Modeling water diffusion anisotropy within fixed newborn primate brain using Bayesian probability theory

Christopher (Chris) Kroenke, G. Larry Bretthorst, Terrie E. Inder, Jeffrey J. Neil

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

An active area of research involves optimally modeling brain diffusion MRI data for various applications. In this study Bayesian analysis procedures were used to evaluate three models applied to phase-sensitive diffusion MRI data obtained from formalin-fixed perinatal primate brain tissue: conventional diffusion tensor imaging (DTI), a cumulant expansion, and a family of modified DTI expressions. In the latter two cases the optimum expression was selected from the model family for each voxel in the image. The ability of each model to represent the data was evaluated by comparing the magnitude of the residuals to the thermal noise. Consistent with previous findings from other laboratories, the DTI model poorly represented the experimental data. In contrast, the cumulant expansion and modified DTI expressions were both capable of modeling the data to within the noise using six to eight adjustable parameters per voxel. In these cases the model selection results provided a valuable form of image contrast. The successful modeling procedures differ from the conventional DTI model in that they allow the MRI signal to decay to a positive offset. Intuitively, the positive offset can be thought of as spins that are sufficiently restricted to appear immobile over the sampled range of b-values.

Original languageEnglish (US)
Pages (from-to)187-197
Number of pages11
JournalMagnetic Resonance in Medicine
Volume55
Issue number1
DOIs
StatePublished - Jan 2006
Externally publishedYes

Fingerprint

Probability Theory
Diffusion Tensor Imaging
Anisotropy
Primates
Water
Brain
Diffusion Magnetic Resonance Imaging
Bayes Theorem
Formaldehyde
Noise
Hot Temperature
Research

Keywords

  • Bayesian probability theory
  • Brain
  • Diffusion ansiotropy
  • MRI
  • Newborn

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Modeling water diffusion anisotropy within fixed newborn primate brain using Bayesian probability theory. / Kroenke, Christopher (Chris); Bretthorst, G. Larry; Inder, Terrie E.; Neil, Jeffrey J.

In: Magnetic Resonance in Medicine, Vol. 55, No. 1, 01.2006, p. 187-197.

Research output: Contribution to journalArticle

@article{f35d390dfec94977a508e2c94fcd04ce,
title = "Modeling water diffusion anisotropy within fixed newborn primate brain using Bayesian probability theory",
abstract = "An active area of research involves optimally modeling brain diffusion MRI data for various applications. In this study Bayesian analysis procedures were used to evaluate three models applied to phase-sensitive diffusion MRI data obtained from formalin-fixed perinatal primate brain tissue: conventional diffusion tensor imaging (DTI), a cumulant expansion, and a family of modified DTI expressions. In the latter two cases the optimum expression was selected from the model family for each voxel in the image. The ability of each model to represent the data was evaluated by comparing the magnitude of the residuals to the thermal noise. Consistent with previous findings from other laboratories, the DTI model poorly represented the experimental data. In contrast, the cumulant expansion and modified DTI expressions were both capable of modeling the data to within the noise using six to eight adjustable parameters per voxel. In these cases the model selection results provided a valuable form of image contrast. The successful modeling procedures differ from the conventional DTI model in that they allow the MRI signal to decay to a positive offset. Intuitively, the positive offset can be thought of as spins that are sufficiently restricted to appear immobile over the sampled range of b-values.",
keywords = "Bayesian probability theory, Brain, Diffusion ansiotropy, MRI, Newborn",
author = "Kroenke, {Christopher (Chris)} and Bretthorst, {G. Larry} and Inder, {Terrie E.} and Neil, {Jeffrey J.}",
year = "2006",
month = "1",
doi = "10.1002/mrm.20728",
language = "English (US)",
volume = "55",
pages = "187--197",
journal = "Magnetic Resonance in Medicine",
issn = "0740-3194",
publisher = "John Wiley and Sons Inc.",
number = "1",

}

TY - JOUR

T1 - Modeling water diffusion anisotropy within fixed newborn primate brain using Bayesian probability theory

AU - Kroenke, Christopher (Chris)

AU - Bretthorst, G. Larry

AU - Inder, Terrie E.

AU - Neil, Jeffrey J.

PY - 2006/1

Y1 - 2006/1

N2 - An active area of research involves optimally modeling brain diffusion MRI data for various applications. In this study Bayesian analysis procedures were used to evaluate three models applied to phase-sensitive diffusion MRI data obtained from formalin-fixed perinatal primate brain tissue: conventional diffusion tensor imaging (DTI), a cumulant expansion, and a family of modified DTI expressions. In the latter two cases the optimum expression was selected from the model family for each voxel in the image. The ability of each model to represent the data was evaluated by comparing the magnitude of the residuals to the thermal noise. Consistent with previous findings from other laboratories, the DTI model poorly represented the experimental data. In contrast, the cumulant expansion and modified DTI expressions were both capable of modeling the data to within the noise using six to eight adjustable parameters per voxel. In these cases the model selection results provided a valuable form of image contrast. The successful modeling procedures differ from the conventional DTI model in that they allow the MRI signal to decay to a positive offset. Intuitively, the positive offset can be thought of as spins that are sufficiently restricted to appear immobile over the sampled range of b-values.

AB - An active area of research involves optimally modeling brain diffusion MRI data for various applications. In this study Bayesian analysis procedures were used to evaluate three models applied to phase-sensitive diffusion MRI data obtained from formalin-fixed perinatal primate brain tissue: conventional diffusion tensor imaging (DTI), a cumulant expansion, and a family of modified DTI expressions. In the latter two cases the optimum expression was selected from the model family for each voxel in the image. The ability of each model to represent the data was evaluated by comparing the magnitude of the residuals to the thermal noise. Consistent with previous findings from other laboratories, the DTI model poorly represented the experimental data. In contrast, the cumulant expansion and modified DTI expressions were both capable of modeling the data to within the noise using six to eight adjustable parameters per voxel. In these cases the model selection results provided a valuable form of image contrast. The successful modeling procedures differ from the conventional DTI model in that they allow the MRI signal to decay to a positive offset. Intuitively, the positive offset can be thought of as spins that are sufficiently restricted to appear immobile over the sampled range of b-values.

KW - Bayesian probability theory

KW - Brain

KW - Diffusion ansiotropy

KW - MRI

KW - Newborn

UR - http://www.scopus.com/inward/record.url?scp=30344465023&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=30344465023&partnerID=8YFLogxK

U2 - 10.1002/mrm.20728

DO - 10.1002/mrm.20728

M3 - Article

C2 - 16342153

AN - SCOPUS:30344465023

VL - 55

SP - 187

EP - 197

JO - Magnetic Resonance in Medicine

JF - Magnetic Resonance in Medicine

SN - 0740-3194

IS - 1

ER -