Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network

Kanghyun Ryu; Na Young Shin; Dong Hyun Kim; Yoonho Nam

doi:10.1016/j.mri.2019.04.002

Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network

Kanghyun Ryu, Na Young Shin, Dong Hyun Kim, Yoonho Nam

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T₁-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE for tissue segmentation which require lengthened scan time and additional processing such as image registration. This study investigated the feasibility of generating synthetic MPRAGE images from mGRE images using a deep convolutional neural network. Tissue segmentation results derived from the synthetic MPRAGE showed good agreement with those from actual MPRAGE (DSC = 0.882 ± 0.017). There was no statistically significant difference between the mean susceptibility values obtained with the regions of interest from synthetic and actual MPRAGEs and high correlation between the two measurements.

Original language	English
Pages (from-to)	13-20
Number of pages	8
Journal	Magnetic Resonance Imaging
Volume	64
DOIs	https://doi.org/10.1016/j.mri.2019.04.002
Publication status	Published - 2019 Dec

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning ( NRF-2017R1D1A1B03030772 , NRF-2018M3C7A1056884 , NRF-2016R1C1B1009247 ).

Funding Information:
The research was supported by Graduate Student Scholarship Program funded by Hyundai Motor Chung Mong-Koo Foundation .

Publisher Copyright:
© 2019 Elsevier Inc.

All Science Journal Classification (ASJC) codes

Biophysics
Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1016/j.mri.2019.04.002

Cite this

@article{2db6b26fd5904303a95c30865ad8ef60,

title = "Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network",

abstract = "For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T1-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE for tissue segmentation which require lengthened scan time and additional processing such as image registration. This study investigated the feasibility of generating synthetic MPRAGE images from mGRE images using a deep convolutional neural network. Tissue segmentation results derived from the synthetic MPRAGE showed good agreement with those from actual MPRAGE (DSC = 0.882 ± 0.017). There was no statistically significant difference between the mean susceptibility values obtained with the regions of interest from synthetic and actual MPRAGEs and high correlation between the two measurements.",

author = "Kanghyun Ryu and Shin, {Na Young} and Kim, {Dong Hyun} and Yoonho Nam",

note = "Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning ( NRF-2017R1D1A1B03030772 , NRF-2018M3C7A1056884 , NRF-2016R1C1B1009247 ). Funding Information: The research was supported by Graduate Student Scholarship Program funded by Hyundai Motor Chung Mong-Koo Foundation . Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",

year = "2019",

month = dec,

doi = "10.1016/j.mri.2019.04.002",

language = "English",

volume = "64",

pages = "13--20",

journal = "Magnetic Resonance Imaging",

issn = "0730-725X",

publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network

AU - Ryu, Kanghyun

AU - Shin, Na Young

AU - Kim, Dong Hyun

AU - Nam, Yoonho

N1 - Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning ( NRF-2017R1D1A1B03030772 , NRF-2018M3C7A1056884 , NRF-2016R1C1B1009247 ). Funding Information: The research was supported by Graduate Student Scholarship Program funded by Hyundai Motor Chung Mong-Koo Foundation . Publisher Copyright: © 2019 Elsevier Inc.

PY - 2019/12

Y1 - 2019/12

N2 - For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T1-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE for tissue segmentation which require lengthened scan time and additional processing such as image registration. This study investigated the feasibility of generating synthetic MPRAGE images from mGRE images using a deep convolutional neural network. Tissue segmentation results derived from the synthetic MPRAGE showed good agreement with those from actual MPRAGE (DSC = 0.882 ± 0.017). There was no statistically significant difference between the mean susceptibility values obtained with the regions of interest from synthetic and actual MPRAGEs and high correlation between the two measurements.

AB - For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T1-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE for tissue segmentation which require lengthened scan time and additional processing such as image registration. This study investigated the feasibility of generating synthetic MPRAGE images from mGRE images using a deep convolutional neural network. Tissue segmentation results derived from the synthetic MPRAGE showed good agreement with those from actual MPRAGE (DSC = 0.882 ± 0.017). There was no statistically significant difference between the mean susceptibility values obtained with the regions of interest from synthetic and actual MPRAGEs and high correlation between the two measurements.

UR - http://www.scopus.com/inward/record.url?scp=85063980909&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063980909&partnerID=8YFLogxK

U2 - 10.1016/j.mri.2019.04.002

DO - 10.1016/j.mri.2019.04.002

M3 - Article

C2 - 30953698

AN - SCOPUS:85063980909

SN - 0730-725X

VL - 64

SP - 13

EP - 20

JO - Magnetic Resonance Imaging

JF - Magnetic Resonance Imaging

ER -

Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this