TY - JOUR
T1 - Modeling water diffusion anisotropy within fixed newborn primate brain using Bayesian probability theory
AU - Kroenke, Christopher D.
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
U2 - 10.1002/mrm.20728
DO - 10.1002/mrm.20728
M3 - Article
C2 - 16342153
AN - SCOPUS:30344465023
SN - 0740-3194
VL - 55
SP - 187
EP - 197
JO - Magnetic resonance in medicine
JF - Magnetic resonance in medicine
IS - 1
ER -