Computer-assisted quantification of lung tumors in respiratory gated PET/CT images: Phantom study

Jiali Wang, Misael Del Valle, Mohammed Goryawala, Juan M. Franquiz, Anthony J. McGoron

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


A computer-aided method was developed to automatically localize tumors in lung PET images of discrete bins within the breathing cycle, followed by an algorithm that registers all the information of a complete respiratory cycle into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body, and Optical Flow registration were compared as well as two registration schemes: Direct scheme and Successive scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level, and computation time. After motion correction, the best compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become faster and more precise.

Original languageEnglish (US)
Pages (from-to)49-58
Number of pages10
JournalMedical and Biological Engineering and Computing
Issue number1
StatePublished - Jan 2010
Externally publishedYes


  • Lung cancer
  • Medical imaging
  • Nuclear medicine
  • Registration
  • Respiratory gating

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications


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