A novel framework for repeated measurements in diffusion tensor imaging

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 studies. 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.