Abstract Background. In lung cancer radiotherapy, planning on the midventilation (MidV) bin of a four-dimensional (4D) CT scan can reduce the systematic errors introduced by respiratory tumour motion compared to conventional CT. In this study four different methods for MidV bin selection are evaluated. Material and methods. The study is based on 4DCT scans of 19 patients with a total of 23 peripheral lung tumours having peak-to-peak displacement ≥ 5 mm in at least one of the left-right (LR), anterior-posterior (AP) or cranio-caudal (CC) directions. For each tumour, the MidV bin was selected based on: 1) visual evaluation of tumour displacement; 2) rigid registration of tumour position; 3) diaphragm displacement in the CC direction; and 4) carina displacement in the CC direction. Determination of the MidV bin based on the displacement of the manually delineated gross tumour volume (GTV) was used as a reference method. The accuracy of each method was evaluated by the distance between GTV position in the selected MidV bin and the time-weighted mean position of GTV throughout the bins (i.e. the geometric MidV error). Results. Median (range) geometric MidV error was 1.4 (0.4-5.4) mm, 1.4 (0.4-5.4) mm, 1.9 (0.5-6.9) mm, 2.0 (0.5-12.3) mm and 1.1 (0.4-5.4) mm for the visual, rigid registration, diaphragm, carina, and reference method. Median (range) absolute difference between geometric MidV error for the evaluated methods and the reference method was 0.0 (0.0-1.2) mm, 0.0 (0.0-1.7) mm, 0.7 (0.0-3.9) mm and 1.0 (0.0-6.9) mm for the visual, rigid registration, diaphragm and carina method. Conclusion. The visual and semi-automatic rigid registration methods were equivalent in accuracy for selecting the MidV bin of a 4DCT scan. The methods based on diaphragm and carina displacement cannot be recommended without modifications.
Acta Odontologica Scandinavica, 2013, Vol 52, Issue 8, p. 1715-1722
Managers and employees at universities, research institutions etc.; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't