Purpose: To introduce a four-dimensional (4D) tomotherapy treatment technique with improved motion control and patient tolerance. Methods and Materials: Computed tomographic images at 10 breathing phases were acquired for treatment planning. The full exhalation phase was chosen as the planning phase, and the CT images at this phase were used as treatment-planning images. Region of interest delineation was the same as in traditional treatment planning, except that no breathing motion margin was used in clinical target volume-planning target volume expansion. The correlation between delivery and breathing phases was set assuming a constant gantry speed and a fixed breathing period. Deformable image registration yielded the deformation fields at each phase relative to the planning phase. With the delivery/breathing phase correlation and voxel displacements at each breathing phase, a 4D tomotherapy plan was obtained by incorporating the motion into inverse treatment plan optimization. A combined laser/spirometer breathing tracking system has been developed to monitor patient breathing. This system is able to produce stable and reproducible breathing signals representing tidal volume. Results: We compared the 4D tomotherapy treatment planning method with conventional tomotherapy on a static target. The results showed that 4D tomotherapy can achieve dose distributions on a moving target similar to those obtained with conventional delivery on a stationary target. Regular breathing motion is fully compensated by motion-incorporated breathing-synchronized delivery planning. Four-dimensional tomotherapy also has close to 100% duty cycle and does not prolong treatment time. Conclusion: Breathing-synchronized delivery is a feasible 4D tomotherapy treatment technique with improved motion control and patient tolerance.
|Number of pages||7|
|Journal||International Journal of Radiation Oncology Biology Physics|
|State||Published - Aug 1 2007|
- Breathing motion
- Deformable image registration
- Respiratory gating
- Treatment plan optimization