/CT-Denoising-Review

review of CT denoising paper

CT image denoising with deep learning

01. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction (KAIST-net)

paper

Dataset

  • AAPM-Mayo Clinic Low-Dose CT Grand Challenge (only abdominal CT images)
  • 512x512, 10 patients, 5743 slices
  • use a 55x55 patches

Model

  • This method works on wavelet coefficients of low-dose CT images
  • Network contains 24 convolution layers
    KAISTNET

02. Low-dose CT via Convolutional Neural Network

paper

Dataset

  • TCIA(The Cancer Imaging Archive) normal-dose CT images.
  • 256x256, 165 patients, 7015 slices.
  • impose Poisson noise into normal-dose sinogram.
  • use a 33x33 patches.

Model

  • Network use only 3 conoluional layers (Conv - ReLU - Conv - ReLU - Conv).

03. Improving Low-Dose CT Image Using Residual Convolutional Network

paper

Dataset

  • AAPM-Mayo Clinic Low-Dose CT Grand Challenge
  • 512x512, 10 patients, 5080 slices
  • use a 44x44 patches(2D), 44x44x24 patches(3D)

Model

  • 2D residual convolution net
  • 3D residual convolution net (take into account the spatial continuity of tissues)
    ResCNN

04. CT Image Denoising with Perceptive Deep Neural Networks

paper

Dataset

  • cadaver CT image dataset collected at Massachusetts General Hospital (MGH)

Model

  • Compare the denoised output against the ground truth in another high-dimensional feature space (from VGG)
    PerDNN

05. Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

paper

Dataset

  • NBIA(Natioanl Biomedical Imaging Archive) normal-dose CT images
  • 256x256, 165 patients, 7015 slices
  • adding Poisson noise into the sinogram simulated from the normal-dose images
  • AAPM-Mayo Clinic Low-Dose CT Grand Challenge
  • 512x512, 10 patients, 2378 slices
  • use a 55x55 patches

Model

  • Incoporated a deconvolution network and shortcut connections into a CNN model
    REDCNN

06. Generative adversarial networks for noise reduction in low-dose CT

paper

Dataset

  • Phantom CT scans
  • An anthropomorphic thorax phantom (QRM anthropomorphic thorax phantom)
  • voltage of 120 kVp. 50mAs(routine-dose), 10mAs(low-dose)
  • Cardiac CT scan (28 patients)
  • voltage of 120 kVp. 5060mAs(routine-dose), 1012mAs(low-dose)

Model

  • Generator transforms the low-dose CT image into noise reduced image
  • Discriminator determines whether the input is a real routine-dose image or not
    GAN

07. Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

paper

Dataset

  • AAPM-Mayo Clinic Low-Dose CT Grand Challenge
  • 512x512, 10 patients, 2378 slices
  • use a 80x80x11 patches

Model

  • (Part 1). Generator consist of eight 3D convolutional (Conv) layers
  • (Part 2). Calculate patch-wise error between the 3D output and the 3D NDCT images
    SMGAN
  • (Part 3). Discriminator distinguishes between two images
    SMGAN_loss

08. Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

paper

Dataset

  • AAPM-Mayo Clinic Low-Dose CT Grand Challenge
  • 512x512, 10 patients, 4000 slices
  • use a 64x64 patches

Model

  • GAN with Wasserstein distance
  • (Part 2). Comparing the perceptual feature of a denoised output against those of the ground truth in an established feature space
    WGAN

09. Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network

paper

Dataset

  • NBIA(Natioanl Biomedical Imaging Archive) normal-dose CT images
  • 512x512, 239 slices
  • adding Poisson + normally Gaussian noise
  • use a 256x256 patches (sampled from the 4 corners and center)
  • Deceased piglet CT
  • voltage of 100 kVp. 300mAs(full-dose) ~ 15mAs(low-dose)
  • Phantom CT scans
  • voltage of 120 kVp. 300mAs(full-dose) ~ 15mAs(low-dose)
  • Data Science Bowl 2017
  • Detect lung cancer from LDCTs

Model

  • Sharpness detection network : generate a similar sharpness map as closs as to real CT
    SAGAN SAGAN_loss

10. 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

paper

Dataset

  • AAPM-Mayo Clinic Low-Dose CT Grand Challenge
  • 512x512, 10 patients
  • use a 64x64 patches

Model

  • Concatenation of feature-maps from the two sides of the conveying-path
  • Learn the 2D model first, and use it to initialize the 3D network. This transfer learning shows much faster convergence and better performance
    Transfer

11. Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography

paper

Dataset

  • 50 CT scans of mitral valve prolapse patients, and 50 CT scans of coronary artery disease patients
  • use a 56x56 patches
  • AAPM-Mayo Clinic Low-Dose CT Grand Challenge

Model

  • In coronary CTA, the images at the low-dose and routine-dose phases do not match each other exactly due to the cardiac motion
  • Two generator denotes the mapping form low-dose to routine-dose image and from routine-dose to low-dose image, two adversarial discriminators distinguish between input images and synthesized images from the generators
  • Using cycle-consistent adversarial denoising network, learn the mapping between the low and routine dose cardiac phases
    CYCLEGAN