Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system

Roland Opfer; Winfried Brenner; Ingwer Carlsen; Steffen Renisch; Jörg Sabczynski; Rafael Wiemker

doi:10.1117/12.770356

17 March 2008 Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system

Roland Opfer, Winfried Brenner, Ingwer Carlsen, Steffen Renisch, Jörg Sabczynski, Rafael Wiemker

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 691513 (2008) https://doi.org/10.1117/12.770356
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. However, dealing simultaneously with several PET/CT scans poses a serious workflow problem. It can be a difficult and tedious task to extract response criteria based upon an integrated analysis of PET and CT images and to track these criteria over time. In order to improve the workflow for serial analysis of PET/CT scans we introduce in this paper a fast lesion tracking algorithm. We combine a global multi-resolution rigid registration algorithm with a local block matching and a local region growing algorithm. Whenever the user clicks on a lesion in the base-line PET scan the course of standardized uptake values (SUV) is automatically identified and shown to the user as a graph plot. We have validated our method by a data collection from 7 patients. Each patient underwent two or three PET/CT scans during the course of a cancer therapy. An experienced nuclear medicine physician manually measured the courses of the maximum SUVs for altogether 18 lesions. As a result we obtained that the automatic detection of the corresponding lesions resulted in SUV measurements which are nearly identical to the manually measured SUVs. Between 38 measured maximum SUVs derived from manual and automatic detected lesions we observed a correlation of 0.9994 and a average error of 0.4 SUV units.

Citation Download Citation

Roland Opfer, Winfried Brenner, Ingwer Carlsen, Steffen Renisch, Jörg Sabczynski, and Rafael Wiemker "Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691513 (17 March 2008); https://doi.org/10.1117/12.770356

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