST/Coil2Coil.git

Then, go inside 'Coil2Coil' file, and install the ssmd packages

Following command will be install a few dependent libraries such as Pytorch, Numpy, and etc...

pip install -e denoise

There might be 'denoise' library on your environment (pip list)

Inference

Run the eval.py with following codes

Arguments are provided below:

python eval.py -dir {directory to save logs} 
               -m {model weight path} 
               -d {dataset path for inference} 
               -noise {amount of noise (inf for just inference)}

Or if you just wanna inference one .mat file ...

python test_chan1.py -dir {directory to save logs} 
               -m {model weight path} 
               -d {dataset path for inference} 
               -noise {amount of noise (inf for just inference)}

Traininig

Run the train.py with following codes

Arguments are provided below:

python train.py -dir {directory to save logs} 
                -a {algorithm, 'c2c' or 'n2c'} 
                -t {path for training dataset} 
                -v {path for validation dataset} 
                -noise {amount of noise (inf or gauss(float))}

LIcense

We provide software for academic research purpose only and NOT for commercial or clinical use.

For commercial use of our software, contact us (snu.list.software@gmail.com) for licensing via Seoul National University.

Please email to “snu.list.software@gmail.com” with the following information.

Name:

Affiliation:

Software:

When sending an email, an academic e-mail address (e.g. .edu, .ac.) is required.

Contact

Juhyug Park, M.S-Ph.D candidate, Seoul National University

jack0878@snu.ac.kr

http://list.snu.ac.kr