Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Evaluating the Effect of Intensity Standardisation on Longitudinal Whole Brain Atrophy Quantification in Brain Magnetic Resonance Imaging

Version 1 : Received: 13 December 2020 / Approved: 15 December 2020 / Online: 15 December 2020 (11:03:11 CET)

A peer-reviewed article of this Preprint also exists.

Carvajal-Camelo, E.E.; Bernal, J.; Oliver, A.; Lladó, X.; Trujillo, M.; Initiative, T.A.D.N. Evaluating the Effect of Intensity Standardisation on Longitudinal Whole Brain Atrophy Quantification in Brain Magnetic Resonance Imaging. Appl. Sci. 2021, 11, 1773. Carvajal-Camelo, E.E.; Bernal, J.; Oliver, A.; Lladó, X.; Trujillo, M.; Initiative, T.A.D.N. Evaluating the Effect of Intensity Standardisation on Longitudinal Whole Brain Atrophy Quantification in Brain Magnetic Resonance Imaging. Appl. Sci. 2021, 11, 1773.

Journal reference: Appl. Sci. 2021, 11, 1773
DOI: 10.3390/app11041773

Abstract

Atrophy quantification is fundamental for understanding brain development and diagnosing and monitoring brain diseases. FSL-SIENA is a well-known fully-automated method that has been widely used in brain magnetic resonance imaging studies. However, intensity variations arising during image acquisition that may compromise evaluation, analysis and even diagnosis. In this work, we study whether intensity standardisation can improve longitudinal atrophy quantification. We considered seven methods comprising z-score, fuzzy c-means, Gaussian mixture model, kernel density, histogram matching, white stripe, and removal of artificial voxel effects by linear regression (RAVEL). We used a total of 330 scans from two publicly-available datasets, ADNI and OASIS. In scan-rescan assessments, that measures robustness to subtle imaging variations, intensity standardisation did not compromise the robustness of FSL-SIENA significantly (p>0.1). In power analysis assessments, that measures the ability to discern between two groups of subjects, three methods led to consistent improvements in both datasets with respect to the original: fuzzy c-means, Gaussian mixture model, and kernel density estimation. Reduction in sample size using these three methods ranged from 17% to 95%. The performance of the other four methods was affected by spatial normalisation, skull stripping errors, presence of periventricular white matter hyperintensities, or tissue proportion variations over time. Our work evinces the relevance of appropriate intensity standardisation in longitudinal cerebral atrophy assessments using FSL-SIENA.

Keywords

Intensity standardisation; FSL-SIENA; longitudinal atrophy quantification; brain magnetic resonance imaging

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