Low SNR in Diffusion MRI Models

ABSTRACT

Noise is a common issue for all magnetic resonance imaging (MRI) techniques such as diffusion MRI and obviously leads to variability of the estimates in any model describing the data. Increasing spatial resolution in MR experiments further diminishes the signal-to-noise ratio (SNR). However, with low SNR the expected signal deviates from the true value. Common modeling approaches therefore lead to a bias in estimated model parameters. Adjustments require an analysis of the data generating process and a characterization of the resulting distribution of the imaging data. We provide an adequate quasi-likelihood approach that employs these characteristics. We elaborate on the effects of typical data preprocessing and analyze the bias effects related to low SNR for the example of the diffusion tensor model in diffusion MRI. We then demonstrate the relevance of the problem using data from the Human Connectome Project. Supplementary materials for this article are available online.

KEYWORDS:

• Estimation bias
• Data preprocessing
• Quasi-likelihood

Supplementary Materials

• R-package dti, version 1.2-4.1: R-package containing functions to estimate the scale parameter σ of the signal distribution and to compute nonlinear least-square and quasi-likelihood estimates in the diffusion tensor model used in the article. (GNU zipped tar-file)

• File functions.r Additional R-functions used in the simulations (R source file).

• File BiasHCPdesign.R R-script to perform the simulations in Section 4. (R source file)

• File HCP.R R-script to perform the analyses in Section 5. (R source file)

• File README.txt README-file containing information on data availability. (.txt file)

Acknowledgment

The authors thank two reviewers for their helpful suggestions and comments.

Funding

Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. This material was based upon work partially supported by the National Science Foundation under Grant DMS-1127914 to the Statistical and Applied Mathematical Sciences Institute.