Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks

Yoonmi Hong; Jaeil Kim; Geng Chen; Weili Lin; Pew Thian Yap; Dinggang Shen

doi:10.1109/TMI.2019.2911203

Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks

Yoonmi Hong, Jaeil Kim, Geng Chen, Weili Lin, Pew Thian Yap^*, Dinggang Shen

^*Corresponding author for this work

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

28 Citations (Scopus)

Abstract

Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data.

Original language	English
Article number	8691605
Pages (from-to)	2717-2725
Number of pages	9
Journal	IEEE Transactions on Medical Imaging
Volume	38
Issue number	12
DOIs	https://doi.org/10.1109/TMI.2019.2911203
Publication status	Published - 2019 Dec

Bibliographical note

Funding Information:
Manuscript received February 22, 2019; revised April 6, 2019; accepted April 7, 2019. Date of publication April 15, 2019; date of current version November 26, 2019. This work was supported in part by NIH under Grant NS093842, Grant MH117943, Grant EB006733, Grant EB009634, Grant AG041721, and Grant MH100217. (Corresponding authors: Pew-Thian Yap; Dinggang Shen.) Y. Hong, G. Chen, W. Lin, and P.-T. Yap are with the Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA, and also with the Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA (e-mail: [email protected]).

Publisher Copyright:
© 2019 IEEE.

Keywords

Graph CNN
adversarial learning
diffusion MRI
early brain development
longitudinal prediction

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TMI.2019.2911203

Cite this

@article{6724f68cd4b946cf9013d9f96e177e77,

title = "Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks",

abstract = "Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data.",

keywords = "Graph CNN, adversarial learning, diffusion MRI, early brain development, longitudinal prediction",

author = "Yoonmi Hong and Jaeil Kim and Geng Chen and Weili Lin and Yap, \{Pew Thian\} and Dinggang Shen",

note = "Funding Information: Manuscript received February 22, 2019; revised April 6, 2019; accepted April 7, 2019. Date of publication April 15, 2019; date of current version November 26, 2019. This work was supported in part by NIH under Grant NS093842, Grant MH117943, Grant EB006733, Grant EB009634, Grant AG041721, and Grant MH100217. (Corresponding authors: Pew-Thian Yap; Dinggang Shen.) Y. Hong, G. Chen, W. Lin, and P.-T. Yap are with the Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA, and also with the Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA (e-mail: [email protected]). Publisher Copyright: {\textcopyright} 2019 IEEE.",

year = "2019",

month = dec,

doi = "10.1109/TMI.2019.2911203",

language = "English",

volume = "38",

pages = "2717--2725",

journal = "IEEE Transactions on Medical Imaging",

issn = "0278-0062",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "12",

}

TY - JOUR

T1 - Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks

AU - Hong, Yoonmi

AU - Kim, Jaeil

AU - Chen, Geng

AU - Lin, Weili

AU - Yap, Pew Thian

AU - Shen, Dinggang

N1 - Funding Information: Manuscript received February 22, 2019; revised April 6, 2019; accepted April 7, 2019. Date of publication April 15, 2019; date of current version November 26, 2019. This work was supported in part by NIH under Grant NS093842, Grant MH117943, Grant EB006733, Grant EB009634, Grant AG041721, and Grant MH100217. (Corresponding authors: Pew-Thian Yap; Dinggang Shen.) Y. Hong, G. Chen, W. Lin, and P.-T. Yap are with the Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA, and also with the Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA (e-mail: [email protected]). Publisher Copyright: © 2019 IEEE.

PY - 2019/12

Y1 - 2019/12

N2 - Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data.

AB - Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data.

KW - Graph CNN

KW - adversarial learning

KW - diffusion MRI

KW - early brain development

KW - longitudinal prediction

UR - https://www.scopus.com/pages/publications/85068232902

U2 - 10.1109/TMI.2019.2911203

DO - 10.1109/TMI.2019.2911203

M3 - Article

C2 - 30990424

AN - SCOPUS:85068232902

SN - 0278-0062

VL - 38

SP - 2717

EP - 2725

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

IS - 12

M1 - 8691605

ER -

Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this