Change search
ReferencesLink to record
Permanent link

Direct link
The 2D Hotelling filter: a quantitativenoise-reducing principal-component filter fordynamic PET data, with applications in patientdose reduction
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.ORCID iD: 0000-0002-3731-3612
PET-center, Department of Radiology, Oncology and Radiation Sciences, Uppsala University, Uppsala, Sweden.
2013 (English)In: BMC Medical Physics, ISSN 1756-6649, Vol. 13, no 1Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
BioMed Central, 2013. Vol. 13, no 1
National Category
Medical Image Processing
Research subject
medicinsk informatik
URN: urn:nbn:se:umu:diva-93669DOI: 10.1186/1756-6649-13-1OAI: diva2:750696
Available from: 2014-09-29 Created: 2014-09-29 Last updated: 2014-10-03Bibliographically approved

Open Access in DiVA

FullText(2713 kB)66 downloads
File information
File name FULLTEXT01.pdfFile size 2713 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links

Publisher's full textfulltext

Search in DiVA

By author/editor
Axelsson, Jan
By organisation
Radiation Physics
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 66 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 110 hits
ReferencesLink to record
Permanent link

Direct link