Search

IBA Dosimetry GmbH participated in the Joint Research Project ‘Increasing cancer treatment efficacy using 3D brachytherapy’ as a non-funded partner in the work package which was mostly dedicated to the determination of dose-to-water distribution from a high-dose-rate (HDR) brachytherapy source.The dose distribution was measured with a MatriXX (MXX) 2D detector array and compared with Dose Cube Data, calculated by treatment planning systems (TPS). All measurements and calculations were performed in cooperation with OUSA, Bratislava and FNB, Prague. The comparison has been carried out for three irradiation geometries: single source position, single line and four line motions of the source, and with the effective point of measurement in a plane at 6 mm, 10 mm and 20 mm distance from the source position.The comparison of the MXX measurements and the TPS calculations was evaluated by the commercial IBA Dosimetry software OmniPro I'mRT (1) as the difference between maximum of measured and calculated values and (2) as the maximum difference between the two-dimensional distributions of measured and calculated values. The dose distribution was evaluated by the gamma method with parameters 3 mm and 3%.All differences of comparison of the MXX measurements and TPS calculations were within the range ±10% and the γ-index was less than 1 for 96% (or 97%, respectively) of the dose distribution in the plane at 10 mm distance from the source position.

Image guidance has become a pre-requisite for hypofractionated radiotherapy where the applied dose per fraction is increased. Particularly in stereotactic body radiotherapy (SBRT) for lung tumours, one has to account for set-up errors and intrafraction tumour motion. In our feasibility study, we compared digitally reconstructed radiographs (DRRs) of lung lesions with MV portal images (PIs) to obtain the displacement of the tumour before irradiation. The verification of the tumour position was performed by rigid intensity based registration and three different merit functions such as the sum of squared pixel intensity differences, normalized cross correlation and normalized mutual information. The registration process then provided a translation vector that defines the displacement of the target in order to align the tumour with the isocentre. To evaluate the registration algorithms, 163 test images were created and subsequently, a lung phantom containing an 8 cm(3) tumour was built. In a further step, the registration process was applied on patient data, containing 38 tumours in 113 fractions. To potentially improve registration outcome, two filter types (histogram equalization and display equalization) were applied and their impact on the registration process was evaluated. Generated test images showed an increase in successful registrations when applying a histogram equalization filter whereas the lung phantom study proved the accuracy of the selected algorithms, i.e. deviations of the calculated translation vector for all test algorithms were below 1 mm. For clinical patient data, successful registrations occurred in about 59% of anterior-posterior (AP) and 46% of lateral projections, respectively. When patients with a clinical target volume smaller than 10 cm(3) were excluded, successful registrations go up to 90% in AP and 50% in lateral projection. In addition, a reliable identification of the tumour position was found to be difficult for clinical target volumes at the periphery of the lung, close to backbone or diaphragm. Moreover, tumour movement during shallow breathing strongly influences image acquisition for patient positioning. Recapitulating, 2D/3D image registration for lung tumours is an attractive alternative compared to conventional CT verification of the tumour position. Nevertheless, size and location of the tumour are limiting parameters for an accurate registration process.