Allison E Hainline1, Vishwesh Nath2, Prasanna Parvathaneni3, Justin A Blaber3, Kurt G Schilling4, Adam W Anderson4,5, Hakmook Kang1,6, Bennett A Landman2,3,4,5,6. 1. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 2. Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA. 3. Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA. 4. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA. 5. Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 6. Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Abstract
PURPOSE: The bias and variance of high angular resolution diffusion imaging methods have not been thoroughly explored in the literature and may benefit from the simulation extrapolation (SIMEX) and bootstrap techniques to estimate bias and variance of high angular resolution diffusion imaging metrics. METHODS: The SIMEX approach is well established in the statistics literature and uses simulation of increasingly noisy data to extrapolate back to a hypothetical case with no noise. The bias of calculated metrics can then be computed by subtracting the SIMEX estimate from the original pointwise measurement. The SIMEX technique has been studied in the context of diffusion imaging to accurately capture the bias in fractional anisotropy measurements in DTI. Herein, we extend the application of SIMEX and bootstrap approaches to characterize bias and variance in metrics obtained from a Q-ball imaging reconstruction of high angular resolution diffusion imaging data. RESULTS: The results demonstrate that SIMEX and bootstrap approaches provide consistent estimates of the bias and variance of generalized fractional anisotropy, respectively. The RMSE for the generalized fractional anisotropy estimates shows a 7% decrease in white matter and an 8% decrease in gray matter when compared with the observed generalized fractional anisotropy estimates. On average, the bootstrap technique results in SD estimates that are approximately 97% of the true variation in white matter, and 86% in gray matter. CONCLUSION: Both SIMEX and bootstrap methods are flexible, estimate population characteristics based on single scans, and may be extended for bias and variance estimation on a variety of high angular resolution diffusion imaging metrics.
PURPOSE: The bias and variance of high angular resolution diffusion imaging methods have not been thoroughly explored in the literature and may benefit from the simulation extrapolation (SIMEX) and bootstrap techniques to estimate bias and variance of high angular resolution diffusion imaging metrics. METHODS: The SIMEX approach is well established in the statistics literature and uses simulation of increasingly noisy data to extrapolate back to a hypothetical case with no noise. The bias of calculated metrics can then be computed by subtracting the SIMEX estimate from the original pointwise measurement. The SIMEX technique has been studied in the context of diffusion imaging to accurately capture the bias in fractional anisotropy measurements in DTI. Herein, we extend the application of SIMEX and bootstrap approaches to characterize bias and variance in metrics obtained from a Q-ball imaging reconstruction of high angular resolution diffusion imaging data. RESULTS: The results demonstrate that SIMEX and bootstrap approaches provide consistent estimates of the bias and variance of generalized fractional anisotropy, respectively. The RMSE for the generalized fractional anisotropy estimates shows a 7% decrease in white matter and an 8% decrease in gray matter when compared with the observed generalized fractional anisotropy estimates. On average, the bootstrap technique results in SD estimates that are approximately 97% of the true variation in white matter, and 86% in gray matter. CONCLUSION: Both SIMEX and bootstrap methods are flexible, estimate population characteristics based on single scans, and may be extended for bias and variance estimation on a variety of high angular resolution diffusion imaging metrics.
Authors: Jonathan A D Farrell; Bennett A Landman; Craig K Jones; Seth A Smith; Jerry L Prince; Peter C M van Zijl; Susumu Mori Journal: J Magn Reson Imaging Date: 2007-09 Impact factor: 4.813
Authors: Allison E Hainline; Vishwesh Nath; Prasanna Parvathaneni; Kurt G Schilling; Justin A Blaber; Adam W Anderson; Hakmook Kang; Bennett A Landman Journal: Magn Reson Imaging Date: 2019-03-26 Impact factor: 2.546