TY - JOUR
T1 - Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning
AU - Zhao, Wei
AU - Lv, Tianling
AU - Lee, Rena
AU - Chen, Yang
AU - Xing, Lei
N1 - Funding Information:
This work was partially supported by NIH/NCI (1R01CA176553, 1R01CA223667 and 1R01CA227713) and a Faculty Research Award from Google Inc.
Publisher Copyright:
© 2019 The Authors.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - Deep learning
KW - Dual-energy computed tomography
KW - Io-dine quantification.
KW - Material decomposition
KW - Single-energy computed tomography
KW - Virtual non-contrast
UR - http://www.scopus.com/inward/record.url?scp=85075970217&partnerID=8YFLogxK
M3 - Conference article
C2 - 31797593
AN - SCOPUS:85075970217
VL - 25
SP - 139
EP - 148
JO - Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing
SN - 2335-6928
IS - 2020
Y2 - 3 January 2020 through 7 January 2020
ER -