MAGNETIC RESONANCE WATER SELF-DIFFUSION TENSOR ENCODING OPTIMIZATION METHODS FOR FULL BRAIN ACQUISITION

Abstract

Water diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive and sensitive modality that is becoming increasingly popular in diagnostic radiology. DT-MRI provides in vivo directional information about the organization and microdynamics of deep brain tissue that is not available by other MRI relaxationbased methods. The DT-MRI experiment involves a host of imaging and diffusion parameters that influence the efficiency (signal-to-noise ratio per unit time), accuracy, and specificity of the information sought. These parameters may include typical imaging parameters such as TE, TR, slice thickness, sampling rate, etc. The DTI relevant parameter space includes pulse duration, separation, direction, number of directions (Ne), order, sign and strength of the diffusion encoding gradient pulses. The goal of this work is to present and compare different tensor encoding strategies used to obtain the DT-MRI information for the whole brain. In this paper an evaluation of tensor encoding advantage is presented using a multi-dimensional non-parametric Bootstrap resampling method. This work also explores the relationship between different tensor encoding schemes using the analytical encoding approach. This work shows that the minimum energy optimization approach can produce uniformly distributed tensor encoding that are comparable to the icosahedral sets. The minimum condition encoding sets are not uniformly distributed and are shown to be suboptimal and related to a commonly used heuristic tensor encoding set. This work shows that the icosahedral set is the only uniformly distributed set with Ne = 6. At equal imaging time, the Bootstrap experiments show that optimal tensor encoding sets can have 6 < Ne < 24.