Fast 4D-PET parametric imaging computation at the whole field of view level: Reliability under simulated conditions of PET KinetiX, a dedicated software solution.

Authors

Sylvain Faure, Adrien Paillet, Claude Comtat, Florent L Besson

Published in

European journal of nuclear medicine and molecular imaging. Apr 21, 2025. Epub Apr 21, 2025.

Abstract

The reliability of a new academic software, PET KinetiX, designed for fast parametric 4D-PET imaging computation, is assessed under simulated conditions.
4D-PET data were simulated using the XCAT digital phantom and realistic time-activity curves (ground truth). Four hundred analytical simulations were reconstructed using CASToR, an open-source software for tomographic reconstruction, replicating the clinical characteristics of two available PET systems with short and long axial fields of view (SAFOV and LAFOV). A total of 2,800 Patlak and 2TCM kinetic parametric maps of ¹⁸F-FDG were generated using PET KinetiX. The mean biases and standard deviations of the kinetic parametric maps were computed for each tissue label and compared to the biases of unprocessed SUV data. Additionally, the mean absolute ratio of kinetic-to-SUV contrast-to-noise ratio (CNR) was estimated for each tissue structure, along with the corresponding standard deviations.
The K_i and v_b parametric maps produced by PET KinetiX faithfully reproduced the predefined multi-tissue structures of the XCAT digital phantom for both Patlak and 2TCM models. Image definition was influenced by the 4D-PET input data: a higher number of iterations resulted in sharper rendering and higher standard deviations in PET signal characteristics. Biases relative to the ground truth varied across tissue structures and hardware configurations, similarly to unprocessed SUV data. In most tissue structures, Patlak kinetic-to-SUV CNR ratios exceeded 1 for both SAFOV and LAFOV configurations. The highest kinetic-to-SUV CNR ratio was observed in 2TCM k₃ maps within tumor regions.
PET KinetiX currently generates K_i and v_b parametric maps that are qualitatively comparable to unprocessed SUV data, with improved CNR in most cases. The 2TCM k₃ parametric maps for tumor structures exhibited the highest CNR enhancement, warranting further evaluation across different anatomical regions and radiotracer applications.

PMID:
40257613
Bibliographic data and abstract were imported from PubMed on 21 Apr 2025.

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