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Register a new userThe grid-dose-spreading algorithm for dose distribution calculation in heavy charged particle radiotherapy
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Added by
Nobuyuki Kanematsu Ph.D.
Publication date
2007
fields
Physics
and research's language is
English
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A new variant of the pencil-beam (PB) algorithm for dose distribution calculation for radiotherapy with protons and heavier ions, the grid-dose spreading (GDS) algorithm, is proposed. The GDS algorithm is intrinsically faster than conventional PB algorithms due to approximations in convolution integral, where physical calculations are decoupled from simple grid-to-grid energy transfer. It was effortlessly implemented to a carbon-ion radiotherapy treatment planning system to enable realistic beam blurring in the field, which was absent with the broad-beam (BB) algorithm. For a typical prostate treatment, the slowing factor of the GDS algorithm relative to the BB algorithm was 1.4, which is a great improvement over the conventional PB algorithms with a typical slowing factor of several tens. The GDS algorithm is mathematically equivalent to the PB algorithm for horizontal and vertical coplanar beams commonly used in carbon-ion radiotherapy while dose deformation within the size of the pristine spread occurs for angled beams, which was within 3 mm for a single proton pencil beam of $30^circ$ incidence, and needs to be assessed against the clinical requirements and tolerances in practical situations.
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This work addresses computing techniques for dose calculations in treatment planning with proton and ion beams, based on an efficient kernel-convolution method referred to as grid-dose spreading (GDS) and accurate heterogeneity-correction method referred to as Gaussian beam splitting. The original GDS algorithm suffered from distortion of dose distribution for beams tilted with respect to the dose-grid axes. Use of intermediate grids normal to the beam field has solved the beam-tilting distortion. Interplay of arrangement between beams and grids was found as another intrinsic source of artifact. Inclusion of rectangular-kernel convolution in beam transport, to share the beam contribution among the nearest grids in a regulatory manner, has solved the interplay problem. This algorithmic framework was applied to a tilted proton pencil beam and a broad carbon-ion beam. In these cases, while the elementary pencil beams individually split into several tens, the calculation time increased only by several times with the GDS algorithm. The GDS and beam-splitting methods will complementarily enable accurate and efficient dose calculations for radiotherapy with protons and ions.
A broad-beam-delivery system for heavy-charged-particle radiotherapy often employs multiple collimators and a range-compensating filter, which potentially offer complex beam customization. In treatment planning, it is however difficult for a conventional pencil-beam algorithm to deal with these structures due to beam-size growth during transport. This study aims to resolve the problem with a novel computational model. The pencil beams are initially defined at the range compensating filter with angular-acceptance correction for the upstream collimators followed by the range compensation effects. They are individually transported with possible splitting near the downstream collimator edges to deal with its fine structure. The dose distribution for a carbon-ion beam was calculated and compared with existing experimental data. The penumbra sizes of various collimator edges agreed between them to a submillimeter level. This beam-customization model will complete an accurate and efficient dose-calculation algorithm for treatment planning with heavy charged particles.
In carbon-ion radiotherapy, single-beam delivery each day in alternate directions has been commonly practiced for operational efficiency, taking advantage of the Bragg peak and the relative biological effectiveness (RBE) for uniform dose conformation to a tumor. The treatment plans are usually evaluated with total RBE-weighted dose, which is however deficient in relevance to the biological effect in the linear-quadratic model due to its quadratic-dose term, or the dose-fractionation effect. In this study, we reformulate the extrapolated response dose (ERD), or synonymously BED, which normalizes the dose-fractionation and cell-repopulation effects as well as the RBE of treating radiation, based on inactivation of a single model cell system and a typical treating radiation in carbon-ion RT. The ERD distribution virtually represents the biological effect of the treatment regardless of radiation modality or fractionation scheme. We applied the ERD formulation to simplistic model treatments and to a preclinical survey for hypofractionation based on an actual prostate-cancer treatment of carbon-ion radiotherapy. The proposed formulation was demonstrated to be practical and to offer theoretical implications. In the prostate-cancer case, the ERD distribution was very similar to the RBE-weighted-dose distribution of the actual treatment in 12 fractions. With hypofractionation, while the RBE-weighted-dose distribution varied significantly, the ERD distribution was nearly invariant, implying that the carbon-ion radiotherapy would be insensitive to fractionation. However, treatment evaluation with simplistic biological dose is intrinsically limited and must be complemented in practice somehow by clinical experiences and biology experiments.
The next great leap toward improving treatment of cancer with radiation will require the combined use of online adaptive and magnetic resonance guided radiation therapy techniques with automatic X-ray beam orientation selection. Unfortunately, by uniting these advancements, we are met with a substantial expansion in the required dose information and consequential increase to the overall computational time imposed during radiation treatment planning, which cannot be handled by existing techniques for accelerating Monte Carlo dose calculation. We propose a deep convolutional neural network approach that unlocks new levels of acceleration and accuracy with regards to post-processed Monte Carlo dose results by relying on data-driven learned representations of low-level beamlet dose distributions instead of more limited filter-based denoising techniques that only utilize the information in a single dose input. Our method uses parallel UNET branches acting on three input channels before mixing latent understanding to produce noise-free dose predictions. Our model achieves a normalized mean absolute error of only 0.106% compared with the ground truth dose contrasting the 25.7% error of the under sampled MC dose fed into the network at prediction time. Our models per-beamlet prediction time is ~220ms, including Monte Carlo simulation and network prediction, with substantial additional acceleration expected from batched processing and combination with existing Monte Carlo acceleration techniques. Our method shows promise toward enabling clinical practice of advanced treatment technologies.
A model for beam customization with collimators and a range-compensating filter based on the phase-space theory for beam transport is presented for dose distribution calculation in treatment planning of radiotherapy with protons and heavier ions. Independent handling of pencil beams in conventional pencil-beam algorithms causes unphysical collimator-height dependence in the middle of large fields, which is resolved by the framework comprised of generation, transport, collimation, regeneration, range-compensation, and edge-sharpening processes with a matrix of pencil beams. The model was verified to be consistent with measurement and analytic estimation at a submillimeter level in penumbra of individual collimators with a combinational-collimated carbon-ion beam. The model computation is fast, accurate, and readily applicable to pencil-beam algorithms in treatment planning with capability of combinational collimation to make best use of the beam-customization devices.
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