Computer-assisted quantification of lung tumors in respiratory gated PET/CT images: phantom study.
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.
Wang
Jiali
J
Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA. Jiali.Wang@fiu.edu
del Valle
Misael
M
Goryawala
Mohammed
M
Franquiz
Juan M
JM
McGoron
Anthony J
AJ
D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods; D015656:Respiratory Mechanics / Q000502:physiology; D014057:Tomography, X-Ray Computed / Q000379:methods