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Objective: To compare radiation-induced toxicity and dosimetry parameters in patients with locally advanced nasopharyngeal cancer (LANPC) treated with a mixed-beam (MB) approach (IMRT followed by proton therapy boost) with an historic cohort of patients treated with a full course of IMRT-only. Material and methods: Twenty-seven patients with LANPC treated with the MB approach were compared to a similar cohort of 17 patients treated with IMRT-only. The MB approach consisted in a first phase of IMRT up to 54–60 Gy followed by a second phase delivered with a proton therapy boost up to 70–74 Gy (RBE). The total dose for patients treated with IMRT-only was 69.96 Gy. Induction chemotherapy was administrated to 59 and 88% and concurrent chemoradiotherapy to 88 and 100% of the MB and IMRT-only patients, respectively. The worst toxicity occurring during the entire course of treatment (acute toxicity) and early-late toxicity were registered according to the Common Terminology Criteria Adverse Events V4.03. Results: The two cohorts were comparable. Patients treated with MB received a significantly higher median total dose to target volumes (p = .02). Acute grade 3 mucositis was found in 11 and 76% (p = .0002) of patients treated with MB and IMRT-only approach, respectively, while grade 2 xerostomia was found in 7 and 35% (p = .02) of patients treated with MB and IMRT-only, respectively. There was no statistical difference in late toxicity. Local progression-free survival (PFS) and progression-free survival curves were similar between the two cohorts of patients (p = .17 and p = .40, respectively). Local control rate was 96% and 81% for patients treated with MB approach and IMRT-only, respectively. Conclusions: Sequential MB approach for LANPC patients provides a significantly lower acute toxicity profile compared to full course of IMRT. There were no differences in early-late morbidities and disease-related outcomes (censored at two-years) but a longer follow-up is required to achieve conclusive results.

Purpose: An innovative strategy to improve the sensitivity of positron emission tomography (PET)- based treatment verification in ion beam radiotherapy is proposed. Methods: Low counting statistics PET images acquired during or shortly after the treatment (Measured PET) and a Monte Carlo estimate of the same PET images derived from the treatment plan (Expected PET) are considered as two frames of a 4D dataset. A 4D maximum likelihood reconstruction strategy was adapted to iteratively estimate the annihilation events distribution in a reference frame and the deformation motion fields that map it in the Expected PET and Measured PET frames. The outputs generated by the proposed strategy are as follows: (1) an estimate of the Measured PET with an image quality comparable to the Expected PET and (2) an estimate of the motion field mapping Expected PET to Measured PET. The details of the algorithm are presented and the strategy is preliminarily tested on analytically simulated datasets. Results: The algorithm demonstrates (1) robustness against noise, even in the worst conditions where 1.5-104 true coincidences and a random fraction of 73% are simulated; (2) a proper sensitivity to different kind and grade of mismatches ranging between 1 and 10 mm; (3) robustness against bias due to incorrect washout modeling in the Monte Carlo simulation up to 1/3 of the original signal amplitude; and (4) an ability to describe the mismatch even in presence of complex annihilation distributions such as those induced by two perpendicular superimposed ion fields. Conclusions: The promising results obtained in this work suggest the applicability of the method as a quantification tool for PET-based treatment verification in ion beam radiotherapy. An extensive assessment of the proposed strategy on real treatment verification data is planned.

Background and purpose In clinical applications of Positron Emission Tomography (PET)-based treatment verification in ion beam therapy (PT-PET), detection and interpretation of inconsistencies between Measured PET and Expected PET are mostly limited by Measured PET noise, due to low count statistics, and by Expected PET bias, especially due to inaccurate washout modelling in off-line implementations. In this work, a recently proposed 4D Maximum Likelihood (ML) reconstruction algorithm which considers Measured PET and Expected PET as two different motion phases of a 4D dataset is assessed on clinical 4D PET-CT datasets acquired after carbon ion therapy. Material and methods The 4D ML reconstruction algorithm estimates: (1) Measured PET of enhanced image quality with respect to the conventional Measured PET, thanks to the exploitation of Expected PET; (2) the deformation field mapping the Expected PET onto the Measured PET as a measure of the occurred displacements. Results Results demonstrate the desired sensitivity to inconsistencies due to breathing motion and/or setup modification, robustness to noise in different count statistics scenarios, but a limited sensitivity to Expected PET washout inaccuracy. Conclusions The 4D ML reconstruction algorithm supports clinical 4D PT-PET in ion beam therapy. The limited sensitivity to washout inaccuracy can be detected and potentially overcome.

The aim of this work is to propose an adaptation of a 4D Maximum Likelihood (ML) reconstruction strategy as a tool to improve the sensitivity of PET-based treatment verification in ion beam radiotherapy. PET images acquired during/shortly after the treatment (Measured PET) and an estimate of the same PET images derived from the treatment plan (Estimated PET) are considered as two frames of a 4D dataset. The algorithm iteratively estimates the annihilation events distribution in a reference frame and the deformation motion fields that map it in the Expected and Measured PET frames. Expected PET images can be then mapped into the Measured PET frame to verify the treatment. The details of the algorithm are presented and the strategy is preliminarily tested on an analytically simulated dataset. Convergence at different count statistics and ability to detect mismatches are assessed.

In ion beam therapy, Positron Emission Tomography (PET) imaging can be applied to provide a consistency check of the delivered treatment with respect to the treatment plan. Treatment verification relies on the comparison between a PET distribution measured during or after treatment delivery (“measured PET”) and a PET simulation calculated on the basis of the treatment plan (“expected PET”). The method is challenged by the poor image quality of the measured PET, which may result in a low sensitivity to detectable mismatches between planned and applied treatment. In this work we propose the application of a motion-aware 4D Maximum Likelihood (ML) reconstruction strategy for PET-based treatment verification in ion beam therapy. This approach is specifically tested on simulated clinical-like scenarios, as well as on a real clinical dataset. The measured PET and the expected PET are interpreted as two different motion states of a 4D PET dataset. The idea is to estimate the deformation field mapping the expected PET onto the measured PET as a measure of mismatches. An enhanced measured PET is therefore obtained by warping the expected PET according to the estimated motion field. Clinical-like scenarios were reproduced by means of Monte Carlo simulations corresponding to a hypo-fractionated carbon ion treatment of liver tumour. The measured PET was attributed to different simulations introducing positioning mismatches by means of rigid translations, to be compared to the reference expected PET simulation without mismatch. Real clinical dataset include PET data acquired shortly after treatment at a dedicated PET-CT scanner installed at HIT (Heidelberg Ion beam Therapy Center, Germany), in comparison to the corresponding expected PET coming from Monte Carlo simulations. The accuracy of mismatch estimation and the robustness to noise are shown.