Improving IMRT dose accuracy via deliverable Monte Carlo optimization for the treatment of head and neck cancer patients

Efficiency enhancements of a Monte Carlo beamlet based treatment planning process: implementation and parameter study

Objective.The computational effort to perform beamlet calculation, plan optimization and final dose calculation of a treatment planning process (TPP) generating intensity modulated treatment plans is enormous, especially if Monte Carlo (MC) simulations are used for dose calculation. The goal of this work is to improve the computational efficiency of a fully MC based TPP for static and dynamic photon, electron and mixed photon-electron treatment techniques by implementing multiple methods and studying the influence of their parameters.Approach.A framework is implemented calculating MC beamlets efficiently in parallel on each available CPU core. The user can specify the desired statistical uncertainty of the beamlets, a fractional sparse dose threshold to save beamlets in a sparse format and minimal distances to the PTV surface from which 2 × 2 × 2 = 8 (medium) or even 4 × 4 × 4 = 64 (large) voxels are merged. The compromise between final plan quality and computational efficiency of beamlet calculation and optimization is studied for several parameter values to find a reasonable trade-off. For this purpose, four clinical and one academic case are considered with different treatment techniques.Main results.Setting the statistical uncertainty to 5% (photon beamlets) and 15% (electron beamlets), the fractional sparse dose threshold relative to the maximal beamlet dose to 0.1% and minimal distances for medium and large voxels to the PTV to 1 cm and 2 cm, respectively, does not lead to substantial degradation in final plan quality compared to using 2.5% (photon beamlets) and 5% (electron beamlets) statistical uncertainty and no sparse format nor voxel merging. Only OAR sparing is slightly degraded. Furthermore, computation times are reduced by about 58% (photon beamlets), 88% (electron beamlets) and 96% (optimization).Significance.Several methods are implemented improving computational efficiency of beamlet calculation and plan optimization of a fully MC based TPP without substantial degradation in final plan quality.

Maintaining dosimetric quality when switching to a Monte Carlo dose engine for head and neck volumetric-modulated arc therapy planning

Head and neck cancers present challenges in radiation treatment planning due to the large number of critical structures near the target(s) and highly heterogeneous tissue composition. While Monte Carlo (MC) dose calculations currently offer the most accurate approximation of dose deposition in tissue, the switch to MC presents challenges in preserving the parameters of care. The differences in dose‐to‐tissue were widely discussed in the literature, but mostly in the context of recalculating the existing plans rather than reoptimizing with the MC dose engine. Also, the target dose homogeneity received less attention. We adhere to strict dose homogeneity objectives in clinical practice. In this study, we started with 21 clinical volumetric‐modulated arc therapy (VMAT) plans previously developed in Pinnacle treatment planning system. Those plans were recalculated “as is” with RayStation (RS) MC algorithm and then reoptimized in RS with both collapsed cone (CC) and MC algorithms. MC statistical uncertainty (0.3%) was selected carefully to balance the dose computation time (1–2 min) with the planning target volume (PTV) dose‐volume histogram (DVH) shape approaching that of a “noise‐free” calculation. When the hot spot in head and neck MC‐based treatment planning is defined as dose to 0.03 cc, it is exceedingly difficult to limit it to 105% of the prescription dose, as we were used to with the CC algorithm. The average hot spot after optimization and calculation with RS MC was statistically significantly higher compared to Pinnacle and RS CC algorithms by 1.2 and 1.0 %, respectively. The 95% confidence interval (CI) observed in this study suggests that in most cases a hot spot of ≤107% is achievable. Compared to the 95% CI for the previous clinical plans recalculated with RS MC “as is” (upper limit 108%), in real terms this result is at least as good or better than the historic plans.

