Impact of prior distributions and central tendency measures on Bayesian intravoxel incoherent motion model fitting

• © 2017 International Society for Magnetic Resonance in Medicine.Purpose: Bayesian model fitting has been proposed as a robust alternative for intravoxel incoherent motion (IVIM) model-fitting parameter estimation. However, consensus regarding choice of prior distribution and posterior distribution central tendency measure is needed. The aim of this study was to compare the quality of IVIM parameter estimates produced by different prior distributions and central tendency measures, and to gain knowledge about the effect of these choices. Methods: Three prior distributions (uniform, reciprocal, and lognormal) and two measures of central tendency (mean and mode) found in the literature were studied using simulations and in vivo data from a tumor mouse model. Results: Simulations showed that the uniform and lognormal priors were superior to the reciprocal prior, especially for the parameters D and f and clinically relevant SNR levels. The choice of central tendency measure had less effect on the results, but had some effects on estimation bias. Results based on simulations and in vivo data agreed well, indicating high validity of the simulations. Conclusions: Choice of prior distribution and central tendency measure affects the results of Bayesian IVIM parameter estimates. This must be considered when comparing results from different studies. The best overall quality of IVIM parameter estimates was obtained using the lognormal prior.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)

Nyckelord

Bayesian estimation

Central tendency measure

Intravoxel incoherent motion

Prior distribution

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