Scatter correction for 3D PET using beam stoppers combined with dual-energy window acquisition: A feasibility study

Jay Wu, Keh Shih Chuang, Ching Han Hsu, Meei Ling Jan, Ing Ming Hwang, Tzong Jer Chen

Research output: Contribution to journalJournal Article peer-review

7 Scopus citations

Abstract

Fully three-dimensional (3D) positron emission tomography (PET) can achieve high sensitivity of coincidence events, but the absence of inter-slice septa inevitably leads to increased scattered events. The scattered events can represent as much as 50% of the total detected events. In this research, we proposed a scatter correction method for 3D PET based on beam stoppers and dual-energy window acquisition. The beam stoppers were placed surrounding the object to attenuate primary beams. The scatter fractions were directly estimated at those blocked lines of response and then the entire scatter fraction distribution was recovered using the dual-energy window ratio as reference. The performance was evaluated by using Monte Carlo simulations of various digital phantoms. For the Utah phantom study, the proposed method accurately estimated the scatter fraction distribution, and improved image contrast and quantification based on four different quality indices as performance measures. For the non-homogeneous Zubal phantom, the simulated results also demonstrated that the proposed method achieved a better restoration of image contrast than the dual-energy window method. We conclude that the proposed scatter correction method could effectively suppress various kinds of scattered events, including multiple scatter and scatter from outside the field of view.

Original languageEnglish
Pages (from-to)4593-4607
Number of pages15
JournalPhysics in Medicine and Biology
Volume50
Issue number19
DOIs
StatePublished - 07 10 2005
Externally publishedYes

Fingerprint

Dive into the research topics of 'Scatter correction for 3D PET using beam stoppers combined with dual-energy window acquisition: A feasibility study'. Together they form a unique fingerprint.

Cite this