EGSnrc application for IMRT planning

The aim of this study was to describe a detailed instruction of intensity modulated radiotherapy (IMRT) planning simulation using BEAMnrc-DOSXYZnrc code system (EGSnrc package) and present a new graphical user interface based on MATLAB code (The MathWorks) to combine more than one. 3ddose file which were obtained from the IMRT plan. This study was performed in four phases: the commissioning of Varian Clinac iX6 MV, the simulation of IMRT planning in EGSnrc, the creation of in-house VDOSE GUI, and the analysis of the isodose contour and dose volume histogram (DVH) curve from several beam angles. The plan paramaters in sequence and control point files were extracted from the planning data in Tan Tock Seng Hospital Singapore (multileaf collimator (MLC) leaf positions - bank A and bank B, gantry angles, coordinate of isocenters, and MU indexes). VDOSE GUI which was created in this study can display the distribution dose curve in each slice and beam angle. Dose distributions from various MLC settings and beam angles yield different dose distributions even though they used the same number of simulated particles. This was due to the differences in the MLC leaf openings in every field. The value of the relative dose error between the two dose ditributions for "body" was 51.23 %. The Monte Carlo (MC) data was normalized with the maximum dose but the analytical anisotropic algorithm (AAA) data was normalized by the dose in the isocenter. In this study, we have presented a Monte Carlo simulation framework for IMRT dose calculation using DOSXYZnrc source 21. Further studies are needed in conducting IMRT simulations using EGSnrc to minimize the different dose error and dose volume histogram deviation.

Comparison of volumetric-modulated arc therapy and intensity-modulated radiation therapy prostate cancer plans accounting for cold spots

This study compared dosimetric indices of volumetric-modulated arc therapy (VMAT) with intensity-modulated radiation therapy (IMRT) accounting for cold spots in prostate cancer plans. IMRT plans were retrospectively generated from 30 prostate cancer patients with ten cases for each risk group, who received VMAT plans. The mean, maximum, and minimum doses, and conformity and homogeneity indexes were evaluated for planning target volume (PTV) and the mean dose and V20-V70 for organs at risk (OAR) including the rectum, bladder, right and left femoral heads, and rectum overlapped with PTV (ROP) regions. The numbers and volume percentages of cold spots within PTVs and ROP regions were measured using in-house software. Three-dimensional probabilistic distributions of the probability and distributions of cold spots were generated using a centroid matching technique for visualization and analysis. There was a statistically better dose conformity in the PTV, rectum, and bladder dose-sparing in VMAT compared to that in the IMRT plans, whereas VMAT had statistically worse target dose homogeneity, and right and left femoral head dose-sparing than those of the IMRT plans. The average volume percentage of cold spots per PTV for the VMAT was 4.37 ± 2.68%, which was smaller than the 5.72 ± 1.84% observed for IMRT plans (P = 0.007). The volume percentage of cold spots per ROP for the VMAT did not significantly differ from those for the IMRT plans. Compared with IMRT, the VMAT plans achieved better PTV dose conformity, OAR dose-sparing, and smaller cold spots in the treatment of prostate cancer.

A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model

PURPOSE

The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on penelope and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field.

METHODS

penelope was first translated from fortran to c++ and the result was confirmed to produce equivalent results to the original code. The c++ code was then adapted to cuda in a workflow optimized for GPU architecture. The original code was expanded to include voxelized transport with Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gpenelope highly user-friendly. Moreover, the vendor-provided MRIdian head model was incorporated into the code in an effort to apply gpenelope as both an accurate and rapid dose validation system. A set of experimental measurements were performed on the MRIdian system to examine the accuracy of both the head model and gpenelope. Ultimately, gpenelope was applied toward independent validation of patient doses calculated by MRIdian's kmc.

RESULTS

An acceleration factor of 152 was achieved in comparison to the original single-thread fortran implementation with the original accuracy being preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen(1), mediastinum (1), and breast (1), the MRIdian dose calculation engine agrees with gpenelope with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm).

CONCLUSIONS

A Monte Carlo simulation platform was developed based on a GPU- accelerated version of penelope. This platform was used to validate that both the vendor-provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems.

Integral Dose-Based Inverse Optimization May Reduce Side Effects in Radiotherapy of Prostate Carcinoma

PURPOSE

The purpose of this work is to apply a novel inverse optimization approach, based on utilization of quantitative imaging information in the optimization function, to prostate carcinoma.

MATERIALS AND METHODS

This new inverse optimization algorithm relies upon quantitative information derived from computed tomography (CT) imaging studies. The Hounsfield numbers of the CT voxels are converted to physical density, which in turn is used to calculate voxel mass and the corresponding integral dose, by summation over the product of dose and mass in each dose voxel. This integral dose is used for plan optimization through its global minimization. The optimization results are compared to the optimization results derived from most commonly used dose-volume-based inverse optimization, where objective functions are formed as summation over all dose voxels of the squared differences between voxel doses and user specified doses. The data from 25 prostate plans were optimized with dose-volume histogram (DVH) and integral dose (energy) minimization objective functions. The results obtained with the energy- and DVH-based optimization schemes were studied through commonly used dosimetric indices (DIs). Statistical equivalence tests were further performed to establish population-based significance results.

