Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies.

ABSTRACT:

Background

Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scanners. In this work, we aim to reduce this variability using a different analysis technique applied to the existing calibration data.

Method

All PET images from the Centiloid calibration dataset, along with 3762 PET images from the AIBL study were analysed using the recommended SPM pipeline. The PET images were SUVR normalised using the whole cerebellum. All SUVR normalised PiB images from the calibration dataset were decomposed using non-negative matrix factorisation (NMF). The NMF co

SUBMITTER: Bourgeat P 

PROVIDER: S-EPMC8049633 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature