The 2D Hotelling filter : a quantitativenoise-reducing principal-component filter fordynamic PET data, with applications in patientdose reduction

摘要

Background: In this paper we apply the principal-component analysis filter (Hotelling filter) to reduce noise fromdynamic positron-emission tomography (PET) patient data, for a number of different radio-tracer molecules. Wefurthermore show how preprocessing images with this filter improves parametric images created from suchdynamic sequence.We use zero-mean unit variance normalization, prior to performing a Hotelling filter on the slices of a dynamictime-series. The Scree-plot technique was used to determine which principal components to be rejected in thefilter process. This filter was applied to [11C]-acetate on heart and head-neck tumors, [18F]-FDG on liver tumors andbrain, and [11C]-Raclopride on brain. Simulations of blood and tissue regions with noise properties matched to realPET data, was used to analyze how quantitation and resolution is affected by the Hotelling filter. Summing varyingparts of a 90-frame [18F]-FDG brain scan, we created 9-frame dynamic scans with image statistics comparable to 20MBq, 60 MBq and 200 MBq injected activity. Hotelling filter performed on slices (2D) and on volumes (3D) werecompared.Results: The 2D Hotelling filter reduces noise in the tissue uptake drastically, so that it becomes simple to manuallypick out regions-of-interest from noisy data. 2D Hotelling filter introduces less bias than 3D Hotelling filter in focalRaclopride uptake. Simulations show that the Hotelling filter is sensitive to typical blood peak in PET prior to tissueuptake have commenced, introducing a negative bias in early tissue uptake. Quantitation on real dynamic data isreliable. Two examples clearly show that pre-filtering the dynamic sequence with the Hotelling filter prior toPatlak-slope calculations gives clearly improved parametric image quality. We also show that a dramatic dosereduction can be achieved for Patlak slope images without changing image quality or quantitation.Conclusions: The 2D Hotelling-filtering of dynamic PET data is a computer-efficient method that gives visuallyimproved differentiation of different tissues, which we have observed improve manual or automated regionof-interest delineation of dynamic data. Parametric Patlak images on Hotelling-filtered data display improved clarity,compared to non-filtered Patlak slope images without measurable loss of quantitation, and allow a dramaticdecrease in patient injected dose.