Motion mitigation for lung cancer patients treated with active scanning proton therapy

PURPOSE

Motion interplay can affect the tumor dose in scanned proton beam therapy. This study assesses the ability of rescanning and gating to mitigate interplay effects during lung treatments.

METHODS

The treatments of five lung cancer patients [48 Gy(RBE)/4fx] with varying tumor size (21.1-82.3 cm(3)) and motion amplitude (2.9-30.6 mm) were simulated employing 4D Monte Carlo. The authors investigated two spot sizes (σ ∼ 12 and ∼ 3 mm), three rescanning techniques (layered, volumetric, breath-sampled volumetric) and respiratory gating with a 30% duty cycle.

RESULTS

For 4/5 patients, layered rescanning 6/2 times (for the small/large spot size) maintains equivalent uniform dose within the target >98% for a single fraction. Breath sampling the timing of rescanning is ∼ 2 times more effective than the same number of continuous rescans. Volumetric rescanning is sensitive to synchronization effects, which was observed in 3/5 patients, though not for layered rescanning. For the large spot size, rescanning compared favorably with gating in terms of time requirements, i.e., 2x-rescanning is on average a factor ∼ 2.6 faster than gating for this scenario. For the small spot size however, 6x-rescanning takes on average 65% longer compared to gating. Rescanning has no effect on normal lung V20 and mean lung dose (MLD), though it reduces the maximum lung dose by on average 6.9 ± 2.4/16.7 ± 12.2 Gy(RBE) for the large and small spot sizes, respectively. Gating leads to a similar reduction in maximum dose and additionally reduces V20 and MLD. Breath-sampled rescanning is most successful in reducing the maximum dose to the normal lung.

CONCLUSIONS

Both rescanning (2-6 times, depending on the beam size) as well as gating was able to mitigate interplay effects in the target for 4/5 patients studied. Layered rescanning is superior to volumetric rescanning, as the latter suffers from synchronization effects in 3/5 patients studied. Gating minimizes the irradiated volume of normal lung more efficiently, while breath-sampled rescanning is superior in reducing maximum doses to organs at risk.

Contour-Driven Atlas-Based Segmentation

We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images.