What question did this study set out to answer?

The aim is to compare whole-brain histogram analysis and the Top 20% Gray and White Matter Ratio with the Centiloid scale for diagnosing amyloid positivity.

May 20, 2026Open Access

Whole-brain histogram analysis and top 20% gray and white matter ratio of amyloid positron emission tomography: A comparison with the centiloid scale

Key Points

The aim is to compare whole-brain histogram analysis and the Top 20% Gray and White Matter Ratio with the Centiloid scale for diagnosing amyloid positivity.
Analyzed MRI and amyloid PET images of 262 participants.
Used multiple brain extraction methods (HD-BET, FSL, SPM) for whole-brain histogram analysis.
Evaluated correlations with the Centiloid scale and dementia severity scores.
Skewness from HD-BET showed a strong negative correlation with Centiloid scale (R = -0.9043).
The Top 20% Gray and White Matter Ratio strongly correlated with Centiloid scale (R = 0.8332).
Centiloid scale demonstrated the highest AUC (0.9959) for distinguishing Aβ-negative from Aβ-positive.

Abstract

Abstract Objective To compare whole-brain histogram analysis (WBHA) of amyloid-β (Aβ) positron emission tomography (PET) using several magnetic resonance imaging (MRI)-based brain extraction (brain extraction tool BET) methods, to evaluate a novel quantitative analysis method, namely the Top 20% Gray and White Matter Ratio (GW-ratio), to determine appropriate thresholds, and to evaluate diagnostic performance in comparison with the Centiloid scale (CL). Methods We analyzed structural MRI, 18 F-flutemetamol amyloid PET images, and dementia severity scores (Global Clinical Dementia Rating G-CDR and Mini-Mental State Examination MMSE) of 262 participants. For WBHA, BET was performed using structural MRI and three different BET software programs, namely High-Definition-BET (HD-BET), FMRIB Software Library (FSL), and Statistical Parametric Mapping (SPM). Skewness and mode-to-mean ratio (MMR) were measured using brain-extracted PET. The Top 20% Map was generated from SPM BET images, separated into gray and white matter voxels, and their ratio was calculated as the GW-ratio. The CL value was computed using structural MRI. Aβ-negativity or positivity was visually determined. Results Skewness from three BET methods was strongly negatively correlated with CL. Skewness (HD-BET) showed the strongest correlation (R = -0.9043). Moreover, the GW-ratio strongly correlated with CL (R = 0.8332), whereas MMR, particularly MMR (FSL) (R = 0.2112), showed poor correlation. All indicators significantly distinguished Aβ-negative from Aβ-positive visuals. CL had the highest area under the curve (AUC, 0.9959), followed by skewness (HD-BET) (0.9927), Top 20% GW-ratio (0.9872), and skewness (SPM) (0.9779), with no statistical difference between CL and skewness (HD-BET) ( P = 0.5763). Based on mean and SD values from 118 cognitively unimpaired (G-CDR = 0, MMSE ≥ 28) and visually Aβ-negative participants, 95% cut-off limits for Aβ-negative individuals without dementia symptoms were: CL ≤ 12.9, skewness (HD-BET) ≥ 0.1769, MMR (HD-BET) ≤ 0.9372, skewness (FSL) ≥ 0.1819, MMR (FSL) ≤ 1.9132, skewness (SPM) ≥ -0.0382, MMR (SPM) ≤ 1.1274, and GW-ratio ≤ 0.1079. Conclusion WBHA using MRI-based BET and GW-ratio showed strong correlations with CL and demonstrated a diagnostic performance comparable to that of CL.

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