A convolutional neural network based super resolution technique of CT image utilizing both sinogram domain and image domain data

Abstract

In previous deep learning based super-resolution techniques for CT images, only image domain data is used for training. However, image blurring can occur in image domain method which disrupts accurate diagnosis. In this work, we propose using both sinogram and image domain data to resolve the blurring issue. To predict upsampled sinogram more accurately, we use a convolutional neural network (CNN) as an encoder, which maps an input image to feature map for decoder. For decoder, we use dual multi-layer perceptron (MLP) structure. Our proposed dual-MLP structure consists of modulator and synthesizer MLP. Synthesizer MLP predicts the output pixel value by using coordinate-based information as an input, and modulator MLP helps synthesizer to estimate the output value accurately by using feature map information as an input. This network structure preserves high frequency components better than simple CNN structure. Through our proposed sinogram upsampling network (SUN) at sinogram domain, upsampled sinogram was generated, and image was reconstructed by filtered backprojection. The reconstructed image from upsampled sinogram preserves detailed textures compared to LR image. However, residual artifacts and blur still remain. Therefore, we train CNN using image domain data to reduce residual artifacts and blur. For the dataset, we acquire projection data from Mayo Clinic image using Siddon's algorithm in fan-beam CT geometry applying 4x1 detector binning. The binned sinogram is then used as an input for the SUN. The results show that our proposed hybrid domain method outperforms image domain and sinogram domain method with higher quantitative evaluation results.