Abstract
The presence of Rician noise in magnetic resonance imaging (MRI) introduces systematic errors in diffusion tensor imaging (DTI) measurements. This paper evaluates gradient direction schemes and tensor estimation routines to determine how to achieve the maximum accuracy and precision of tensor derived measures for a fixed amount of scan time. We present Monte Carlo simulations that quantify the effect of noise on diffusion measurements and validate these simulation results against appropriate in-vivo images. The predicted values of the systematic and random error caused by imaging noise are essential both for interpreting the results of statistical analysis and for selecting optimal imaging protocols given scan time limitations.
This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through Grant U54 EB005149. The authors acknowledge support from the NIMH Silvio Conte Center for Neuroscience of Mental Disorders MH064065 as well as the National Alliance for Autism Research (NAAR) and the Blowitz-Ridgeway Foundation.
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Goodlett, C., Fletcher, P.T., Lin, W., Gerig, G. (2007). Quantification of Measurement Error in DTI: Theoretical Predictions and Validation. In: Ayache, N., Ourselin, S., Maeder, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75757-3_2
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DOI: https://doi.org/10.1007/978-3-540-75757-3_2
Publisher Name: Springer, Berlin, Heidelberg
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