When the geant 4 particle tracking method is used to calculate the average LET values within targets with a small step limit, such as smaller than 500 μm, the authors recommend the use of LETt in the dose plateau region and LETd around the Bragg peak. For a large step limit, i.e., 500 μm, LETd is recommended along the whole Bragg curve. The transition point depends on beam parameters and can be found by determining the location where the gradient of the ratio of LETd and LETt becomes positive.

With volume-of-interest (VOI) cone beam computed tomography (CBCT) imaging, one set of projection images are acquired with the VOI collimator at a regular or high exposure level and the second set of projection images are acquired without the collimator at a reduced exposure level. The high exposure VOI scan data inside the VOI and the low exposure full field scan data outside the VOI are then combined together to generate composite projection images for image reconstruction. To investigate and quantify scatter reduction, dose saving, and image quality improvement in VOI CBCT imaging, a flat panel detector based bench-top experimental CBCT system was built to measure the dose, the scatter-to-primary ratio (SPR), the image contrast, noise level, the contrast-tonoise ratio (CNR), and the figure of merits (FOMs) in the CBCT reconstructed images for two polycarbonate cylinders simulating the small and the large phantoms. The results showed that, compared to the FF CBCT technique, radiation doses for the VOI CBCT technique were reduced by a factor of 1.20 and 1.36 for the small and the large phantoms at the phantom center, respectively, and from 2.7 to 3.0 on the edge of the phantom, respectively. Inside the VOI, the SPRs were substantially reduced by a factor of 6.6 and 10.3 for the small and the large phantoms, the contrast signals were improved by a factor of 1.35 and 1.8, and the noise levels were increased by a factor of 1.27 and 1.6, respectively. As the result, the CNRs were improved by a factor of 1.06 and 1.13 for the small and the large phantoms and the FOMs were improved by a factor of 1.4 and 1.7, respectively.

Background: Major challenges in the application of intensity-modulated proton therapy (IMPT) for lung cancer patients include the uncertainties associated with breathing motion, its mitigation and its consideration in IMPT optimization. The primary objective of this research was to evaluate the potential of four-dimensional robust optimization (4DRO) methodology to make IMPT dose distributions resilient to respiratory motion as well as to setup and range uncertainties; Methods: The effect of respiratory motion, characterized by different phases of 4D computed tomography (4DCT), was incorporated into an in-house 4DRO system. Dose distributions from multiple setup and range uncertainty scenarios were calculated for each of the ten phases of CT datasets. The 4DRO algorithm optimizes dose distributions to achieve target dose coverage and normal tissue sparing for multiple setup and range uncertainty scenarios as well as for all ten respiratory phases simultaneously. IMPT dose distributions of ten lung cancer patients with different tumor sizes and motion magnitudes were optimized to illustrate our approach and its potential; Results: Compared with treatment plans generated using the conventional planning target volume (PTV)-based optimization and 3D robust optimization (3DRO), plans generated by 4DRO were found to have superior clinical target volume coverage and dose robustness in the face of setup and range uncertainties as well as for respiratory motion. In most of the cases we studied, 4DRO also resulted in more homogeneous target dose distributions. Interestingly, such improvements were found even for cases in which moving diaphragms intruded into the proton beam paths; Conclusion: The incorporation of respiratory motion, along with setup and range uncertainties, into robust optimization, has the potential to improve the resilience of target and normal tissue dose distributions in IMPT plans in the face of the uncertainties considered. Moreover, it improves the optimality of plans compared to PTV-based optimization as well as 3DRO.