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Texture Matching GAN for CT Image Enhancement

arXiv 2312.13422 ↗First posted 20 Dec 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Deep neural networks (DNN) are commonly used to denoise and sharpen X-ray computed tomography (CT) images with the goal of reducing patient X-ray dosage while maintaining reconstruction quality. However, naive application of DNN-based methods can result in image texture that is undesirable in clinical applications. Alternatively, generative adversarial network (GAN) based methods can produce appropriate texture, but naive application of GANs can introduce inaccurate or even unreal image detail. In this paper, we propose a texture matching generative adversarial network (TMGAN) that enhances CT images while generating an image texture that can be matched to a target texture. We use parallel generators to separate anatomical features from the generated texture, which allows the GAN to be trained to match the desired texture without directly affecting the underlying CT image. We demonstrate that TMGAN generates enhanced image quality while also producing image texture that is desirable for clinical application.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 49 references that are in the map. First posted on arXiv 20 Dec 2023.

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12 of 49 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  4. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  5. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  6. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  7. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  8. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161
  9. 2018Distribution Matching Losses Can Hallucinate Features in Medical Image Translationcited by 99
  10. 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
  11. 2018CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)cited by 81
  12. 2017TextureGAN: Controlling Deep Image Synthesis with Texture Patchescited by 47

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