PCA filter for szintigraphic imaging

Werner Backfrieder, Martin Forster, Wilma Maschek

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


Quality of image data in nuclear medicine diagnostics is mainly limited by radioactive dose and acquisition time, both yielding low total counts. There are studies with a maximum pixel count of 10 in a frame, thus signal-to-noise-ratio (SNR) is very poor. A novel data driven method was developed to enhance SNR but preserving underlying image features. In contrast to traditional filtering methods calculation of smoothed image data is not based to a region surrounding the pixel, but image structures are separated from noise using a simple statistical model. The signal is reconstructed from principal components images (PCI) and the amount of noise is assessed by chi2 statistics. With this method even fine structures are preserved. The potential of the novel technique is demonstrated with in vivo data from perfusion and Parkinson studies. After removal of heavy noise, enhanced image are subject to sophisticated image processing, as automated inter-modality image registration.

Original languageEnglish
Title of host publicationProceedings of the 21st European Modelling and Simulation Symposium
Publication statusPublished - 2009
Event21st European Modeling and Simulation Symposium, EMSS 2009 - Puerto de la Cruz, Spain
Duration: 23 Sept 200925 Sept 2009


Conference21st European Modeling and Simulation Symposium, EMSS 2009
CityPuerto de la Cruz


  • Data driven filter
  • Image registration
  • PCA


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