A Novel Framework for repeated measurements in diffusion tensor imaging
Paper in proceedings, 2016
In the context of diffusion tensor imaging (DTI), the utility of making repeated measurements in each diffusion sensitizing direction has been the subject of numerous stud-
ies. One can estimate the true signal value using either the raw complex-valued data or the real-valued magnitude
signal. While conventional methods focus on the former strategy, this paper proposes a new framework for acquiring/processing repeated measurements based on the latter strategy. The aim is to enhance the DTI processing pipeline by adding a diffusion signal estimator (DSE). This permits us to exploit the knowledge of the noise distribution to estimate the true signal value in each direction. An extensive study of the proposed framework, including theoretical analysis, experiments with synthetic data, performance evaluation and comparisons is presented.
Our results show that the precision of estimated diffusion
parameters is dependent on the number of available samples
and the manner in which the DSE accounts for noise. The
proposed framework improves the precision in estimation
of diffusion parameters given a sufficient number of unique
measurements. This encourages future work with rich real
datasets and downstream applications.
Diffusion signal estimation