Particle filter de-noising of voxel-specific time-activity-curves in personalized 177Lu therapy

Götz T, Götz TI, Lang EW, Schmidkonz C, Maier A, Kuwert T, Ritt P (2019)

Publication Type: Journal article

Publication year: 2019

Journal

DOI: 10.1016/j.zemedi.2019.10.005

Abstract

Background: Currently, there is a high interest in 177Lu targeted radionuclide therapies, which could be attributed to favorable results obtained from 177Lu compounds targeting neuro-endocrine and prostate tumors. SPECT based dosimetry could be used for deriving dose values for individual voxels, as is the standard in external-beam radiation-therapy (EBRT). For this a time-activity-curve (TAC) at voxel resolution and also a voxel-wise modeling of radiation energy deposition are necessary. But a voxel-wise determination of TACs is problematic, since several confounding factors exist, such as e.g. poor count-statistics or registration inaccuracies, which add noise to the observed activity states. A particle filter (PF) is a class of methods which applies regularization based on a model of the temporal evolution of activity states. The aim of this study is to introduce the application of PFs for de-noising of per-voxel time-activity curves. Methods: We applied a PF for de-noising the TACs of 26 patients, who underwent 177Lu-DOTATOC or -PSMA therapy. The TACs were obtained from fully-quantitative, serial SPECT(/CT) data, acquired at 4 h, 24 h, 48 h, 72 h p.i. The model used in the PF was a mono-exponential decay and its free parameters were determined based on objective criteria. The time-integrated activities (TIA) resulting from the PF (PFF) were compared to the results of a mono-exponential fit (SF) of individual voxels in several volumes of interest (kidneys, spleen, tumors). Additionally, an organ-averaged TIA was derived from whole-organ VOIs and subsequent curve-fitting. This whole-organ TIA was also compared to the whole-organ TIAs obtained from summation of the voxel-wise TIAs from PFF and SF. Results: The number of particles was set to 1000. Optimal values for noise of observations and noise of the model were 0.25 and 0.5, respectively. The deviation of whole-organ TIAs from conventional organ-based dosimetry and the summation of the voxel-wise TIAs was substantial for SF (kidneys −22.3%, spleen −49.6%, tumor −60.0%), as well as for PFF (kidneys −37.1%, spleen −57.9%, tumor −70.9%). The distribution of voxel-wise half-lives resulting from the PFF method was considerably closer to the organ-averaged value, and the number of implausibly long half-lives (>physical HL) was reduced. Conclusion: The PFF leads to voxel-wise half-lives, which are more plausible than those resulting from SF. However, one has to admit that voxel-wise fitting generally leads to considerable deviations from the organ-averaged TIA as obtained by conventional whole-organ evaluation. Unfortunately, we did not have ground-truth TIA of our patient data and proper ground-truth could even be impossible to obtain. Nevertheless, there are strong indicators that particle filtering can be used for reducing voxel-wise TAC noise.

Authors with CRIS profile

Involved external institutions

Universität Regensburg Germany (DE)

How to cite

APA:

Götz, T., Götz, T.I., Lang, E.W., Schmidkonz, C., Maier, A., Kuwert, T., & Ritt, P. (2019). Particle filter de-noising of voxel-specific time-activity-curves in personalized 177Lu therapy. Zeitschrift für Medizinische Physik. https://dx.doi.org/10.1016/j.zemedi.2019.10.005

MLA:

Götz, Theresa, et al. "Particle filter de-noising of voxel-specific time-activity-curves in personalized 177Lu therapy." Zeitschrift für Medizinische Physik (2019).

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