A multifractal approach to space-filling recovery for PET quantification.

Purpose: A new image-based methodology is developed for estimating the apparent space-filling properties of an object of interest in PET imaging without need for a robust segmentation step and used to recover accurate estimates of total lesion activity (TLA).

Methods: A multifractal approach and the fractal dimension are proposed to recover the apparent space-filling index of a lesion (tumor volume, TV) embedded in nonzero background. A practical implementation is proposed, and the index is subsequently used with mean standardized uptake value (SUV mean) to correct TLA estimates obtained from approximate lesion contours. The methodology is illustrated on fractal and synthetic objects contaminated by partial volume effects (PVEs), validated on realistic (18)F-fluorodeoxyglucose PET simulations and tested for its robustness using a clinical (18)F-fluorothymidine PET test-retest dataset.

Results: TLA estimates were stable for a range of resolutions typical in PET oncology (4-6 mm). By contrast, the space-filling index and intensity estimates were resolution dependent. TLA was generally recovered within 15% of ground truth on postfiltered PET images affected by PVEs. Volumes were recovered within 15% variability in the repeatability study. Results indicated that TLA is a more robust index than other traditional metrics such as SUV mean or TV measurements across imaging protocols.

Conclusions: The fractal procedure reported here is proposed as a simple and effective computational alternative to existing methodologies which require the incorporation of image preprocessing steps (i.e., partial volume correction and automatic segmentation) prior to quantification.