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FBPConvNet for computed tomography

Home Page: http://ieeexplore.ieee.org/document/7949028/

MATLAB 35.90% M 0.06% Objective-C 0.02% Makefile 1.00% TeX 9.07% Python 5.81% CSS 0.32% JavaScript 0.04% HTML 0.42% C++ 13.61% Cuda 30.64% C 1.20% Shell 1.89%

fbpconvnet's Introduction

FBPConvNet - Matlab

Deep Convolutional Neural Network for Inverse Problems in Imaging
http://ieeexplore.ieee.org/document/7949028/

Readme

  1. Before launching FBPConvNet, the MatConvNet (http://www.vlfeat.org/matconvnet/) have to be properly installed. (For the GPU, it needs a different compilation.)
  2. Properly modify matconvnet path in main.m and evaluation.m files.
  3. To start, download 2 links;
    (1) pretrained network : https://drive.google.com/open?id=0B9fSVcoxJuVwMVJ1eWFPdEEwbWs , then put this network into 'pretrain' folder
    (2) dataset : https://drive.google.com/open?id=0B9fSVcoxJuVwMDlxbXdvcTRaM2M just place this data in the same folder with main.m
  4. Use main.m for training. After training, run evaluation.m for deploy of test data set.

*note : phantom data set (x20) is only provided. SNR value may be slightly different with our paper.
*note : these codes mainly ran on Matlab 2016a with GPU TITAN X (architecture : Maxwell)
contact : Kyong Jin ([email protected]),

special thanks to Junhong Min (Senior Researcher at Samsung Electronics) for providing initial codes.

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