SHORE‐based detection and imputation of dropout in diffusion MRI

Alexandra Koch, Andrei Zhukov, Tony Stöcker, Samuel Gröschel, and Thomas Schultz
In: Magnetic Resonance in Medicine (2019)
 

Abstract

Purpose: In diffusion MRI, dropout refers to a strong attenuation of the measured signal that is caused by bulk motion during the diffusion encoding. When left uncorrected, dropout will be erroneously interpreted as high diffusivity in the affected direction. We present a method to automatically detect dropout, and to replace the affected measurements with imputed values.

Methods: Signal dropout is detected by deriving an outlier score from a simple harmonic oscillator‐based reconstruction and estimation (SHORE) fit of all measurements. The outlier score is defined to detect measurements that are substantially lower than predicted by SHORE in a relative sense, while being less sensitive to measurement noise in cases of weak baseline signal. A second SHORE fit is based on detected inliers only, and its predictions are used to replace outliers.

Results: Our method is shown to reliably detect and accurately impute dropout in simulated data, and to achieve plausible results in corrupted in vivo dMRI measurements. Computational effort is much lower than with previously proposed alternatives.

Conclusions: Deriving a suitable outlier score from SHORE results in a fast and accurate method for detection and imputation of dropout in diffusion MRI. It requires measurements with multiple b values (such as multi‐shell or DSI), but is independent from the models used for analysis (such as DKI, NODDI, deconvolution, etc.).

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Bibtex

@ARTICLE{koch:MRM2019,
    author = {Koch, Alexandra and Zhukov, Andrei and St{\"o}cker, Tony and Gr{\"o}schel, Samuel and Schultz, Thomas},
     title = {SHORE‐based detection and imputation of dropout in diffusion MRI},
   journal = {Magnetic Resonance in Medicine},
      year = {2019},
      note = {Early View},
  abstract = {Purpose:
              In diffusion MRI, dropout refers to a strong attenuation of the measured signal that is caused by
              bulk motion during the diffusion encoding. When left uncorrected, dropout will be erroneously
              interpreted as high diffusivity in the affected direction. We present a method to automatically
              detect dropout, and to replace the affected measurements with imputed values.
              
              Methods:
              Signal dropout is detected by deriving an outlier score from a simple harmonic oscillator‐based
              reconstruction and estimation (SHORE) fit of all measurements. The outlier score is defined to
              detect measurements that are substantially lower than predicted by SHORE in a relative sense, while
              being less sensitive to measurement noise in cases of weak baseline signal. A second SHORE fit is
              based on detected inliers only, and its predictions are used to replace outliers.
              
              Results:
              Our method is shown to reliably detect and accurately impute dropout in simulated data, and to
              achieve plausible results in corrupted in vivo dMRI measurements. Computational effort is much lower
              than with previously proposed alternatives.
              
              Conclusions:
              Deriving a suitable outlier score from SHORE results in a fast and accurate method for detection and
              imputation of dropout in diffusion MRI. It requires measurements with multiple b values (such as
              multi‐shell or DSI), but is independent from the models used for analysis (such as DKI, NODDI,
              deconvolution, etc.).},
       doi = {10.1002/mrm.27893}
}