An information theoretic approach characterizing diffusion anisotropy in diffusion-weighted magnetic resonance images.
We propose an alternative approach that does not rely on tensor models for characterizing diffusion anisotropy from diffusion-weighted magnetic resonance images. Information content inherent in the diffusion attenuation values are the only measures needed for our characterization. We explore the information content inherent in these values. We calculate Shannon's entropy on the diffusion attenuation values measured across the applied diffusion-sensitizing gradient directions. This method is evaluated with data generated with different diffusion gradient encoding schemes demonstrating the validity of our approach and its potential use to better differentiate between brain tissue types over tensor-based measures.
Metwalli
Nader S
NS
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. nader.metwalli@bme.gatech.edu
LaConte
Stephen M
SM
Hu
Xiaoping P
XP
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000166:cytology*; D002555:Cerebrospinal Fluid / Q000166:cytology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007257:Information Theory; D008297:Male; D009413:Nerve Fibers, Myelinated / Q000000981:diagnostic imaging*; D009474:Neurons / Q000166:cytology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography