• Corpus ID: 219177277

Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network

  title={Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network},
  author={Tianling Lyu and Zhan Wu and Yikun Zhang and Yang Chen and Lei Xing and Wei Zhao},
  journal={arXiv: Medical Physics},
Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT… 

Monochromatic image reconstruction via machine learning

A one-dimensional network model is proposed to learn a non-linear transform from a training dataset to map a polychromatic CT image to its monochromatic sinogram at a pre-specified energy level, realizing virtual monochchromatic (VM) imaging effectively and efficiently.

X-ray Monochromatic Imaging from Single-spectrum CT via Machine Learning

A machine-learning-based CT reconstruction method is proposed to perform monochromatic image reconstruction using a single-spectrum CT scanner with great potential in clinical DECT applications such as tissue characterization, beam hardening correction and proton therapy planning.



Pseudo dual energy CT imaging using deep learning-based framework: basic material estimation

A deep learning-based framework to obtain basic material images directly form single energy CT images via cascade deep convolutional neural networks (CD-ConvNet) can help to improve research utility of CT in quantitative imaging, especially in single energyCT.

Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning

A deep learning approach to perform DECT imaging by using the standard SECT data and may enable significantly simplified hardware design, scanning dose and image cost reduction for future DECT systems.

Physics-informed Deep Learning for Dual-Energy Computed Tomography Image Processing

A convolutional neural network is trained to develop a framework to reconstruct non-contrast SECT images from DECT scans that leverages the underlying physics of the DECT image generation process as well as the anatomic information gleaned via training with actual images can generate higher fidelity processed DECT images.

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.

Development of a deep neural network for generating synthetic dual-energy chest x-ray images with single x-ray exposure

The proposed algorithm for the synthesis of DECR from a SECR through deep learning can obtain high-quality bone- and soft tissue-only CR images without the need for additional hardware for double x-ray exposures in clinical practice.

Image domain dual material decomposition for dual-energy CT using butterfly network.

A model-based Butterfly network is developed to perform image domain material decomposition for DECT and the decomposition results of digital phantom validate its capability of decomposing two basis materials from DECT images.

Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity that is capable of not only reducing the image noise level but also trying to keep the critical information at the same time.

Single-Scan Dual-Energy CT Using Primary Modulation

By granting the opportunity for high-quality single-scan DECT on conventional CT scanners via limited hardware modification, PM-DECT has the potential to liberate DECT from specialized scanners, extending clinical availability, and implementation.

Accurate Multi-Material Decomposition in Dual-Energy CT: A Phantom Study

A statistical MMD (SMMD) algorithm, which applied the statistical weight to account for the noise variance in the DECT images to suppress the noise, and achieves an overall improvement of the normalized cross-correlation matrix diagonality.

Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT).

  • Wei ZhaoB. Han L. Xing
  • Medicine, Physics
    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
  • 2019