On the parametrization of lateral dose profiles in proton radiation therapy

Bellinzona, Valentina Elettra; Ciocca, Mario; Embriaco, Alessia; Fontana, A.; Mairani, A.; Mori, Martina; Parodi, Katia

doi:10.1016/j.ejmp.2015.05.004

Cited by 33 publications

(37 citation statements)

References 20 publications

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“…( 17 ). The behavior is likely explained by the limited number of charged particles past the BP causing the second Gaussian to be fitted to the “aura” of uncharged secondaries ( 24 ), which is more appropriately described by a non-Gaussian function ( 20 , 21 , 23 , 24 ). Although a double-Gaussian fit still provides some improvement in this region (c.f.…”

Section: Methodsmentioning

confidence: 99%

Fast Pencil Beam Dose Calculation for Proton Therapy Using a Double-Gaussian Beam Model

2015

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The highly conformal dose distributions produced by scanned proton pencil beams (PBs) are more sensitive to motion and anatomical changes than those produced by conventional radiotherapy. The ability to calculate the dose in real-time as it is being delivered would enable, for example, online dose monitoring, and is therefore highly desirable. We have previously described an implementation of a PB algorithm running on graphics processing units (GPUs) intended specifically for online dose calculation. Here, we present an extension to the dose calculation engine employing a double-Gaussian beam model to better account for the low-dose halo. To the best of our knowledge, it is the first such PB algorithm for proton therapy running on a GPU. We employ two different parameterizations for the halo dose, one describing the distribution of secondary particles from nuclear interactions found in the literature and one relying on directly fitting the model to Monte Carlo simulations of PBs in water. Despite the large width of the halo contribution, we show how in either case the second Gaussian can be included while prolonging the calculation of the investigated plans by no more than 16%, or the calculation of the most time-consuming energy layers by about 25%. Furthermore, the calculation time is relatively unaffected by the parameterization used, which suggests that these results should hold also for different systems. Finally, since the implementation is based on an algorithm employed by a commercial treatment planning system, it is expected that with adequate tuning, it should be able to reproduce the halo dose from a general beam line with sufficient accuracy.

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Section: Methodsmentioning

confidence: 99%

Fast Pencil Beam Dose Calculation for Proton Therapy Using a Double-Gaussian Beam Model

2015

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“…On the one hand, fewer hot spots are produced beyond that structure for MicroCalc than for PCS, as observable by comparing has been pointed out that might be insufficient to reproduce adequately transversal dose distributions. 32 As pointed out, the function in Eq. (5) that characterizes the energy deposition on the proton energy is extrapolated beyond 100 MeV 14 because of the limitation existing in the current version of Geant4-DNA.…”

Section: Discussionmentioning

confidence: 98%

“…(d) and (e)]. However, the beam model employed in Eclipse TM to analytically compute dose uses a single Gaussian to characterize the lateral profile whereas it has been pointed out that might be insufficient to reproduce adequately transversal dose distributions …”

Section: Discussionmentioning

confidence: 99%

Calculation of clinical dose distributions in proton therapy from microdosimetry

et al. 2019

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Purpose To introduce a new algorithm—MicroCalc—for dose calculation by modeling microdosimetric energy depositions and the spectral fluence at each point of a particle beam. Proton beams are considered as a particular case of the general methodology. By comparing the results obtained against Monte Carlo computations, we aim to validate the microdosimetric formalism presented here and in previous works. Materials and methods In previous works, we developed a function on the energy for the average energy imparted to a microdosimetric site per event and a model to compute the energetic spectrum at each point of the patient image. The number of events in a voxel is estimated assuming a model in which the voxel is completely filled by microdosimetric sites. Then, dose at every voxel is computed by integrating the average energy imparted per event multiplied by the number of events per energy beam of the spectral distribution within the voxel. Our method is compared with the proton convolution superposition (PCS) algorithm implemented in Eclipse™ and the fast Monte Carlo code MCsquare, which is here considered the benchmark, for in‐water calculations, using in both cases clinically validated beam data. Two clinical cases are considered: a brain and a prostate case. Results For a SOBP beam in water, the mean difference at the central axis found for MicroCalc is of 0.86% against 1.03% for PCS. Three‐dimensional gamma analyses in the PTVs compared with MCsquare for criterion (3%, 3 mm) provide gamma index of 95.07% with MicroCalc vs 94.50% with PCS for the brain case and 99.90% vs 100.00%, respectively, for the prostate case. For selected organs at risk in each case (brainstem and rectum), mean and maximum difference with respect to MCsquare dose are analyzed. In the brainstem, mean differences are 0.25 Gy (MicroCalc) vs 0.56 Gy (PCS), whereas for the rectum, these values are 0.05 Gy (MicroCalc) vs 0.07 Gy (PCS). Conclusions The accuracy of MicroCalc seems to be, at least, not inferior to PCS, showing similar or better agreement with MCsquare in the considered cases. Additionally, the algorithm enables simultaneous computation of other quantities of interest. These results seem to validate the microdosimetric methodology in which the algorithm is based on.

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“…In PB algorithms for protons, like the ones proposed in Fujimoto et al and da Silva et al, the central beam broadening due to multiple Coulomb scattering effects and nuclear interactions is described using single or multiple Gaussian beams. More complex parametrization models, based on direct fitting of lateral dose deposition functions, are very common to account for the low‐dose halo region. Although these models showed to be capable of an accurate dose distribution description, the extension to the magnetic field case is not straightforward.…”

Section: Discussionmentioning

confidence: 99%

A pencil beam algorithm for magnetic resonance image‐guided proton therapy

2018

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PurposeThe feasibility of magnetic resonance image (MRI)‐based proton therapy is based, among several other factors, on the implementation of appropriate extensions on current dose calculation methods. This work aims to develop a pencil beam algorithm (PBA) for dose calculation of proton beams within magnetic field regions of up to 3 T.MethodsMonte Carlo (MC) simulations using the GATE 7.1/GEANT4.9.4p02 toolkit were performed to generate calibration and benchmarking data for the PBA. Dose distributions from proton beams in the clinical required energy range 60–250 MeV impinging on a 400 × 400 × 400 mm3 water phantom and transverse magnetic fields ranging from 0 to 3 T were considered. Energy depositions in homogeneous and heterogeneous phantoms filled with water, adipose, bone, and air were evaluated for proton energies of 80, 150, and 240 MeV, combining a trajectory calculation method and look‐up tables (LUT). A novel parametrization model, using independent tailed Gauss fitting functions, was employed to describe the nonsymmetric shape of lateral beam profiles. Integrated depth‐dose curves (IDD), lateral dose profiles, and two‐dimensional dose distributions calculated with the PBA were compared with results from MC simulations to assess the performance of the algorithm. A gamma index criterion of 2%/2 mm was used for analysis.ResultsA close to perfect agreement was observed for PB‐based dose calculations in water in magnetic fields of 0.5, 1.5, and 3 T. IDD functions showed differences between the PBA and MC of less than 0.1% before the Bragg peak, and deviations of 2–8% in the distal energy falloff region. Gamma index pass rates higher than 99% and mean values lower than 0.1 were encountered for all analyzed configurations. For homogeneous phantoms, only the full bone configuration offered deviations in the Bragg peak position of up to 1.7% and overestimations of the lateral beam spot width for high‐energy protons and magnetic field intensities. An excellent agreement between PBA and MC dose calculation was also achieved using slab‐like and lateral heterogeneous phantoms, with gamma index passing rates above 98% and mean values between 0.1 and 0.2. As expected, agreement reduced for high‐energy protons and high‐intensity magnetic fields, although results remained good enough to be considered for future implementation in clinical practice.ConclusionsThe proposed pencil beam algorithm for protons can accurately account for dose distortion effects induced by external magnetic fields. The application of an analytical model for dose estimation and corrections reduces the calculation times considerably, making the presented PBA a suitable candidate for integration in a treatment planning system.

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On the parametrization of lateral dose profiles in proton radiation therapy

Cited by 33 publications

References 20 publications

Fast Pencil Beam Dose Calculation for Proton Therapy Using a Double-Gaussian Beam Model

Fast Pencil Beam Dose Calculation for Proton Therapy Using a Double-Gaussian Beam Model

Calculation of clinical dose distributions in proton therapy from microdosimetry

A pencil beam algorithm for magnetic resonance image‐guided proton therapy

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