Purpose: To quantify MVCT dose of TomoTherapy for three jaw settings, J4, J1, and J0.1, corresponding to beam widths of 7 mm, 4 mm, and 3 mm, respectively, at the isocenter plane, and three imaging modes, fine, normal, and coarse, corresponding to a couch speed of 4, 8, and 12 mm/rotation, respectively. Material and Methods: An MVCT dose engine was commissioned specifically for the MVCT beams, including updates to the fluence attenuation table (FAT), energy deposition kernel, cone profiles, and penumbrae. MVCT dose calculation was then applied on real and synthesized images of cylindrical water phantoms of diameters ranging from 5 cm to 40 cm, and the results were compared with film measurement. Result: For the J1 jaw and coarse imaging mode, the maximum difference between calculation and measurement was about 6% of the center dose. Calculation on synthesized phantoms showed that the center dose decreased almost linearly as the phantom diameter increased, and that the fine mode received twice the dose of the normal mode and three times that of the coarse mode. The maximal dose due to the helical ripple ranged from 100%∼200% of the center dose, with increasing ratios for larger phantoms (due to the larger radius), smaller jaws, and faster couch speed (the latter two yielding a higher helical pitch). For all jaw settings and couch speeds, the mean dose and average surface dose vary from 95%–115% of the center dose with increasing ratios for larger phantoms. Conclusion: An MVCT dose calculator was set up with validation through film measurement and subsequently used to calculate TomoTherapy MVCT dose for various phantom sizes under various imaging parameters. The results can serve as a reference for TomoTherapy MVCT dose.
Purpose: To efficiently calculate the head scatter fluence for an arbitrary intensity‐modulated field with any source distribution using the source occlusion model. Method: The source occlusion model with focal and extra focal radiation (Jaffray et al, 1993) can be used to account for LINAC head scatter. In the model, the fluence map of any field shape at any point can be calculated via integration of the source distribution within the visible range, as confined by each segment, using the detector eye's view. A 2D integration would be required for each segment and each fluence plane point, which is time‐consuming, as an intensity‐modulated field contains typically tens to hundreds of segments. In this work, we prove that the superposition of the segmental integrations is equivalent to a simple convolution regardless of what the source distribution is. In fact, for each point, the detector eye's view of the field shape can be represented as a function with the origin defined at the point's pinhole reflection through the center of the collimator plane. We were thus able to reduce hundreds of source plane integration to one convolution. We calculated the fluence map for various 3D and IMRT beams and various extra‐focal source distributions using both the segmental integration approach and the convolution approach and compared the computation time and fluence map results of both approaches. Results: The fluence maps calculated using the convolution approach were the same as those calculated using the segmental approach, except for rounding errors (<0.1%). While it took considerably longer time to calculate all segmental integrations, the fluence map calculation using the convolution approach took only ∼1/3 of the time for typical IMRT fields with ∼100 segments. Conclusions: The convolution approach for head scatter fluence calculation is fast and accurate and can be used to enhance the online process.
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