RESULTS

Both DVH- and energy-based plans for each case were normalized so that 95% of the planning target volume receives the prescription dose. The average differences for the rectum and bladder DIs ranged from 1.6 to 25%, where the energy-based quantities were lower. For both femoral heads, the energy-based optimization-derived doses were lower on average by 32%. The statistical tests demonstrated that the significant differences in the tallied dose indices range from 2.7% to more than 50% for rectum, bladder, and femoral heads.

CONCLUSION

For majority of the clinically relevant dosimetric quantities, energy-based inverse optimization performs better than the standard of care DVH-based optimization in prostate carcinoma. The population averaged statistically significant differences range from ~3 to ~50%. Therefore, this newly proposed optimization approach, incorporating explicitly quantitative imaging information in the inverse optimization function, holds potential for further reduction of complication rates in prostate cancer.

Dosimetric evaluation of a simple planning method for improving intensity-modulated radiotherapy for stage III lung cancer

This study aimed to evaluate the dosimetric outcomes of a base-dose-plan-compensation (BDPC) planning method for improving intensity-modulated radiotherapy (IMRT) for stage III lung cancer. For each of the thirteen included patients, three types of planning methods were applied to obtain clinically acceptable plans: (1) the conventional optimization method (CO); (2) a split-target optimization method (STO), in which the optimization objectives were set higher dose for the target with lung density; (3) the BDPC method, which compensated for the optimization-convergence error by further optimization based on the CO plan. The CO, STO and BDPC methods were then compared regarding conformity index (CI), homogeneity index (HI) of the target, organs at risk (OARs) sparing and monitor units (MUs). The BDPC method provided better HI/CI by 54%/7% on average compared to the CO method and by 38%/3% compared to the STO method. The BDPC method also spared most of the OARs by up to 9%. The average MUs of the CO, STO and BDPC plans were 890, 937 and 1023, respectively. Our results indicated that the BDPC method can effectively improve the dose distribution in IMRT for stage III lung cancer, at the expense of more MUs.

Flattening Filter-Free Beams in Intensity-Modulated Radiotherapy and Volumetric Modulated Arc Therapy for Sinonasal Cancer

PURPOSE

To evaluate the dosimetric impacts of flattening filter-free (FFF) beams in intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) for sinonasal cancer.

METHODS

For fourteen cases, IMRT and VMAT planning was performed using 6-MV photon beams with both conventional flattened and FFF modes. The four types of plans were compared in terms of target dose homogeneity and conformity, organ-at-risk (OAR) sparing, number of monitor units (MUs) per fraction, treatment time and pure beam-on time.

RESULTS

FFF beams led to comparable target dose homogeneity, conformity, increased number of MUs and lower doses to the spinal cord, brainstem and normal tissue, compared with flattened beams in both IMRT and VMAT. FFF beams in IMRT resulted in improvements by up to 5.4% for sparing of the contralateral optic structures, with shortened treatment time by 9.5%. However, FFF beams provided comparable overall OAR sparing and treatment time in VMAT. With FFF mode, VMAT yielded inferior homogeneity and superior conformity compared with IMRT, with comparable overall OAR sparing and significantly shorter treatment time.

CONCLUSIONS

Using FFF beams in IMRT and VMAT is feasible for the treatment of sinonasal cancer. Our results suggest that the delivery mode of FFF beams may play an encouraging role with better sparing of contralateral optic OARs and treatment efficiency in IMRT, but yield comparable results in VMAT.

