Abstract:
:The ability to evaluate empirical diffusion MRI acquisitions for quality and to correct the resulting imaging metrics allows for improved inference and increased replicability. Previous work has shown promise for estimation of bias and variance of generalized fractional anisotropy (GFA) but comes at the price of computational complexity. This paper aims to provide methods for estimating GFA, bias of GFA and standard deviation of GFA quickly and accurately. In order to provide a method for bias and variance estimation that can return results faster than the previously studied statistical techniques, three deep, fully-connected neural networks are developed for GFA, bias of GFA, and standard deviation of GFA. The results of these networks are compared to the observed values of the metrics as well as those fit from the statistical techniques (i.e. Simulation Extrapolation (SIMEX) for bias estimation and wild bootstrap for variance estimation). Our GFA network provides predictions that are closer to the true GFA values than a Q-ball fit of the observed data (root-mean-square error (RMSE) 0.0077 vs 0.0082, p < .001). The bias network also shows statistically significant improvement in comparison to the SIMEX-estimated error of GFA (RMSE 0.0071 vs. 0.01, p < .001).
journal_name
Magn Reson Imagingjournal_title
Magnetic resonance imagingauthors
Hainline AE,Nath V,Parvathaneni P,Schilling KG,Blaber JA,Anderson AW,Kang H,Landman BAdoi
10.1016/j.mri.2019.03.021subject
Has Abstractpub_date
2019-06-01 00:00:00pages
130-136eissn
0730-725Xissn
1873-5894pii
S0730-725X(18)30436-3journal_volume
59pub_type
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