Robust fitting of [11C]-WAY-100635 PET data

Document Type

Article

Publication Date

7-1-2010

Abstract

Fitting of a positron emission tomography (PET) time-activity curve is typically accomplished according to the least squares (LS) criterion, which is optimal for data having Gaussian distributed errors, but not robust in the presence of outliers. Conversely, quantile regression (QR) provides robust estimates not heavily influenced by outliers, sacrificing a little efficiency relative to LS when no outliers are present. Given these considerations, we hypothesized that QR would improve parameter estimate accuracy as measured by reduced intersubject variance in distribution volume (VT) compared with LS in PET modeling. We compare VT values after applying QR with those using LS on 49 controls studied with [11C]-WAY-100635. QR decreases the standard deviation of the VT estimates (relative improvement range: 0.08% to 3.24%), while keeping the within-group average VT values almost unchanged. QR variance reduction results in fewer subjects required to maintain the same statistical power in group analysis without additional hardware and/or image registration to correct head motion. © 2010 ISCBFM All rights reserved.

Identifier

77954244587 (Scopus)

Publication Title

Journal of Cerebral Blood Flow and Metabolism

External Full Text Location

https://doi.org/10.1038/jcbfm.2010.20

ISSN

0271678X

PubMed ID

20179725

First Page

1366

Last Page

1372

Issue

7

Volume

30

Grant

0954796

Fund Ref

National Science Foundation

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