The 2D Hotelling filter - a quantitative noise-reducing principal-component filter for dynamic PET data, with applications in patient dose reduction.

ABSTRACT:

Background

In this paper we apply the principal-component analysis filter (Hotelling filter) to reduce noise from dynamic positron-emission tomography (PET) patient data, for a number of different radio-tracer molecules. We furthermore show how preprocessing images with this filter improves parametric images created from such dynamic sequence.We use zero-mean unit variance normalization, prior to performing a Hotelling filter on the slices of a dynamic time-series. The Scree-plot technique was used to determine which principal components to be rejected in the filter process. This filter was applied to [11C]-acetate on heart and head-neck tumors, [18F]-FDG on liver tumors and brain, and [11C]-Raclopride on brain. Simulations of blood and tissue regions with noise properties matched

SUBMITTER: Axelsson J 

PROVIDER: S-EPMC3636030 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature