A non-voxel-based broad-beam (NVBB) framework for IMRT treatment planning

AAPM Task Group Report 306: Quality control and assurance for tomotherapy: An update to Task Group Report 148

Since the publication of AAPM Task Group (TG) 148 on quality assurance (QA) for helical tomotherapy, there have been many new developments on the tomotherapy platform involving treatment delivery, on-board imaging options, motion management, and treatment planning systems (TPSs). In response to a need for guidance on quality control (QC) and QA for these technologies, the AAPM Therapy Physics Committee commissioned TG 306 to review these changes and make recommendations related to these technology updates. The specific objectives of this TG were (1) to update, as needed, recommendations on tolerance limits, frequencies and QC/QA testing methodology in TG 148, (2) address the commissioning and necessary QA checks, as a supplement to Medical Physics Practice Guidelines (MPPG) with respect to tomotherapy TPS and (3) to provide risk-based recommendations on the new technology implemented clinically and treatment delivery workflow. Detailed recommendations on QA tests and their tolerance levels are provided for dynamic jaws, binary multileaf collimators, and Synchrony motion management. A subset of TPS commissioning and QA checks in MPPG 5.a. applicable to tomotherapy are recommended. In addition, failure mode and effects analysis has been conducted among TG members to obtain multi-institutional analysis on tomotherapy-related failure modes and their effect ranking.

Global optimization for spot-based treatment planning

PURPOSE

Many radiotherapy modalities can deliver concentrated radiation in the form of spots, such as Gamma Knife (GK), GammaPod (GP), intensity-modulated proton therapy, and brachytherapy, and can be generalized as spot-based treatments. These treatments have a great therapeutic advantage of creating potent target dose while sparing the surrounding normal tissues. However, global optimization to determine the spot positions, shapes, and intensities is an intractable combinatorial problem for any real 3D problem. The conventional approach adopts heuristic spot selection and intensity optimization in a sequential manner to mitigate the problem complexity. In this work, we propose a novel framework that enables global optimization of spot-based treatment planning.

METHODS

The framework is based on kernel decomposition (KD) dose calculation, which models each spot dose as a scaled shift-invariant kernel, with the reference kernels and scales pre-calculated. During optimization, the framework incorporates Fast Fourier Transform (FFT) for objective and derivative evaluations and accommodates all spot candidates in optimization search with a temporal complexity of O(N3 log N) as opposed to O(N6 ) complexity in the conventional beamlet framework for volume dimensions of N × N × N. We demonstrated the FFT framework using simulations with different objectives. The framework's planning performance was illustrated using clinical GK and GP cases.

RESULTS

Pre-processing involves only a small number of reference kernels and a scale map for the KD model with marginal spatial and temporal overheads. For simulations with 512 × 512 image dimensions, plan optimization finished in ∼2 seconds with FFT, whereas it took 100× longer with the beamlet approach. For clinical cases, the FFT attained solutions within a minute with improved plan quality compared to clinical plans: better conformity and less integral dose because of using a global fine search space for optimal spots.

CONCLUSIONS

The scaled shift-invariance and FFT framework opens a new paradigm for spot-based treatment planning, as it can substantially reduce both the spatial and temporal complexities. The framework makes global optimization for spot-based treatment planning clinically feasible.

TransDose: a transformer-based UNet model for fast and accurate dose calculation for MR-LINACs

Objective. To present a transformer-based UNet model (TransDose) for fast and accurate dose calculation for magnetic resonance-linear accelerators (MR-LINACs). Approach. A 2D fluence map from each beam was first projected into a 3D fluence volume and then fed into the TransDose model together with patient density volume and output predicted beam dose. The proposed TransDose model combined a 3D residual UNet with a transformer encoder, where convolutional layers extracted the volumetric spatial features, and the transformer encoder processed the long-range dependencies in a global space. Ninety-eight cases with four tumor sites (brain, nasopharynx, lung, and rectum) treated with fixed-beam intensity-modulated radiotherapy were included in the dataset; 78 cases were used for model training and validation; and 20 cases were used for testing. The ground-truth beam doses were calculated with Monte Carlo (MC) simulations within 1% statistical uncertainty and magnetic field strength B = 1.5 T in the superior and inferior direction. Beam angles from the training and validation datasets were rotated 2–5 times, and doses were recalculated to augment the datasets. Results. The dose-volume histograms and indices between the predicted and MC doses showed good consistency. The average 3D γ-passing rates (3%/2 mm, for dose regions above 10% of maximum dose) were 99.13 ± 0.89% (brain), 98.31 ± 1.92% (nasopharynx), 98.74 ± 0.70% (lung), and 99.28 ± 0.25% (rectum). The average dose calculation time, which included the fluence projection and model prediction, was less than 310 ms for each beam. Significance. We successfully developed a transformer-based UNet dose calculation model—TransDose in magnetic fields. Its accuracy and efficiency indicated its potential for use in online adaptive plan optimization for MR-LINACs.

Volumetric dose extension for isodose tuning

PURPOSE

To develop a method that can extend dose from two isodose surfaces (isosurfaces) to the entire patient volume, and to demonstrate its application in radiotherapy plan isodose tuning.

METHODS

We hypothesized that volumetric dose distribution can be extended from two isosurfaces-the 100% isosurface and a reference isosurface--with the distances to these two surfaces (L_ref ) as extension variables. The extension function is modeled by a three-dimensional lookup table (LUT), where voxel dose values from clinical plans are binned by three indexes: L₁₀₀ , L_ref and D_ref (reference dose level). The mean and standard deviation of voxel doses in each bin are calculated and stored in LUT. Volumetric dose extension is performed voxelwisely by indexing the LUT with the L₁₀₀ , L_ref and D_ref of each query voxel. The mean dose stored in the corresponding bin is filled into the query voxel as extended dose, and the standard deviation be filled voxelwisely as the uncertainty of extension result. We applied dose extension in isodose tuning, which aims to tune volumetric dose distribution by isosurface dragging. We adopted extended dose as an approximate dose estimation, and combined it with dose correction strategy to achieve accurate dose tuning.

RESULTS

We collected 32 post-operative prostate volumetric modulated arc therapy (VMAT) cases and built the LUT and its associated uncertainties from the doses of 27 cases. The dose extension method was tested on five cases, whose dose distributions were defined as ground truth (GT). We extended the doses from 100% and 50% GT isosurfaces to the entire volume, and evaluated the accuracy of extended doses. The 5 mm/5% gamma passing rate (GPR) of extended doses are 92.0%. The mean error is 4.5%, which is consistent to the uncertainty estimated by LUT. The dose difference in 90.5% of voxels is within two sigma and 97.5% in three sigma. The calculation time is less than two seconds. To simulate plan isodose tuning, we optimized a dose with less sparing on rectum (than GT dose) and defined it as a "base dose"-the dose awaiting isosurface dragging. In front-end, the simulated isodose tuning is conducted as such that the base dose was given to plan tuner, and its 50% isosurface would be dragged to the desired position (position of 50% isosurface in GT dose). In back-end, the output of isodose tuning is obtained by 1) extending dose from the desired isosurfaces and viewed the extended dose as an approximate dose, 2) obtaining a correction map from the base dose, and 3) applying the correction map to the extended dose. The accuracy of output-extended dose with correction-was 97.2% in GPR (3 mm/3%) and less than 1% in mean dose difference. The total calculation time is less than two seconds, which allows for interactive isodose tuning.

CONCLUSIONS

We developed a dose extension method that generates volumetric dose distribution from two surfaces. The application of dose extension is in interactive isodose tuning. The distance-based LUT fashion and correction strategy guarantee the computation efficiency and accuracy in isodose tuning. This article is protected by copyright. All rights reserved.

Dose kernel decomposition for spot-based radiotherapy treatment planning

PURPOSE

Pre-calculation of accurate dose deposition kernels for treatment planning of spot-based radiotherapies, such as Gamma Knife (GK) and Gamma Pod (GP), can be very time-consuming and may require large data storage with an enormous number of possible spots. We proposed a novel kernel decomposition (KD) model to address accurate and fast (real-time) dose calculation with reduced data storage requirements for spot-based treatment planning. The application of the KD model was demonstrated for clinical GK and GP radiotherapy platforms.

METHODS

The dose deposition kernel at each spot (shot position) is modeled as the product of a shift-invariant kernel based on a reference kernel and spatially variant scale factor. The reference kernel, one for each collimator, is defined at the center of the commissioning phantom for GK and at the center of the treatment target for GP and calculated using the Monte Carlo (MC) method. The spatially variant scale factor is defined as the ratio of the mean tissue maximum ratio (TMR) at the candidate shot position to that at the reference kernel position, and the mean TMR map is calculated within the entire volume through parallel beam ray tracing on the density image followed by averaging over all source directions. The proposed KD dose calculations were compared with the MC method and with the GK and GP treatment planning system (TPS) computations for various shot positions and collimator sizes utilizing a phantom and 14 and 12 clinical plans for GK and GP, respectively.

RESULTS

For the phantom study, the KD Gamma index (3%/1 mm) passing rates were greater than 99% (median 100%) relative to the MC doses, except for the shots close to the boundary. The passing rates dropped below 90% for 8 mm (16 mm) shots positioned within ∼1 cm (∼2 cm) of the boundary. For the clinical GK plans, the KD Gamma passing rates were greater than 99% (median 100%) compared to the MC and greater than 92% (median 99%) compared to the TPS. For the clinical GP plans, the KD Gamma passing rates were greater than 95% (median 98%) compared to the MC and greater than 91% (median 97%) compared to the TPS. The scale factors were calculated in sub-seconds with GPU implementation and only need to be calculated once before treatment plan optimization. The calculation of the dose kernel was also within sub-seconds without requiring beam-by-beam calculation commonly done in the TPS.

CONCLUSION

The proposed model can provide an accurate dose and enables real-time dose and derivative calculations by kernel shifting and scaling without pre-calculating or requiring large data storage for GK and GP dose deposition kernels during treatment planning. This model could be useful for spot-based radiotherapy treatment planning by allowing an efficient global fine search for optimal spots.

Assessing the dose rate delivery of helical TomoTherapy prostate and head & neck treatments

The dose rate distributions delivered to 55 prostate and head & neck (H&N) cancer patients treated with a helical TomoTherapy (HT) system were resolved and assessed with regard to pitch and field width defined during treatment planning. Statistical analysis of the studied cases showed that the median treatment delivery time was 4.4 min and 6.3 min for the prostate and H&N cases, respectively. Dose rate volume histogram data for the studied cases showed that the 25% and 12% of the volume of the planning target volumes of the prostate and H&N cases are irradiated with a dose rate of greater or equal to 1 Gy min^-1. Quartile dose rate (QDR) data confirmed that in HT, where the target is irradiated in slices, most of the dose is delivered to each voxel of the target when it travels within the beam. Analysis of the planning data from all cases showed that this lasts for 68 s (median value). QDRs results showed that using the 2.5 cm field width, 75% of the prescribed dose is delivered to target voxels with a median dose rate of at least 3.2 Gy min^-1and 4.5 Gy min^-1, for the prostate and H&N cases, respectively. Systematically higher dose rates were observed for the H&N cases due to the shallower depths of the lesions in this anatomical site. Delivered dose rates were also found to increase with field width and pitch setting, due to the higher output of the system which, in general, results in accordingly decreased total treatment time. The biological effect of the dose rate findings of this work needs to be further investigated using in-vitro studies and clinical treatment data.

Investigation of absolute dose calibration accuracy for TomoTherapy using real water

A systematic bias in TomoTherapy output calibration was reported by the Imaging and Radiation Oncology Core Houston (IROC‐H) after analyzing intensity‐modulated radiation therapy (IMRT) credentialing results from hundreds of TomoTherapy units. Multiple theories were developed to explain this observation. One theory was that the use of a solid water “cheese” phantom instead of real water in the calibration measurement was the culprit. A phantom filled with distilled water was built to investigate whether our TomoTherapy was miscalibrated due to the use of a solid water phantom. A miscalibration of −1.47% was detected on our TomoTherapy unit. It is found that despite following the vendor's updated recommendation on computed tomography (CT) number to density calibration, the cheese phantom was still mapped to a density of 1.028 g/cm³, rather than the 1.01 g/cm³ value reported in literature. When the density of the cheese phantom was modified to 1.01 g/cm³ in the treatment planning system, the measurement also indicated that our TomoTherapy machine was miscalibrated by −1.52%, agreeing with the real water phantom findings. Our single‐institution finding showed that the cheese phantom density assignment can introduce greater than 1% errors in the TomoTherapy absolute dose calibration. It is recommended that the absolute dose calibration for TomoTherapy be performed either in real water or in the cheese phantom with the density in TPS overridden as 1.01 g/cm³.

Deep learning-based inverse mapping for fluence map prediction

We developed a fluence map prediction method that directly generates fluence maps for a given desired dose distribution without optimization for volumetric modulated arc therapy (VMAT) planning. The prediction consists of two steps. First, projections of the desired dose are calculated and then inversely mapped to fluence maps in the phantom geometry by a deep neural network. Second, a plan scaling technique is applied to scale fluence maps from phantom to patient geometry. We evaluated the performance of the proposed fluence map prediction method for 102 head and neck (H&N) and 14 prostate cancer VMAT plans by comparing the patient doses calculated from the predicted fluence maps with the given desired dose distributions. The mean dose differences were 1.42% ± 0.37%, 1.53% ± 0.44% and 1.25% ± 0.44% for the planning target volume (PTV), the region from the PTV boundary to the 50% isodose line, and the region from the 50% to the 20% isodose line, respectively. The gamma passing rate was 98.06% ± 2.64% with the 3 mm/3% criterion. The prediction time for a single VMAT plan was less than one second. In conclusion, we developed an inverse mapping-based method that predicts fluence maps for desired dose distributions with high accuracy. Our method is effectively an optimization-free inverse planning approach, which was orders of magnitude faster than fluence map optimization. Combining the proposed method with leaf sequencing has the potential to dramatically speed up VMAT treatment planning.

Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy

PURPOSE

Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using anatomical structures and static beam orientations. However, Pareto optimal dose prediction for intensity-modulated radiation therapy (IMRT) prostate planning with variable beam numbers and orientations has not yet been investigated. We propose to develop a deep learning model that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities.

METHODS

We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35 000 plans. We studied and compared two different models, Models I and II. Although they both used the same anatomical structures - including the planning target volume (PTV), organs at risk (OARs), and body - these models were designed with two different methods for representing beam angles. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. We divided the 70 patients into 54 training, 6 validation, and 10 testing patients, thus yielding 27 000 training, 3000 validation, and 5000 testing plans. Mean square loss (MSE) was taken as the loss function. We used the Adam optimizer with a default learning rate of 0.01 to optimize the network's performance. We evaluated the models' performance by comparing their predicted dose distributions with the ground truth (Pareto optimal) dose distribution, in terms of dose volume histogram (DVH) plots and evaluation metrics such as PTV D98 , D95 , D50 , D2 , Dmax , Dmean , Paddick Conformation Number, R50, and Homogeneity index.

RESULTS

Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. The DVHs generated also precisely matched the ground truth. Evaluation metrics such as PTV statistics, dose conformity, dose spillage (R50), and homogeneity index also confirmed the accuracy of PTV curves on the DVH. Quantitatively, Model I's prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), and 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), and 6.30% (D2). Model I also outperformed Model II in terms of the mean dose error and the max dose error on the PTV, bladder, rectum, left femoral head, and right femoral head.

CONCLUSIONS

Treatment planners who use our models will be able to use deep learning to control the trade-offs between the PTV and OAR weights, as well as the beam number and configurations in real time. Our dose prediction methods provide a stepping stone to building automatic IMRT treatment planning.

Parallel beamlet dose calculation via beamlet contexts in a distributed multi-GPU framework

PURPOSE

Dose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model-based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units (GPUs), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under-utilizing the potential of modern GPUs. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi-GPU computational nodes.

METHODS

The proposed context-based transformation separates beamlet-local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed-cone convolution superposition procedure, producing the set of beamlet-specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager-worker architecture is constructed around the context-based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPUs. Phantom experiments were executed to verify the accuracy of the context-based approach compared to Monte Carlo and a reference CPU-CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet-sequential GPU-CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPUs.

RESULTS

The dosimetric accuracy of the context-based method displays <1.35% and 2.35% average error from the existing serialized CPU-CCCS algorithm and Monte Carlo simulation for beamlet-specific PDDs in water and slab phantoms, respectively. The context-based method demonstrates substantial speedup of up to two orders of magnitude over the beamlet-sequential GPU-CCCS method in the tested configurations. The context-based framework demonstrates near linear scaling in the number of distributed compute nodes and GPUs employed, indicating that it is flexible enough to meet the performance requirements of most users by simply increasing the hardware utilization.

CONCLUSIONS

The context-based approach demonstrates a new expectation of performance for beamlet-based dose calculation methods. This approach has been successful in accelerating the dose calculation process for very large-scale treatment planning problems - such as automatic 4π IMRT beam orientation and VMAT arc trajectory selection, with hundreds of thousands of beamlets - in clinically feasible timeframes. The flexibility of this framework makes it as a strong candidate for use in a variety of other very large-scale treatment planning tasks and clinical workflows.

Determining efficient helical IMRT modulation factor from the MLC leaf-open time distribution on precision treatment planning system

Purpose

Since the modulation factor (MF) impacts both plan quality and delivery efficiency in tomotherapy Intensity Modulated Radiation Therapy (IMRT) treatment planning, the purpose of this study was to demonstrate a technique in determining an efficient MF from the Multileaf Collimator (MLC) leaf‐open time (LOT) distribution of a tomotherapy treatment delivery plan.

Methods

Eight clinical plans of varying complexity were optimized with the highest allowed MF on the Accuracy Precision treatment planning system. Using a central limit theorem argument a range of reduced MFs were then determined from the first two moments of the LOT distribution. A step down approach was used to calculate the reduced‐MF plans and plan comparison tools available on the Precision treatment planning system were used to evaluate dose differences with the reference plan.

Results

A reduced‐MF plan that balanced delivery time and dosimetric quality was found from the set of five MFs determined from the LOT distribution of the reference plan. The reduced‐MF plan showed good agreement with the reference plan (target and critical organ dose‐volume region of interest dose differences were within 1% and 2% of prescription dose, respectively).

Discussion

Plan evaluation and acceptance criteria can vary depending on individual clinical expectations and dosimetric quality trade‐offs. With the scheme presented in this paper a planner should be able to efficiently generate a high‐quality plan with efficient delivery time without knowing a good MF beforehand.

Conclusion

A methodology for deriving a reduced MF from the LOT distribution of a high MF treatment plan using the central limit theorem has been presented. A scheme for finding a reduced MF from a set of MFs that results in a plan balanced in both dosimetric quality and treatment delivery efficiency has also been presented.

A fast inverse direct aperture optimization algorithm for intensity-modulated radiation therapy

PURPOSE

The goal of this work was to develop and evaluate a fast inverse direct aperture optimization (FIDAO) algorithm for IMRT treatment planning and plan adaptation.

METHODS

A previously proposed fluence map optimization algorithm called fast inverse dose optimization (FIDO) was extended to optimize the aperture shapes and weights of IMRT beams. FIDO is a very fast fluence map optimization algorithm for IMRT that finds the global minimum using direct matrix inversion without unphysical negative beam weights. In this study, an equivalent second-order Taylor series expansion of the FIDO objective function was used, which allowed for the objective function value and gradient vector to be computed very efficiently during direct aperture optimization, resulting in faster optimization. To evaluate the speed gained with FIDAO, a proof-of-concept algorithm was developed in MATLAB using an interior-point optimization method to solve the reformulated aperture-based FIDO problem. The FIDAO algorithm was used to optimize four step-and-shoot IMRT cases: on the AAPM TG-119 phantom as well as a liver, prostate, and head-and-neck clinical cases. Results were compared with a conventional DAO algorithm that uses the same interior-point method but using the standard formulation of the objective function and its gradient vector.

RESULTS

A substantial gain in optimization speed was obtained with the prototype FIDAO algorithm compared to the conventional DAO algorithm while producing plans of similar quality. The optimization time (number of iterations) for the prototype FIDAO algorithm vs the conventional DAO algorithm was 0.3 s (17) vs 56.7 s (50); 2.0 s (28) vs 134.1 s (57); 2.5 s (26) vs 180.6 s (107); and 6.7 s (20) vs 469.4 s (482) in the TG-119 phantom, liver, prostate, and head-and-neck examples, respectively.

CONCLUSIONS

A new direct aperture optimization algorithm based on FIDO was developed. For the four IMRT test cases examined, this algorithm executed approximately 70-200 times faster without compromising the IMRT plan quality.

Interfacility variation in treatment planning parameters in tomotherapy: field width, pitch, and modulation factor

Several studies have reported changes in dose distribution and delivery time based on the value of specific planning parameters [field width (FW), pitch, and modulation factor (MF)] in tomotherapy. However, the variation in the parameters between different facilities is unknown. The purpose of this study was to determine standard values of the above parameters for cases of head and neck cancer (HNC) and prostate cancer (PC) in Japan. In this survey, a web-based questionnaire was sent to 48 facilities performing radiation therapy with tomotherapy in March 2016. The deadline for data submission was April 2016. In the questionnaire, the values of the planning parameters usually used were requested and 23 responses were received, representing a response rate of 48% (23/48). The FW selected was 2.5 cm in most facilities, and facilities with a tomoEDGE license used dynamic FW rather than fixed FW. Facilities changed the pitch based on FW, dose per fraction, or target offset more frequently in HNC than in PC. In contrast, >50% of the facilities used the magic number proposed by Kissick et al. Median preset MFs (range, min to max) in HNC and PC were 2.4 (1.8-2.8) and 2.0 (1.8-3.0), respectively, and MF values showed large variations between the facilities. Our results are likely to be useful to several facilities designing treatment plans in tomotherapy.

Performance of a hybrid Monte Carlo-Pencil Beam dose algorithm for proton therapy inverse planning

PURPOSE

Analytical algorithms have a limited accuracy when modeling very heterogeneous tumor sites. This work addresses the performance of a hybrid dose optimizer that combines both Monte Carlo (MC) and pencil beam (PB) dose engines to get the best trade-off between speed and accuracy for proton therapy plans.

METHODS

The hybrid algorithm calculates the optimal spot weights (w) by means of an iterative optimization process where the dose at each iteration is computed by using a precomputed dose influence matrix based on the conventional PB plus a correction term c obtained from a MC simulation. Updates of c can be triggered as often as necessary by calling the MC dose engine with the last corrected values of w as input. In order to analyze the performance of the hybrid algorithm against dose calculation errors, it was applied to a simplistic water phantom for which several test cases with different errors were simulated, including proton range uncertainties. Afterwards, the algorithm was used in three clinical cases (prostate, lung, and brain) and benchmarked against full MC-based optimization. The influence of different stopping criteria in the final results was also investigated.

RESULTS

The hybrid algorithm achieved excellent results provided that the estimated range in a homogeneous material is the same for the two dose engines involved, i.e., PB and MC. For the three patient cases, the hybrid plans were clinically equivalent to those obtained with full MC-based optimization. Only a single update of c was needed in the hybrid algorithm to fulfill the clinical dose constraints, which represents an extra computation time to obtain c that ranged from 1 (brain) to 4 min (lung) with respect to the conventional PB-based optimization, and an estimated average gain factor of 14 with respect to full MC-based optimization.

CONCLUSION

The hybrid algorithm provides an improved trade-off between accuracy and speed. This algorithm can be immediately considered as an option for improving dose calculation accuracy of commercial analytical treatment planning systems, without a significant increase in the computation time (≪5 min) with respect to current PB-based optimization.

Evaluation of TomoTherapy dose calculations with intrafractional motion and motion compensation

PURPOSE

Anatomical motion, both cyclical and aperiodic, can impact the dose delivered during external beam radiation. In this work, we evaluate the use of a research version of the clinical TomoTherapy^® dose calculator to calculate dose with intrafraction rigid motion. We also evaluate the feasibility of a method of motion compensation for helical tomotherapy using the jaws and MLC.

METHODS

Treatment plans were created using the TomoTherapy treatment planning system. Dose was recalculated for several simple rigid motion traces including a 4 mm step motion applied either longitudinally or transversely, and a sinusoidal motion. The calculated dose volumes were compared to dose measurements that were performed by translating the phantom with the same motion traces used in the calculations. Measurements were made using film and ion chambers. Finally, the delivery plans were modified to compensate for the motion by sweeping the jaws for longitudinal motion and shifting the MLC leaves for transverse motion, and the calculations and measurements were repeated.

RESULTS

A transverse step motion shifted the dose that was delivered after the step occurred, but otherwise did not impact the dose distribution. Film measurements agreed with dose calculations to within 2%/2 mm for 99% of dose points within the 50% isodose line. A shift in the MLC leaf delivery pattern successfully compensated for the step motion to within the 3 mm accuracy allowed by the finite leaf widths. A longitudinal step motion impacted the dose in the interior of the target volume to a degree that was dependent on the planning field width and step size. Film measurements agreed with dose calculations to within 2%/2 mm for 98% of dose points within the 50% isodose line. Shifts in the jaw position successfully compensated for the longitudinal step motion. Sinusoidal (breathing-like) motion was also studied, with similar results.

CONCLUSIONS

A research version of the clinical TomoTherapy dose calculator has been shown to accurately calculate the dose from treatment plans delivered in the presence of arbitrary rigid motion. Modifications to the delivery plan using jaw and MLC leaf shifts that follow the motion can successfully compensate for the target motion.

[Establishment of the Commissioning Procedure for Modifying the Beam Model of Helical Tomotherapy]

Although much evidence about the helical tomotherapy system are available, there is not a document about the procedure of quality assurance (QA) for changing the beam model. This study establishes the commissioning procedure for modifying the beam model of helical tomotherapy. Firstly, some intensity-modulated radiotherapy (IMRT) plans were created, and compared them with the calculated dose and the measured dose. Secondly, the absorbed doses to water in the machine-specific reference field and the plan-class specific reference field with a protocol in Japan; Standard Dosimetry of Absorbed Dose to Water in External Beam Radiotherapy (Standard Dosimetry 12) were measured. Thirdly, we reconfirmed patient-specific quality assurance. The recommended commissioning procedure after the change of the beam model was shown through three verification processes. This report would be helpful for not only changing the beam model of helical tomotherapy but also introducing Standard Dosimetry 12 to a clinic.

Advanced optimization methods for whole pelvic and local prostate external beam therapy

PURPOSE

Radiation treatment planning inherently involves multiple conflicting planning goals, which makes it a suitable application for multicriteria optimization (MCO). This study investigates a MCO algorithm for VMAT planning (VMAT-MCO) for prostate cancer treatments including pelvic lymph nodes and uses standard inverse VMAT optimization (sVMAT) and Tomotherapy planning as benchmarks.

METHODS

For each of ten prostate cancer patients, a two stage plan was generated, consisting of a stage 1 plan delivering 22Gy to the prostate, and a stage 2 plan delivering 50.4Gy to the lymph nodes and 56Gy to the prostate with a simultaneous integrated boost. The single plans were generated by three planning techniques (VMAT-MCO, sVMAT, Tomotherapy) and subsequently compared with respect to plan quality and planning time efficiency.

RESULTS

Plan quality was similar for all techniques, but sVMAT showed slightly better rectum (on average Dmean -7%) and bowel sparing (Dmean -17%) compared to VMAT-MCO in the whole pelvic treatments. Tomotherapy plans exhibited higher bladder dose (Dmean +42%) in stage 1 and lower rectum dose (Dmean -6%) in stage 2 than VMAT-MCO. Compared to manual planning, the planning time with MCO was reduced up to 12 and 38min for stage 1 and 2 plans, respectively.

CONCLUSION

MCO can generate highly conformal prostate VMAT plans with minimal workload in the settings of prostate-only treatments and prostate plus lymph nodes irradiation. In the whole pelvic plan manual VMAT optimization led to slightly improved OAR sparing over VMAT-MCO, whereas for the primary prostate treatment plan quality was equal.

Monte Carlo simulations of patient dose perturbations in rotational-type radiotherapy due to a transverse magnetic field: a tomotherapy investigation

PURPOSE

Several groups are exploring the integration of magnetic resonance (MR) image guidance with radiotherapy to reduce tumor position uncertainty during photon radiotherapy. The therapeutic gain from reducing tumor position uncertainty using intrafraction MR imaging during radiotherapy could be partially offset if the negative effects of magnetic field-induced dose perturbations are not appreciated or accounted for. The authors hypothesize that a more rotationally symmetric modality such as helical tomotherapy will permit a systematic mediation of these dose perturbations. This investigation offers a unique look at the dose perturbations due to homogeneous transverse magnetic field during the delivery of Tomotherapy(®) Treatment System plans under varying degrees of rotational beamlet symmetry.

METHODS

The authors accurately reproduced treatment plan beamlet and patient configurations using the Monte Carlo code geant4. This code has a thoroughly benchmarked electromagnetic particle transport physics package well-suited for the radiotherapy energy regime. The three approved clinical treatment plans for this study were for a prostate, head and neck, and lung treatment. The dose heterogeneity index metric was used to quantify the effect of the dose perturbations to the target volumes.

RESULTS

The authors demonstrate the ability to reproduce the clinical dose-volume histograms (DVH) to within 4% dose agreement at each DVH point for the target volumes and most planning structures, and therefore, are able to confidently examine the effects of transverse magnetic fields on the plans. The authors investigated field strengths of 0.35, 0.7, 1, 1.5, and 3 T. Changes to the dose heterogeneity index of 0.1% were seen in the prostate and head and neck case, reflecting negligible dose perturbations to the target volumes, a change from 5.5% to 20.1% was observed with the lung case.

CONCLUSIONS

This study demonstrated that the effect of external magnetic fields can be mitigated by exploiting a more rotationally symmetric treatment modality.

A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures

PURPOSE

Real-time adaptive planning and treatment has been infeasible due in part to its high computational complexity. There have been many recent efforts to utilize graphics processing units (GPUs) to accelerate the computational performance and dose accuracy in radiation therapy. Data structure and memory access patterns are the key GPU factors that determine the computational performance and accuracy. In this paper, the authors present a nonvoxel-based (NVB) approach to maximize computational and memory access efficiency and throughput on the GPU.

METHODS

The proposed algorithm employs a ray-tracing mechanism to restructure the 3D data sets computed from the CT anatomy into a nonvoxel-based framework. In a process that takes only a few milliseconds of computing time, the algorithm restructured the data sets by ray-tracing through precalculated CT volumes to realign the coordinate system along the convolution direction, as defined by zenithal and azimuthal angles. During the ray-tracing step, the data were resampled according to radial sampling and parallel ray-spacing parameters making the algorithm independent of the original CT resolution. The nonvoxel-based algorithm presented in this paper also demonstrated a trade-off in computational performance and dose accuracy for different coordinate system configurations. In order to find the best balance between the computed speedup and the accuracy, the authors employed an exhaustive parameter search on all sampling parameters that defined the coordinate system configuration: zenithal, azimuthal, and radial sampling of the convolution algorithm, as well as the parallel ray spacing during ray tracing. The angular sampling parameters were varied between 4 and 48 discrete angles, while both radial sampling and parallel ray spacing were varied from 0.5 to 10 mm. The gamma distribution analysis method (γ) was used to compare the dose distributions using 2% and 2 mm dose difference and distance-to-agreement criteria, respectively. Accuracy was investigated using three distinct phantoms with varied geometries and heterogeneities and on a series of 14 segmented lung CT data sets. Performance gains were calculated using three 256 mm cube homogenous water phantoms, with isotropic voxel dimensions of 1, 2, and 4 mm.

RESULTS

The nonvoxel-based GPU algorithm was independent of the data size and provided significant computational gains over the CPU algorithm for large CT data sizes. The parameter search analysis also showed that the ray combination of 8 zenithal and 8 azimuthal angles along with 1 mm radial sampling and 2 mm parallel ray spacing maintained dose accuracy with greater than 99% of voxels passing the γ test. Combining the acceleration obtained from GPU parallelization with the sampling optimization, the authors achieved a total performance improvement factor of >175 000 when compared to our voxel-based ground truth CPU benchmark and a factor of 20 compared with a voxel-based GPU dose convolution method.

CONCLUSIONS

The nonvoxel-based convolution method yielded substantial performance improvements over a generic GPU implementation, while maintaining accuracy as compared to a CPU computed ground truth dose distribution. Such an algorithm can be a key contribution toward developing tools for adaptive radiation therapy systems.

Dosimetric evaluation of intensity-modulated radiotherapy, volumetric modulated arc therapy, and helical tomotherapy for hippocampal-avoidance whole brain radiotherapy

Background

Whole brain radiotherapy (WBRT) is a vital tool in radiation oncology and beyond, but it can result in adverse health effects such as neurocognitive decline. Hippocampal Avoidance WBRT (HA-WBRT) is a strategy that aims to mitigate the neuro-cognitive side effects of whole brain radiotherapy treatment by sparing the hippocampi while delivering the prescribed dose to the rest of the brain. Several competing modalities capable of delivering HA-WBRT, include: Philips Pinnacle step-and-shoot intensity modulated radiotherapy (IMRT), Varian RapidArc volumetric modulated arc therapy (RapidArc), and helical TomoTherapy (TomoTherapy).

Methods

In this study we compared these methods using 10 patient datasets. Anonymized planning CT (computerized tomography) scans and contour data based on fused MRI images were collected. Three independent planners generated treatment plans for the patients using three modalities, respectively. All treatment plans met the RTOG 0933 criteria for HA-WBRT treatment.

Results

In dosimetric comparisons between the three modalities, TomoTherapy has a significantly superior homogeneity index of 0.15 ± 0.03 compared to the other two modalities (0.28 ± .04, p < .005 for IMRT and 0.22 ± 0.03, p < .005 for RapidArc). RapidArc has the fastest average delivery time of 2.5 min compared to the other modalities (15 min for IMRT and 18 min for TomoTherapy).

Conclusion

TomoTherapy is considered to be the preferred modality for HA-WBRT due to its superior dose distribution. When TomoTherapy is not available or treatment time is a concern, RapidArc can provide sufficient dose distribution meeting RTOG criteria and efficient treatment delivery.

Monte Carlo evaluation of the dose calculation algorithm of TomoTherapy for clinical cases in dynamic jaws mode

PURPOSE

For the TomoTherapy(®) system, longitudinal conformation can be improved by selecting a smaller field width but at the expense of longer treatment time. Recently, the TomoEdge(®) feature has been released with the possibility to move dynamically the jaws at the edges of the target volume, improving longitudinal penumbra and enabling faster treatments. Such delivery scheme requires additional modeling of treatment delivery. Using a previously validated Monte Carlo model (TomoPen), we evaluated the accuracy of the implementation of TomoEdge in the new dose engine of TomoTherapy for 15 clinical cases.

METHODS

TomoPen is based on PENELOPE. Particle tracking in the treatment head is performed almost instantaneously by 1) reading a particle from a phase-space file corresponding to the largest field and 2) correcting the weight of the particle depending on the actual jaw and MLC configurations using Monte Carlo pre-generated data. 15 clinical plans (5 head-and-neck, 5 lung and 5 prostate tumors) planned with TomoEdge and with the last release of the treatment planning system (VoLO(®)) were re-computed with TomoPen. The resulting dose-volume histograms were compared.

RESULTS

Good agreement was achieved overall, with deviations for the target volumes typically within 2% (D95), excepted for small lung tumors (17 cm(3)) where a maximum deviation of 4.4% was observed for D95. The results were consistent with previously reported values for static field widths.

CONCLUSIONS

For the clinical cases considered in the present study, the introduction of TomoEdge did not impact significantly the accuracy of the computed dose distributions.

GPU-based high-performance computing for radiation therapy

Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. The graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of study has been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this paper, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented.

A detailed evaluation of TomoDirect 3DCRT planning for whole-breast radiation therapy

The goal of this work was to develop planning strategies for whole-breast radiotherapy (WBRT) using TomoDirect three-dimensional conformal radiation therapy (TD-3DCRT) and to compare TD-3DCRT with conventional 3DCRT and TD intensity-modulated radiation therapy (TD-IMRT) to evaluate differences in WBRT plan quality. Computed tomography (CT) images of 10 women were used to generate 150 WBRT plans, varying in target structures, field width (FW), pitch, and number of beams. Effects on target and external maximum doses (EMD), organ-at-risk (OAR) doses, and treatment time were assessed for each parameter to establish an optimal planning technique. Using this technique, TD-3DCRT plans were generated and compared with TD-IMRT and standard 3DCRT plans. FW 5.0cm with pitch = 0.250cm significantly decreased EMD without increasing lung V20Gy. Increasing number of beams from 2 to 6 and using an additional breast planning structure decreased EMD though increased lung V20Gy. Changes in pitch had minimal effect on plan metrics. TD-3DCRT plans were subsequently generated using FW 5.0cm, pitch = 0.250cm, and 2 beams, with additional beams or planning structures added to decrease EMD when necessary. TD-3DCRT and TD-IMRT significantly decreased target maximum dose compared to standard 3DCRT. FW 5.0cm with 2 to 6 beams or novel planning structures or both allow for TD-3DCRT WBRT plans with excellent target coverage and OAR doses. TD-3DCRT plans are comparable to plans generated using TD-IMRT and provide an alternative to conventional 3DCRT for WBRT.

Quantitative characterization of tomotherapy MVCT dosimetry

Megavoltage computed tomography (MVCT) is used as image guidance for patient setup in almost every tomotherapy treatment. Frequent use of ionizing radiation for image guidance has raised concern of imaging dose. The purpose of this work is to quantify and characterize tomotherapy MVCT dosimetry. Our dose calculation was based on a commissioned dose engine, and the calculation result was compared with film measurement. We studied dose profiles, center dose, maximal dose, surface dose, and mean dose on homogeneous cylindrical water phantoms of various diameters for various scanning parameters, including 3 different jaw openings (of nominal value J4, J1, and J0.1) and couch speeds (fine, normal, and coarse). The comparison between calculation and film measurement showed good agreement. In particular, the thread pattern on the film of the helical delivery matched very well with calculation. For the J1 jaw and coarse imaging mode, the maximum difference between calculation and measurement was about 6% of the center dose. Calculation on various sizes of synthesized phantoms showed that the center dose decreases almost linearly as the phantom diameter increases, and that the fine mode (couch speed of 4mm/rotation) received twice the dose of the normal mode (couch speed of 8mm/rotation) and 3 times that of the coarse mode (couch speed of 12mm/rotation) as expected. The maximal dose ranged from 100% to ∼200% of the center dose, with increasing ratios for larger phantoms, smaller jaws, and faster couch speed. For all jaw settings and couch speeds, the mean dose and average surface dose vary from 95% to 125% of the center dose with increasing ratios for larger phantoms. We present a quantitative dosimetric characterization of the tomotherapy MVCT in terms of scanning parameters, phantom size, center dose, maximal dose, surface dose, and mean dose. The results can provide an overall picture of dose distribution and a reference data set that enables estimation of CT dose index for the tomotherapy MVCT.

Improvements in dose calculation accuracy for small off-axis targets in high dose per fraction tomotherapy

PURPOSE

A recent field safety notice from TomoTherapy detailed the underdosing of small, off-axis targets when receiving high doses per fraction. This is due to angular undersampling in the dose calculation gantry angles. This study evaluates a correction method to reduce the underdosing, to be implemented in the current version (v4.1) of the TomoTherapy treatment planning software.

METHODS

The correction method, termed "Super Sampling" involved the tripling of the number of gantry angles from which the dose is calculated during optimization and dose calculation. Radiochromic film was used to measure the dose to small targets at various off-axis distances receiving a minimum of 21 Gy in one fraction. Measurements were also performed for single small targets at the center of the Lucy phantom, using radiochromic film and the dose magnifying glass (DMG).

RESULTS

Without super sampling, the peak dose deficit increased from 0% to 18% for a 10 mm target and 0% to 30% for a 5 mm target as off-axis target distances increased from 0 to 16.5 cm. When super sampling was turned on, the dose deficit trend was removed and all peak doses were within 5% of the planned dose. For measurements in the Lucy phantom at 9.7 cm off-axis, the positional and dose magnitude accuracy using super sampling was verified using radiochromic film and the DMG.

CONCLUSIONS

A correction method implemented in the TomoTherapy treatment planning system which triples the angular sampling of the gantry angles used during optimization and dose calculation removes the underdosing for targets as small as 5 mm diameter, up to 16.5 cm off-axis receiving up to 21 Gy.

Validation of GPU based TomoTherapy dose calculation engine

PURPOSE

The graphic processing unit (GPU) based TomoTherapy convolution/superposition(C/S) dose engine (GPU dose engine) achieves a dramatic performance improvement over the traditional CPU-cluster based TomoTherapy dose engine (CPU dose engine). Besides the architecture difference between the GPU and CPU, there are several algorithm changes from the CPU dose engine to the GPU dose engine. These changes made the GPU dose slightly different from the CPU-cluster dose. In order for the commercial release of the GPU dose engine, its accuracy has to be validated.

METHODS

Thirty eight TomoTherapy phantom plans and 19 patient plans were calculated with both dose engines to evaluate the equivalency between the two dose engines. Gamma indices (Γ) were used for the equivalency evaluation. The GPU dose was further verified with the absolute point dose measurement with ion chamber and film measurements for phantom plans. Monte Carlo calculation was used as a reference for both dose engines in the accuracy evaluation in heterogeneous phantom and actual patients.

RESULTS

The GPU dose engine showed excellent agreement with the current CPU dose engine. The majority of cases had over 99.99% of voxels with Γ(1%, 1 mm) < 1. The worst case observed in the phantom had 0.22% voxels violating the criterion. In patient cases, the worst percentage of voxels violating the criterion was 0.57%. For absolute point dose verification, all cases agreed with measurement to within ±3% with average error magnitude within 1%. All cases passed the acceptance criterion that more than 95% of the pixels have Γ(3%, 3 mm) < 1 in film measurement, and the average passing pixel percentage is 98.5%-99%. The GPU dose engine also showed similar degree of accuracy in heterogeneous media as the current TomoTherapy dose engine.

CONCLUSIONS

It is verified and validated that the ultrafast TomoTherapy GPU dose engine can safely replace the existing TomoTherapy cluster based dose engine without degradation in dose accuracy.

Theoretical analysis of the thread effect in helical TomoTherapy

PURPOSE

The longitudinal dose ripple on the off-axis caused by helical radiation delivery, such as the TomoTherapy system, has been observed, and its relation with respect to pitch has been studied with empirically found optimal pitches, 0.86∕n, by Kissick et al. [Med. Phys. 32, 1414-1423 (2005)]. This ripple artifact referred to as the thread effect is periodic in nature and is caused by various periodic factors. In this work, the factors that cause the thread effect were unveiled, including jaw profile divergence, the inverse square law, attenuation, and the cone effect, and their impact on the thread effect were studied.

METHODS

Mathematical formulation for individual and combined factors were set up. Based on theoretical analysis and simulations, optimal pitches that result in local minima of the ripple amplitude with respect to the jaw width and off-axis distance were identified and verified. The effectiveness of optimization in reducing the thread effect were also studied.

RESULTS

Analysis and simulation based on the square-shaped jaw profiles well characterize the thread effect. Simulations based on the real jaw profiles show reduced ripples and very good agreement of optimal pitches compared with those based on the square profiles. The optimal pitches were found to have little jaw width dependence, except for the real jaw profile of small width (1.05 cm). The optimal pitches for the real jaw profile of width 1.05 cm are unidentifiable except for the largest ones, due to the relative smoothness of the jaw profile. With optimized intensity modulation, the thread effect can be largely suppressed. For real jaw profiles, the optimal pitches with or without dose optimization do not change much. The numbers 0.86∕n found by Kissick et al. well approximate the optimal pitches for off-axis distance of 5 cm. However, optimal pitches are not universal for different off-axis distances: they decrease as the off-axis distance increases.

CONCLUSIONS

The thread effect can be well explained by the proposed model. Optimization can largely reduce the thread effect. However, an optimal pitch reduces the ripple much easier especially when optimization is limited by many constraints. The optimal pitches predicted by the proposed model could be used as a reference for pitch selection regardless the tumor is at large or small off-axis distance.

Bridging the gap between IMRT and VMAT: dense angularly sampled and sparse intensity modulated radiation therapy

PURPOSE

To propose an alternative radiation therapy (RT) planning and delivery scheme with optimal angular beam sampling and intrabeam modulation for improved dose distribution while maintaining high delivery efficiency.

METHODS

In the proposed approach, coined as dense angularly sampled and sparse intensity modulated RT (DASSIM-RT), a large number of beam angles are used to increase the angular sampling, leading to potentially more conformal dose distributions as compared to conventional IMRT. At the same time, intensity modulation of the incident beams is simplified to eliminate the dispensable segments, compensating the increase in delivery time caused by the increased number of beams and facilitating the plan delivery. In a sense, the proposed approach shifts and transforms, in an optimal fashion, some of the beam segments in conventional IMRT to the added beams. For newly available digital accelerators, the DASSIM-RT delivery can be made very efficient by concatenating the beams so that they can be delivered sequentially without operator's intervention. Different from VMAT, the level of intensity modulation in DASSIS-RT is field specific and optimized to meet the need of each beam direction. Three clinical cases (a head and neck (HN) case, a pancreas case, and a lung case) are used to evaluate the proposed RT scheme. DASSIM-RT, VMAT, and conventional IMRT plans are compared quantitatively in terms of the conformality index (CI) and delivery efficiency.

RESULTS

Plan quality improves generally with the number and intensity modulation of the incident beams. For a fixed number of beams or fixed level of intensity modulation, the improvement saturates after the intensity modulation or number of beams reaches to a certain level. An interplay between the two variables is observed and the saturation point depends on the values of both variables. For all the cases studied here, the CI of DASSIM-RT with 15 beams and 5 intensity levels (0.90, 0.79, and 0.84 for the HN, pancreas, and lung cases, respectively) is similar with that of conventional IMRT with seven beams and ten intensity levels (0.88, 0.79, and 0.83) and is higher than that of single-arc VMAT (0.75, 0.75, and 0.82). It is also found that the DASSIM-RT plans generally have better sparing of organs-at-risk than IMRT plans. It is estimated that the dose delivery time of DASSIM-RT with 15 beams and 5 intensity levels is about 4.5, 4.4, and 4.2 min for the HN, pancreas, and lung case, respectively, similar to that of IMRT plans with 7 beams and 10 intensity levels.

CONCLUSION

DASSIS-RT bridges the gap between IMRT and VMAT and allows optimal sampling of angular space and intrabeam modulation, thus it provides improved conformity in dose distributions while maintaining high delivery efficiency.

Dynamic tomotherapy delivery

PURPOSE

Several dynamic techniques are introduced to speed up TomoTherapy delivery and improve longitudinal conformity. These techniques include dynamic jaws, dynamic couch, and their combinations.

METHODS

In general, dynamic jaws techniques allow jaws to move during a treatment. On the one hand, the jaws open wide to increase efficiency and thus reduce beam-on time. On the other hand, the jaws can close and follow the target border when sharp penumbra is required near the superior and inferior borders of tumor sites, which results in improved longitudinal dose conformity. The main purpose of the dynamic couch technique is to move the couch as fast as possible at variable speed to reduce beam-on time. Delivering most conformal dose as fast as possible requires a combination of dynamic jaws and dynamic couch techniques (DJDC). Motions of the jaws and couch are determined from the longitudinal fluence profile, which is calculated from an optimized leaf sinogram of small jaw width regular delivery or running start and stop delivery (RSS). We focused on RSS and DJDC in this study and also discussed other delivery techniques.

RESULTS

Several conceptual cases are simulated to compare different delivery techniques. The results show that beam-on time can be reduced by about 60% compared to regular delivery with a 2.5 cm jaw width (REG 2.5 cm) for these cases and arbitrary longitudinal fluence profiles can be delivered. Two clinical cases, a prostate and a head-and-neck case, with different delivery techniques are calculated. The results show that plan quality yielded by DJDC with a maximum 5.0 cm jaw width is overall comparable to or better than that of the existing REG 2.5 cm.

CONCLUSIONS

The DVH comparisons show better critical structure avoidance with the dynamic techniques. At the same time, beam-on time is reduced by about one half compared to REG 2.5 cm. Dynamic delivery techniques provide users more tools to speed up delivery and/or improve plan quality.

GPU computing in medical physics: a review

The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction, dose calculation and treatment plan optimization, and image processing.

A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation

Targeting at the development of an accurate and efficient dose calculation engine for online adaptive radiotherapy, we have implemented a finite-size pencil beam (FSPB) algorithm with a 3D-density correction method on graphics processing unit (GPU). This new GPU-based dose engine is built on our previously published ultrafast FSPB computational framework (Gu et al 2009 Phys. Med. Biol. 54 6287-97). Dosimetric evaluations against Monte Carlo dose calculations are conducted on ten IMRT treatment plans (five head-and-neck cases and five lung cases). For all cases, there is improvement with the 3D-density correction over the conventional FSPB algorithm and for most cases the improvement is significant. Regarding the efficiency, because of the appropriate arrangement of memory access and the usage of GPU intrinsic functions, the dose calculation for an IMRT plan can be accomplished well within 1 s (except for one case) with this new GPU-based FSPB algorithm. Compared to the previous GPU-based FSPB algorithm without 3D-density correction, this new algorithm, though slightly sacrificing the computational efficiency (∼5-15% lower), has significantly improved the dose calculation accuracy, making it more suitable for online IMRT replanning.

Ultrafast convolution/superposition using tabulated and exponential kernels on GPU

PURPOSE

Collapsed-cone convolution/superposition (CCCS) dose calculation is the workhorse for IMRT dose calculation. The authors present a novel algorithm for computing CCCS dose on the modern graphic processing unit (GPU).

METHODS

The GPU algorithm includes a novel TERMA calculation that has no write-conflicts and has linear computation complexity. The CCCS algorithm uses either tabulated or exponential cumulative-cumulative kernels (CCKs) as reported in literature. The authors have demonstrated that the use of exponential kernels can reduce the computation complexity by order of a dimension and achieve excellent accuracy. Special attentions are paid to the unique architecture of GPU, especially the memory accessing pattern, which increases performance by more than tenfold.

RESULTS

As a result, the tabulated kernel implementation in GPU is two to three times faster than other GPU implementations reported in literature. The implementation of CCCS showed significant speedup on GPU over single core CPU. On tabulated CCK, speedups as high as 70 are observed; on exponential CCK, speedups as high as 90 are observed.

CONCLUSIONS

Overall, the GPU algorithm using exponential CCK is 1000-3000 times faster over a highly optimized single-threaded CPU implementation using tabulated CCK, while the dose differences are within 0.5% and 0.5 mm. This ultrafast CCCS algorithm will allow many time-sensitive applications to use accurate dose calculation.