A SPATIO-TEMPORAL ATLAS OF NEONATAL DIFFUSION MRI BASED ON KERNEL RIDGE REGRESSION

被引：0

作者：

Shen, Kaikai ^{[1
]}

Fripp, Jurgen ^{[1
]}

Pannek, Kerstin ^{[1
]}

George, Joanne ^{[2
]}

Colditz, Paul ^{[3
]}

Boyd, Roslyn ^{[2
]}

Rose, Stephen ^{[1
]}

机构：

[1] CSIRO Hlth & Biosecur, Australian eHlth Res Ctr, Canberra, ACT, Australia

[2] Univ Queensland, Sch Med, Queensland Cerebral Palsy & Rehabil Res Ctr, Brisbane, Qld, Australia

[3] Univ Queensland, Ctr Clin Res, Brisbane, Qld, Australia

来源：

2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017) | 2017年

关键词：

spatio-temporal atlas; neonatal neuroimaging; diffusion MRI; fibre orientation distribution; kernel ridge regression; CONSTRAINED SPHERICAL DECONVOLUTION; BRAIN; SEGMENTATION; ORGANIZATION; MATTER;

D O I：

暂无

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Spatio-temporal atlas is a useful tool in imaging studies of neurodevelopment, which characterizes the growth of brain, and allows the monitoring of its development. The imaging of preterm and term born infants provides opportunities to develop a series of spatio-temporal atlases that track the changes during the particular period of neurodevelopment between. The aim of this paper is to develop a spatiot-emporal atlas of diffusion MRI for neonatal brains between 32 to 42 weeks postmenstrual age (PMA). We subdivided the cohort consisting of preterm- and term-born infants according to their PMA at the MRI scan based on a kernel ridge regression, and generated the atlases based on Fibre Orientation Distribution (FOD) reconstruction of the diffusion data.

引用

页码：126 / 129

页数：4

共 50 条

[1] Kernel averaged predictors for spatio-temporal regression models
Heaton, Matthew J.
Gelfand, Alan E.
[J]. SPATIAL STATISTICS, 2012, 2 : 15 - 32
[2] Adaptive kernel smoothing regression for spatio-temporal environmental datasets
Pouzols, Federico Montesino
Lendasse, Amaury
[J]. NEUROCOMPUTING, 2012, 90 : 59 - 65
[3] Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression
Serag, Ahmed
Aljabar, Paul
Ball, Gareth
Counsel, Serena J.
Boardman, James P.
Rutherford, Mary A.
Edwards, A. David
Hajnal, Joseph V.
Rueckert, Daniel
[J]. NEUROIMAGE, 2012, 59 (03) : 2255 - 2265
[4] A spatio-temporal regression model for the analysis of functional MRI data
Katanoda, K
Matsuda, Y
Sugishita, M
[J]. NEUROIMAGE, 2002, 17 (03) : 1415 - 1428
[5] Kernel regression in mixed feature spaces for spatio-temporal saliency detection
Li, Yansheng
Tan, Yihua
Yu, Jin-Gang
Qi, Shengxiang
Tian, Jinwen
[J]. COMPUTER VISION AND IMAGE UNDERSTANDING, 2015, 135 : 126 - 140
[6] Spatio-temporal graphical modeling with innovations based on multi-scale diffusion kernel
Chalmond, Bernard
[J]. SPATIAL STATISTICS, 2014, 7 : 40 - 61
[7] A New Non-Separable Kernel for Spatio-Temporal Gaussian Process Regression
Gallagher, Sean
Quinn, Anthony
[J]. 2023 34TH IRISH SIGNALS AND SYSTEMS CONFERENCE, ISSC, 2023,
[8] Spatio-temporal conjecture for diffusion
Vainstein, MH
Morgado, R
Oliveira, FA
[J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2005, 357 (01) : 109 - 114
[9] Clustering Spatio-temporal Trajectories Based on Kernel Density Estimation
Zhang, Pengdong
Deng, Min
Van de Weghe, Nico
[J]. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2014, PT 1, 2014, 8579 : 298 - 311
[10] A spatio-temporal memory based on SOMs with activity diffusion
Euliano, NR
Principe, JC
[J]. KOHONEN MAPS, 1999, : 253 - 265

← 1 2 3 4 5 →