Numerical Uncertainty in Linear Registration: An Experimental Study

TL;DR

This study evaluates the numerical stability of linear registration tools in MRI preprocessing using Monte-Carlo Arithmetic, revealing tool-specific variability and potential for automated quality control, with implications for clinical and research applications.

Contribution

First comprehensive experimental assessment of numerical uncertainty in linear MRI registration across major software packages using MCA simulations.

Findings

SPM showed highest stability among tools evaluated.

ANTs exhibited sensitivity leading to registration failures.

Numerical stability was similar between healthy and clinical cohorts.

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.