: Diffusionmagnetic resonance imaging(MRI) in combination with functional MRI promises a whole new vista for scientists to investigate noninvasively the structural and functional connectivity of the human brain—the human connectome, which had heretofore been out of reach. As with other imaging modalities, diffusionMRI data are inherently noisy and its acquisition time-consuming. Further, a faithful representation of the human connectome that can serve as a predictive model requires a robust and accurate data-analytic pipeline. The focus of this paper is on one of the key segments of this pipeline—in particular, the development of a sparse and optimal acquisition (SOA) design for diffusionMRI multiple-shell acquisition and beyond.Methods
: The authors propose a novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusionMRI and a novel and effective semistochastic and moderately greedy combinatorial search strategy with simulated annealing to locate the optimum design or configuration. The goal of the optimality criteria is threefold: first, to maximize uniformity of the diffusion measurements in each shell, which is equivalent to maximal incoherence in angular measurements; second, to maximize coverage of the diffusion measurements around each radial line to achieve maximal incoherence in radial measurements for multiple-shell acquisition; and finally, to ensure maximum uniformity of diffusion measurement directions in the limiting case when all the shells are coincidental as in the case of a single-shell acquisition. The approach taken in evaluating the stability of various acquisition designs is based on the condition number and the A-optimal measure of the design matrix.Results
: Even though the number of distinct configurations for a given set of diffusion gradient directions is very large in general—e.g., in the order of 10232 for a set of 144 diffusion gradient directions, the proposed search strategy was found to be effective in finding the optimum configuration. It was found that the square design is the most robust (i.e., with stable condition numbers and A-optimal measures under varying experimental conditions) among many other possible designs of the same sample size. Under the same performance evaluation, the square design was found to be more robust than the widely used sampling schemes similar to that of 3D radial MRI and of diffusion spectrum imaging (DSI).Conclusions
: A novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusionMRI and an effective search strategy for finding the best configuration have been developed. The results are very promising, interesting, and practical for diffusionMRI acquisitions.
C.G.K. dedicates this work to Pauline Toh and Eng Khoon Leong. Software related to this work will be made available through the following URL: http://sites.google.com/site/hispeedpackets. This work was supported in part by the National Institutes of Health Grant No. IRCMH090912-01. E.Ö. was supported by the Department of Defense in the Center for Neuroscience and Regenerative Medicine (CNRM) and the Henry M. Jackson Foundation (HJF).
II.A. Semistochastic and moderately greedy combinatorial search strategy
II.A.1. Semistochastic and moderately greedy combinatorial search algorithm
II.B. Optimal ordering strategy
III. METHOD OF EVALUATION OF ACQUISITION DESIGNS
IV.A. Illustrative example
IV.B. Evaluation of acquisition designs
- Magnetic resonance imaging
- Medical imaging
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