Model-based crosstalk compensation for simultaneous 99mTc/ 123I dual-isotope brain SPECT imaging

Yong Du, Benjamin M.W. Tsui, Eric C. Frey

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

In this work, we developed a model-based method to estimate and compensate for the crosstalk contamination in simultaneous I123 and Tc 99m dual isotope brain single photo emission computed tomography imaging. The model-based crosstalk compensation (MBCC) includes detailed modeling of photon interactions inside both the object and the detector system. In the method, scatter in the object is modeled using the effective source scatter estimation technique, including contributions from all the photon emissions. The effects of the collimator-detector response, including the penetration and scatter components due to high-energy I123 photons, are modeled using pre-calculated tables of Monte Carlo simulated point-source response functions obtained from sources in air at various distances from the face of the collimator. The model-based crosstalk estimation method was combined with iterative reconstruction based compensation to reduce contamination due to crosstalk. The MBCC method was evaluated using Monte Carlo simulated and physical phantom experimentally acquired simultaneous dual-isotope data. Results showed that, for both experimental and simulation studies, the model-based method provided crosstalk estimates that were in good agreement with the true crosstalk. Compensation using MBCC improved image contrast and removed the artifacts for both Monte Carlo simulated and experimentally acquired data. The results were in good agreement with images acquired without any crosstalk contamination.

Original languageEnglish (US)
Pages (from-to)3530-3543
Number of pages14
JournalMedical physics
Volume34
Issue number9
DOIs
StatePublished - 2007

Keywords

  • Brain SPECT imaging
  • Crosstalk compensation
  • Dual isotope

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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