Optimal diffusion MRI acquisition for fiber orientation density estimation: An analytic approach
Year: 2009
Type: article
Abstract: An important challenge in the design of diffusion MRI experiments is how to optimize statistical efficiency, i.e., the accuracy with which parameters can be estimated from the diffusion data in a given amount of imaging time. In model-based spherical deconvolution analysis, the quantity of interest is the fiber orientation density (FOD). Here, we demonstrate how the spherical harmonics (SH) can be used to form an explicit analytic expression for the efficiency of the minimum variance (maximally efficient) linear unbiased estimator of the FOD. Using this expression, we calculate optimal b-values for maximum FOD estimation efficiency with SH expansion orders of L = 2, 4, 6, and 8 to be approximately b = 1,500, 3,000, 4,600, and 6,200 s/mm(2), respectively. However, the arrangement of diffusion directions and scanner-specific hardware limitations also play a role in determining the realizable efficiency of the FOD estimator that can be achieved in practice. We show how some commonly used methods for selecting diffusion directions are sometimes inefficient, and propose a new method for selecting diffusion directions in MRI based on maximizing the statistical efficiency. We further demonstrate how scanner-specific hardware limitations generally lead to optimal b-values that are slightly lower than the ideal b-values. In summary, the analytic expression for the statistical efficiency of the unbiased FOD estimator provides important insight into the fundamental tradeoff between angular resolution, b-value, and FOD estimation accuracy.
Source: Human Brain Mapping
DOI: 10.1002/hbm.20799
Authors: Nathan S. White, Anders M. Dale
Institution: University of California San Diego
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G104586243, Imaging Studies in Neurotoxicity and Neurodevelopment, Structural & Metabolic Neuroimaging Biomarkers in Early Alzheimer's Disease, Morphometry Biomedical Informatics Research Network, Spatiotemporal Brain Imaging: Microscopic and System Level, Alzheimer's Disease Neuroimaging Initiative, Center for the Study of the Neural Bases of Language and Learning, G80392895
OpenAlex ID: W2079690093
