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

Wei Zhao, Tianling Lv, Rena Lee, Yang Chen, Lei Xing

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations

Abstract

Computed tomographic (CT) is a fundamental imaging modality to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object, and it has been routinely used in clinical applications and nondestructive testing. In a standard CT image, pixels having the same Houns-eld Units (HU) can correspond to different materials, and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but the costly DECT scanners are not widely available as single-energy CT (SECT) scanners. Recent advancement in deep learning provides an enabling tool to map images between different modalities with incorporated prior knowledge. Here we develop a deep learning approach to perform DECT imaging by using the standard SECT data. The end point of the approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. The feasibility of the deep learning-based DECT imaging method using a SECT data is demonstrated using contrast-enhanced DECT images and evaluated using clinical relevant indexes. This work opens new opportunities for numerous DECT clinical applications with a standard SECT data and may enable significantly simplified hardware design, scanning dose and image cost reduction for future DECT systems.

Original languageEnglish
Pages (from-to)139-148
Number of pages10
JournalPacific Symposium on Biocomputing
Volume25
Issue number2020
StatePublished - 2020
Event25th Pacific Symposium on Biocomputing, PSB 2020 - Big Island, United States
Duration: 3 Jan 20207 Jan 2020

Keywords

  • Convolutional neural network
  • Deep learning
  • Dual-energy computed tomography
  • Io-dine quantification.
  • Material decomposition
  • Single-energy computed tomography
  • Virtual non-contrast

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