Numerical Uncertainty in Linear Registration: An Experimental Study

Niusha Mirhakimi, Yohan Chatelain, Jean-baptiste Poline, Tristan Glatard

Keywords: Linear Registration · MCA · Numerical Uncertainty.

Abstract:

While linear registration is a critical step in MRI preprocessing
pipelines, its numerical uncertainty is understudied. Using Monte-
Carlo Arithmetic (MCA) simulations, we assessed the most commonly
used linear registration tools within major software packages—SPM,
FSL, and ANTs—across multiple image similarity measures, two brain
templates, and both healthy control (HC, n=50) and Parkinson’s Disease
(PD, n=50) cohorts. Our findings highlight the influence of linear
registration tools and similarity measures on numerical stability. Among
the evaluated tools and with default similarity measures, SPM exhibited
the highest stability. FSL and ANTs showed greater and similar
ranges of variability, with ANTs demonstrating particular sensitivity to
numerical perturbations that occasionally led to registration failure. Furthermore,
no significant differences were observed between healthy and
PD cohorts, suggesting that numerical stability analyses obtained with
healthy subjects may generalise to clinical populations. Finally, we also
demonstrated how numerical uncertainty measures may support automated
quality control (QC) of linear registration results. Overall, our
experimental results characterize the numerical stability of linear registration
experimentally and can serve as a basis for future uncertainty
analyses.

Submission Number: 6