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Purpose <p>This study aimed to develop and evaluate an in-house software tool, <i>Amyloid PET Quantification</i> (AmPQ), for automatic Centiloid calculation. AmPQ supports both MR-based and MR-free adaptive PET template-based spatial normalization.</p> Methods <p>The Centiloid quantification procedure was developed according to the standard GAAIN pipeline, with two-tier validation involving PiB and three [<sup>18</sup>F]-labeled amyloid PET tracers: Florbetapir (FBP), Flutemetamol (FMM), and Florbetaben (FBB). Additional independent validation was performed using three external cohorts: T-ADNI (<i>n</i> = 92), ADNI (<i>n</i> = 50), and AIBL (<i>n</i> = 50). Simple linear regression was used for quality control (QC) and to assess the performance of the MR-free spatial normalization.</p> Results <p>For level-1 replication, both MR-based (slope = 1.001; intercept = 0.0477; R² = 0.9994) and MR-free (slope = 0.9890; intercept = 0.6073; R² = 0.9890) approaches satisfied the QC criteria. In level-2 calibration, all tracers exceeded the R² threshold of 0.70: FBP (MR-based = 0.895; MR-free = 0.868), FMM (MR-based = 0.960; MR-free = 0.947), and FBB (MR-based = 0.953; MR-free = 0.941). Centiloid values derived from MR-free and MR-based methods were highly correlated (R² &gt;0.98 in GAAIN datasets and R² &gt;0.96 in all external cohorts).</p> Conclusion <p>AmPQ demonstrates robust replicability and high concordance with the standard Centiloid pipeline. The high correlation between MR-free and MR-based spatial normalization supports the feasibility of MR-free quantification in large-scale or resource-limited settings.</p>

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The Development of an Automatic Amyloid PET Quantification (AmPQ) Software with MR-based and MR-free Spatial Normalization

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Shao-Yi Huang,
  • Kun-Ju Lin,
  • Zong-Jhe Lyu,
  • Ing-Tsung Hsiao

摘要

Purpose

This study aimed to develop and evaluate an in-house software tool, Amyloid PET Quantification (AmPQ), for automatic Centiloid calculation. AmPQ supports both MR-based and MR-free adaptive PET template-based spatial normalization.

Methods

The Centiloid quantification procedure was developed according to the standard GAAIN pipeline, with two-tier validation involving PiB and three [18F]-labeled amyloid PET tracers: Florbetapir (FBP), Flutemetamol (FMM), and Florbetaben (FBB). Additional independent validation was performed using three external cohorts: T-ADNI (n = 92), ADNI (n = 50), and AIBL (n = 50). Simple linear regression was used for quality control (QC) and to assess the performance of the MR-free spatial normalization.

Results

For level-1 replication, both MR-based (slope = 1.001; intercept = 0.0477; R² = 0.9994) and MR-free (slope = 0.9890; intercept = 0.6073; R² = 0.9890) approaches satisfied the QC criteria. In level-2 calibration, all tracers exceeded the R² threshold of 0.70: FBP (MR-based = 0.895; MR-free = 0.868), FMM (MR-based = 0.960; MR-free = 0.947), and FBB (MR-based = 0.953; MR-free = 0.941). Centiloid values derived from MR-free and MR-based methods were highly correlated (R² >0.98 in GAAIN datasets and R² >0.96 in all external cohorts).

Conclusion

AmPQ demonstrates robust replicability and high concordance with the standard Centiloid pipeline. The high correlation between MR-free and MR-based spatial normalization supports the feasibility of MR-free quantification in large-scale or resource-limited settings.