TY - GEN
T1 - Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment
AU - Sharma, Anuja
AU - Fletcher, P. Thomas
AU - Gilmore, John H.
AU - Escolar, Maria L.
AU - Vardhan, Avantika
AU - Gupta, Aditya
AU - Styner, Martin
AU - Gerig, Guido
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.
AB - This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.
KW - Mallow's distance
KW - diffusion tensor imaging
KW - distribution-valued data
KW - early neurodevelopment
KW - spatiotemporal growth trajectory
UR - http://www.scopus.com/inward/record.url?scp=84881618101&partnerID=8YFLogxK
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U2 - 10.1109/ISBI.2013.6556567
DO - 10.1109/ISBI.2013.6556567
M3 - Conference contribution
AN - SCOPUS:84881618101
SN - 9781467364546
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 684
EP - 687
BT - ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
T2 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Y2 - 7 April 2013 through 11 April 2013
ER -