This is a CT denoise model. In reality, the projection data will have noise, so it need to design a model to denoise the final reconstruction result.

In our experiment, we add noise into the projection data, and then use FBP algorithm to reconstruction, after that use the DWConvResNet to train by the reconstruction result with noise, compare with the clean reconstruction result.

The DWConvResNet is base ResNet but we remove the pooling layer and change conv to depthwise conv(in tflearn, the depthwise conv is grouped_conv_2d), and add the Channel Shuffle between two depthwise conv. We define a new resnet_dwconv_block like below:

bn->relu->depthwiseconv->ChannelShuffle->bn->relu->depthwiseconv->bn->relu->conv

And the network basic structure is:

   resnet_dwconv_block(outputchannels=64)
   resnet_dwconv_block(outputchannels=128)
   resnet_dwconv_block(outputchannels=256)
   resnet_dwconv_block(outputchannels=128)
   resnet_dwconv_block(outputchannels=64)

The loss is begin with 25549, final loss is 106.2. Here is the loss curve:

This model can be trained by denosie the gaussian noise, the result like this: The first one is the original picture, second one is add gaussian nosie, the third one is the reconstruction result by using DWConvResNet

And alse can be trained by denoise the multi-noise(like gaussian noise, poisson nosie and salt&pepper nosie), train loss like this: Loss at begin is 26390, the final loss is 20.77. Result like below: The first column is orignial picture, second column is add noise(first one is add poisson noise, second one is add gaussian noise, third one is add salt&pepper noise), third column is the denoise picutre.