Purpose: Accurate detection of cancer lesions in positron emission tomography (PET) is fundamental to achieving favorable clinical outcomes. Therefore, image reconstruction, processing, visualization, and interpretation techniques must be optimized for this task. The objective of this work was to (1) develop and validate an efficient method to generate well-characterized synthetic lesions in real patient data and (2) to apply these lesions in a human perception experiment to establish baseline measurements of the limits of lesion detection as a function of lesion size and contrast using current imaging technologies.

Approach: A fully integrated software package for synthesizing well-characterized lesions in real patient PET was developed using a vendor provided PET image reconstruction toolbox (REGRECON5, General Electric Healthcare, Waukesha, Wisconsin). Lesion characteristics were validated experimentally for geometric accuracy, activity accuracy, and absence of artifacts. The Lesion Synthesis Toolbox was used to generate a library of 133 synthetic lesions of varying sizes (n  =  7) and contrast levels (n  =  19) in manually defined locations in the livers of 37 patient studies. A lesion-localization perception study was performed with seven observers to determine the limits of detection with regard to lesion size and contrast using our web-based perception study tool.

Results: The Lesion Synthesis Toolbox was validated for accurate lesion placement and size. Lesion intensities were deemed accurate with slightly elevated activities (5% at 2:1 lesion-to-background contrast) in small lesions (Ø  =  15  mm spheres), and no bias in large lesions (Ø  =  22.5  mm). Bed-stitching artifacts were not observed, and lesion attenuation correction bias was small (−1.6  ±  1.2  %  ). The 133 liver lesions were synthesized in ∼50  h, and readers were able to complete the perception study of these lesions in 12  ±  3  min with consistent limits of detection amongst all readers.

Conclusions: Our open-source utilities can be employed by nonexperts to generate well-characterized synthetic lesions in real patient PET images and for administering perception studies on clinical workstations without the need to install proprietary software.