The use of external surrogates to predict tumor motion in real-time for extra-cranial sites requires the use of accurate correlation models. This is extremely challenging when motion prediction is to be performed over several breathing cycles, as occurs for real-time tumor tracking with Cyberknife((R)) Synchrony((R)). In this work we compare three different approaches to infer tumor motion based on external surrogates, since no comparative study is available to assess the accuracy of correlation models in tumor tracking over a long time period. We selected 20 cases in a database of 130 patients treated with real-time tumor tracking by means of the Synchrony((R)) module. The implemented correlation models comprise linear/quadratic correlation, artificial neural networks and fuzzy logic. The accuracy of each correlation model is evaluated on the basis of ground truth tumor position information acquired during treatment, as detected by means of stereoscopic X-ray imaging. Results show that the implemented models achieve an error reduction with respect to Synchrony((R)), measured at the 95% confidence level, up to 10.8% for the fuzzy logic approach. This latter is able to partly reduce the incidence of tumor tracking errors above 6 mm, resulting in improved accuracy for larger discrepancies. In conclusion, complex models are suggested to predict tumor motion over long time periods. This leads to an effective improvement with respect to Cyberknife((R)) Synchrony((R)). Future studies will investigate the sensitivity of the implemented models to the input database, in order to define optimal strategies.
Purpose
To evaluate a method for generating virtual four‐dimensional computed tomography (4DCT) from four‐dimensional magnetic resonance imaging (4DMRI) data in carbon ion radiotherapy with pencil beam scanning for abdominal tumors.
Methods
Deformable image registration is used to: (a) register each respiratory phase of the 4DMRI to the end‐exhale MRI; (b) register the reference end‐exhale CT to the end‐exhale MRI volume; (c) generate the virtual 4DCT by warping the registered CT according to the obtained deformation fields. A respiratory‐gated carbon ion treatment plan is optimized on the planning 4DCT and the corresponding dose distribution is recalculated on the virtual 4DCT. The method was validated on a digital anthropomorphic phantom and tested on eight patients (18 acquisitions). For the phantom, a ground truth dataset was available to assess the method performances from the geometrical and dosimetric standpoints. For the patients, the virtual 4DCT was compared with the planning 4DCT.
Results
In the phantom, the method exhibits a geometrical accuracy within the voxel size and Dose Volume Histograms deviations up to 3.3% for target V95% (mean dose difference ≤ 0.2% of the prescription dose, gamma pass rate > 98%). For patients, the virtual and the planning 4DCTs show good agreement at end‐exhale (3% median D95% difference), whereas other respiratory phases exhibit moderate motion variability with consequent dose discrepancies, confirming the need for motion mitigation strategies during treatment.
Conclusions
The virtual 4DCT approach is feasible to evaluate treatment plan robustness against intra‐ and interfraction motion in carbon ion therapy delivered at the abdominal site.
The results demonstrate that high sensitivity in toxicity prediction can be achieved with optimized ANNs, that are put forward to represent a valuable support in medical decisions. Future studies will be focused on enlarging the available patient database to increase the reliability of toxicity prediction algorithms and to define optimal classification criteria.
Accurate dose delivery to extra-cranial lesions requires tumor motion compensation. An effective compensation can be achieved by real-time tracking of the target position, either measured in fluoroscopy or estimated through correlation models as a function of external surrogate motion. In this work, we integrated two internal/external correlation models (a state space model and an artificial neural network-based model) into a custom infra-red optical tracking system (OTS). Dedicated experiments were designed and conducted at GSI (Helmholtzzentrum für Schwerionenforschung). A robotic breathing phantom was used to reproduce regular and irregular internal target motion as well as external thorax motion. The position of a set of markers placed on the phantom thorax was measured with the OTS and used by the correlation models to infer the internal target position in real-time. Finally, the estimated target position was provided as input for the dynamic steering of a carbon ion beam. Geometric results showed that the correlation models transversal (2D) targeting error was always lower than 1.3 mm (root mean square). A significant decrease of the dosimetric error with respect to the uncompensated irradiation was achieved in four out of six experiments, demonstrating that phase shifts are the most critical irregularity for external/internal correlation models.
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