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

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

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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 languageEnglish
Pages (from-to)13-20
Number of pages8
JournalMagnetic Resonance Imaging
Publication statusPublished - 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


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