eunh/low_dose_CT

Deep Convolutional Framelet Denoising for Low-Dose CT via Wavelet Residual Network

Paper

• A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction
  • published in Medical Physics (2017): [http://onlinelibrary.wiley.com/doi/10.1002/mp.12344/full]
  • 2nd winner of '2016 Low-Dose CT Grand Challenge'
• Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction
  • Accepted at Fully3D 2017: [https://arxiv.org/abs/1703.01383]
• Deep Convolutional Framelet Denoising for Low-Dose CT via Wavelet Residual Network
  • published in IEEE Transactions on Medical Imaging (2018): [https://ieeexplore.ieee.org/abstract/document/8332971]

Implementation

• MatConvNet (matconvnet-1.0-beta24)
  • Please run the matconvnet/matlab/vl_compilenn.m file to compile matconvnet.
  • There is instruction on "http://www.vlfeat.org/matconvnet/mfiles/vl_compilenn/"

Learned network

• Learned network for '2016 Low-Dose CT Grand Challenge' is uploaded.
• Learned network for WavResNet is uploaded.
• Learned network for Deep Convolutional Framelet Denoising is uploaded.

Test data

• 3 CT images from '2016 Low-Dose CT Grand Challenge' are uploaded to test.
  • Thanks Dr. Cynthia McCollough, the Mayo Clinic, the American Association of Physicists in Medicine(AAPM), and grand EB017095 and EB017185 from the National Institute of Biomedical Imaging and Bioengineering for providing the Low-Dose CT Grand Challenge dataset.