Cryptoexplosion structures caused from without or from within the earth? ("astroblemes" or "geoblemes?")

Abstract

<h3>ABSTRACT</h3> <h3>Purpose</h3> The Centiloid scale provides a systematic means of harmonising amyloid-β PET measures across different acquisition and processing methodologies. This work explores the Centiloid transformation of [¹⁸F]florbetapir PET data acquired on a combined PET/MR scanner and processed with methods that differ from the standard Centiloid pipeline. <h3>Methods</h3> The Standard PiB and Florbetapir Calibration datasets were processed using a standardised uptake value ratio (SUVR) pipeline with MRI parcellations from the Geodesic Information Flow (GIF) algorithm in native PET space. We generated SUVRs using whole cerebellum (GIF_WC_SUVR) and eroded white matter (GIF_WM_SUVR) reference regions, with and without partial volume correction (PVC). Linear regression was used to calibrate these processing pipelines to the standard Centiloid approach. We then applied the resulting transformation to 432 florbetapir scans from the Insight 46 study of mostly cognitively normal individuals aged ∼70 years, and defined Centiloid cutpoints for amyloid-β positivity using Gaussian-mixture modelling. <h3>Results</h3> GIF-based SUVR processing pipelines were suitable for conversion according to Centiloid criteria. For GIF_WC_SUVR, cutpoints translated to 14.2 Centiloids, or 11.8 with PVC. There was a differential relationship between florbetapir uptake in WM and WC regions in Florbetapir Calibration and Insight 46 datasets, causing implausibly low Centiloid values for GIF_WM_SUVR. Linear adjustment to account for this difference resulted in Centiloid cutpoints of 18.1 for GIF_WM_SUVR (17.0 with PVC). <h3>Conclusion</h3> Our results show florbetapir SUVRs acquired on PET/MR scanners can be reliably converted to Centiloids. Acquisition or biological factors can have large effects on Centiloid values from different datasets, we propose a correction to account for these effects.