Extension and validation of an analytical model for in vivo PET verification of proton therapy--a phantom and clinical study.

The interest in positron emission tomography (PET) as a tool for treatment verification in proton therapy has become widespread in recent years, and several research groups worldwide are currently investigating the clinical implementation. After the first off-line investigation with a PET/CT scanner at MGH (Boston, USA), attention is now focused on an in-room PET application immediately after treatment in order to also detect shorter-lived isotopes, such as O15 and N13, minimizing isotope washout and avoiding patient repositioning errors. Clinical trials are being conducted by means of commercially available PET systems, and other tests are planned using application-dedicated tomographs. Parallel to the experimental investigation and new hardware development, great interest has been shown in the development of fast procedures to provide feedback regarding the delivered dose from reconstructed PET images. Since the thresholds of inelastic nuclear reactions leading to tissue β+ -activation fall within the energy range of 15-20 MeV, the distal activity fall-off is correlated, but not directly matched, to the distal fall-off of the dose distribution. Moreover, the physical interactions leading to β+ -activation and energy deposition are of a different nature. All these facts make it essential to further develop accurate and fast methodologies capable of predicting, on the basis of the planned dose distribution, expected PET images to be compared with actual PET measurements, thus providing clinical feedback on the correctness of the dose delivery and of the irradiation field position. The aim of this study has been to validate an analytical model and to implement and evaluate it in a fast and flexible framework able to locally predict such activity distributions directly taking the reference planning CT and planned dose as inputs. The results achieved in this study for phantoms and clinical cases highlighted the potential of the implemented method to predict expected activity distributions with great accuracy. Thus, the analytical model can be used as a powerful substitute method to the sensitive and time-consuming Monte Carlo approach.