Investigating the limits of PET/CT imaging at very low true count rates and high random fractions in ion-beam therapy monitoring

Authors


Abstract

Purpose:

External beam radiotherapy with protons and heavier ions enables a tighter conformation of the applied dose to arbitrarily shaped tumor volumes with respect to photons, but is more sensitive to uncertainties in the radiotherapeutic treatment chain. Consequently, an independent verification of the applied treatment is highly desirable. For this purpose, the irradiation-induced β+-emitter distribution within the patient is detected shortly after irradiation by a commercial full-ring positron emission tomography/x-ray computed tomography (PET/CT) scanner installed next to the treatment rooms at the Heidelberg Ion-Beam Therapy Center (HIT). A major challenge to this approach is posed by the small number of detected coincidences. This contribution aims at characterizing the performance of the used PET/CT device and identifying the best-performing reconstruction algorithm under the particular statistical conditions of PET-based treatment monitoring. Moreover, this study addresses the impact of radiation background from the intrinsically radioactive lutetium-oxyorthosilicate (LSO)-based detectors at low counts.

Methods:

The authors have acquired 30 subsequent PET scans of a cylindrical phantom emulating a patientlike activity pattern and spanning the entire patient counting regime in terms of true coincidences and random fractions (RFs). Accuracy and precision of activity quantification, image noise, and geometrical fidelity of the scanner have been investigated for various reconstruction algorithms and settings in order to identify a practical, well-suited reconstruction scheme for PET-based treatment verification. Truncated listmode data have been utilized for separating the effects of small true count numbers and high RFs on the reconstructed images. A corresponding simulation study enabled extending the results to an even wider range of counting statistics and to additionally investigate the impact of scatter coincidences. Eventually, the recommended reconstruction scheme has been applied to exemplary postirradiation patient data-sets.

Results:

Among the investigated reconstruction options, the overall best results in terms of image noise, activity quantification, and accurate geometrical recovery were achieved using the ordered subset expectation maximization reconstruction algorithm with time-of-flight (TOF) and point-spread function (PSF) information. For this algorithm, reasonably accurate (better than 5%) and precise (uncertainty of the mean activity below 10%) imaging can be provided down to 80 000 true coincidences at 96% RF. Image noise and geometrical fidelity are generally improved for fewer iterations. The main limitation for PET-based treatment monitoring has been identified in the small number of true coincidences, rather than the high intrinsic random background. Application of the optimized reconstruction scheme to patient data-sets results in a 25% − 50% reduced image noise at a comparable activity quantification accuracy and an improved geometrical performance with respect to the formerly used reconstruction scheme at HIT, adopted from nuclear medicine applications.

Conclusions:

Under the poor statistical conditions in PET-based treatment monitoring, improved results can be achieved by considering PSF and TOF information during image reconstruction and by applying less iterations than in conventional nuclear medicine imaging. Geometrical fidelity and image noise are mainly limited by the low number of true coincidences, not the high LSO-related random background. The retrieved results might also impact other emerging PET applications at low counting statistics.

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