CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

Abstract

Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from lowresolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this deep imaging process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel 1 × 1 1 × 1 1 × 1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for superresolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three largescale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

“…Since its introduction in 2014, generative adversarial network (GAN) has achieved remarkable success in generative image modeling and has shown outstanding performances in numerous applications . The architecture of the generative adversarial network integrates two competing networks, a generative network and a discriminative network, into one framework.…”

Boosting Resolution and Recovering Texture of 2D and 3D Micro‐CT Images with Deep Learning

“…The neural network architecture used in this study can be made more efficient with wide activation network trimming (Yu et al, ) or more flexible with unpaired, unsupervised cyclical networks (You et al, ). The choice of loss function weights will affect the interplay between generator and discriminator (features and textures), and the availability of training data and augmentation further adds avenues of algorithmic improvement.…”

“…Cycle‐consistent GANs (cycleGANs) (Zhu et al, ) have been used as a semisupervised method of generating SR images by training on unpaired SR and LR datasets (You et al, ). While SRGAN results in features that look to the human eye as realistic when surveyed, the resulting generated SR images are lower in pixelwise accuracy compared to SRCNN due to pixelwise mismatch (You et al, ). Since CT images contain significant amounts of image noise and texture as high‐frequency features, this is also inadvertently recovered.…”

Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

“…Since PSNR and SSIM values cannot guarantee a visually favorable result, we employ an additional metric for the final results, namely the information fidelity criterion (IFC) [48]. Although IFC is scarcely used in literature [15], Yang et al [49] pointed out that IFC is correlated well with the human perception of SR images. As for PSNR and SSIM, higher IFC values indicate better results.…”