Purpose: An accurate leaf fluence model can be used in applications such as patient specific delivery QA and in‐vivo dosimetry for TomoTherapy systems. It is known that the total fluence is not a linear combination of individual leaf fluence due to leakage‐transmission, tongue‐and‐groove, and source occlusion effect. Here we propose a method to model the nonlinear effects as linear terms thus making the MLC‐detector system a linear system. Methods: A leaf pattern basis (LPB) consisting of no‐leaf‐open, single‐leaf‐open, double‐leaf‐open and triple‐leaf‐open patterns are chosen to represent linear and major nonlinear effects of leaf fluence as a linear system. An arbitrary leaf pattern can be expressed as (or decomposed to) a linear combination of the LPB either pulse by pulse or weighted by dwelling time. The exit detector responses to the LPB are obtained by processing returned detector signals resulting from the predefined leaf patterns for each jaw setting. Through forward transformation, detector signal can be predicted given a delivery plan. An equivalent leaf open time (LOT) sinogram containing output variation information can also be inversely calculated from the measured detector signals. Twelve patient plans were delivered in air. The equivalent LOT sinograms were compared with their planned sinograms. Results: The whole calibration process was done in 20 minutes. For two randomly generated leaf patterns, 98.5% of the active channels showed differences within 0.5% of the local maximum between the predicted and measured signals. Averaged over the twelve plans, 90% of LOT errors were within +/−10 ms. The LOT systematic error increases and shows an oscillating pattern when LOT is shorter than 50 ms. Conclusion: The LPB method models the MLC‐detector response accurately, which improves patient specific delivery QA and in‐vivo dosimetry for TomoTherapy systems. It is sensitive enough to detect systematic LOT errors as small as 10 ms.
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.
Purpose: The TomoTherapy thread effect is a longitudinal pitch‐dependent dose‐ripple artifact caused by helical radiation delivery. It has been observed and studied before. However, previously empirically identified optimal pitches, 0.86/n, are only for the jaw width 5 cm and off‐axis distance 5 cm. Through theoretical analysis and simulation, we were able to identify optimal pitches for various jaw widths at various off‐axis distances. We further investigated the effect of optimization in reducing this ripple artifact. Methods: As the ripple in the dose profile is essentially a 1D problem, we set up a 1D model that account for the contributing factors, including profile divergence, the inverse square law, attenuation, and the cone effect, to study the thread effect. Based on the 1D model, we analyzed individual and combined factors theoretically and numerically to identify optimal pitches. We further set up optimization to reduce ripples in the 1D cases and simulate 3D cases using TomoTherapyˈs optimization. Results: The 1D model captures the thread effect found in 3D. At the off‐axis distance of 5 cm, for the jaw width of 5 cm, the derived optimal pitches agree with 0.86/n. However, for other jaw widths or off‐axis distances, the optimal pitches do not follow 0.86/n. For a fixed jaw width, the optimal pitches shift downward as the off‐axis distance increases. Optimization in both 1D and 3D show consistent results, and the ripples are largely suppressed by optimization through intensity modulation. Conclusions: With optimized intensity modulation, the optimal pitches tend to move toward the ones for the profile divergence factor, as optimized modulation is expected to compensate for the intensity‐related factors up to the restriction of modulation factor and optimization iterations. Choosing a good pitch, though further improves the thread effect, may not be as critical, as optimization already bring down the thread effect considerably.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.