A simple optimization approach for improving target dose homogeneity in intensity-modulated radiotherapy for sinonasal cancer

Homogeneous target dose distribution in intensity-modulated radiotherapy (IMRT) for sinonasal cancer (SNC) is challenging to achieve. To solve this problem, we established and evaluated a basal-dose-compensation (BDC) optimization approach, in which the treatment plan is further optimized based on the initial plans. Generally acceptable initial IMRT plans for thirteen patients were created and further optimized individually by (1) the BDC approach and (2) a local-dose-control (LDC) approach, in which the initial plan is further optimized by addressing hot and cold spots. We compared the plan qualities, total planning time and monitor units (MUs) among the initial, BDC, LDC IMRT plans and volumetric modulated arc therapy (VMAT) plans. The BDC approach provided significantly superior dose homogeneity/conformity by 23%-48%/6%-9% compared with both the initial and LDC IMRT plans, as well as reduced doses to the organs at risk (OARs) by up to 18%, with acceptable MU numbers. Compared with VMAT, BDC IMRT yielded superior homogeneity, inferior conformity and comparable overall OAR sparing. The planning of BDC, LDC IMRT and VMAT required 30, 59 and 58 minutes on average, respectively. Our results indicated that the BDC optimization approach can achieve significantly better dose distributions with shorter planning time in the IMRT for SNC.

Dose-mass inverse optimization for minimally moving thoracic lesions

In the past decade, several different radiotherapy treatment plan evaluation and optimization schemes have been proposed as viable approaches, aiming for dose escalation or an increase of healthy tissue sparing. In particular, it has been argued that dose-mass plan evaluation and treatment plan optimization might be viable alternatives to the standard of care, which is realized through dose-volume evaluation and optimization. The purpose of this investigation is to apply dose-mass optimization to a cohort of lung cancer patients and compare the achievable healthy tissue sparing to that one achievable through dose-volume optimization. Fourteen non-small cell lung cancer (NSCLC) patient plans were studied retrospectively. The range of tumor motion was less than 0.5 cm and motion management in the treatment planning process was not considered. For each case, dose-volume (DV)-based and dose-mass (DM)-based optimization was performed. Nine-field step-and-shoot IMRT was used, with all of the optimization parameters kept the same between DV and DM optimizations. Commonly used dosimetric indices (DIs) such as dose to 1% the spinal cord volume, dose to 50% of the esophageal volume, and doses to 20 and 30% of healthy lung volumes were used for cross-comparison. Similarly, mass-based indices (MIs), such as doses to 20 and 30% of healthy lung masses, 1% of spinal cord mass, and 33% of heart mass, were also tallied. Statistical equivalence tests were performed to quantify the findings for the entire patient cohort. Both DV and DM plans for each case were normalized such that 95% of the planning target volume received the prescribed dose. DM optimization resulted in more organs at risk (OAR) sparing than DV optimization. The average sparing of cord, heart, and esophagus was 23, 4, and 6%, respectively. For the majority of the DIs, DM optimization resulted in lower lung doses. On average, the doses to 20 and 30% of healthy lung were lower by approximately 3 and 4%, whereas lung volumes receiving 2000 and 3000 cGy were lower by 3 and 2%, respectively. The behavior of MIs was very similar. The statistical analyses of the results again indicated better healthy anatomical structure sparing with DM optimization. The presented findings indicate that dose-mass-based optimization results in statistically significant OAR sparing as compared to dose-volume-based optimization for NSCLC. However, the sparing is case-dependent and it is not observed for all tallied dosimetric endpoints.

Improving target dose coverage and organ-at-risk sparing in intensity-modulated radiotherapy of advanced laryngeal cancer by a simple optimization technique

OBJECTIVE

To evaluate a simple optimization technique intended to improve planning target volume (PTV) dose coverage and organ-at-risk (OAR) sparing in intensity-modulated radiotherapy (IMRT) of advanced laryngeal cancer.

METHODS

Generally acceptable initial IMRT plans were generated for 12 patients and were improved individually by the following two techniques: (1) base dose function-based (BDF) technique, in which the treatment plans were reoptimized based on the initial IMRT plans; (2) dose-controlling structure-based (DCS) technique, in which the initial IMRT plans were reoptimized by adding constraints for hot and cold spots. The initial, BDF and DCS IMRT plans and additionally generated volumetric modulated arc therapy (VMAT) plans were compared concerning homogeneity index (HI) and conformity index (CI) of PTVs prescribed at 70 Gy/60 Gy (PTV70/PTV60), OAR sparing, monitor units (MUs) per fraction and total planning time.

RESULTS

Compared with the initial IMRT and DCS IMRT plans, the BDF technique provided superior HI/CI, by approximately 19-37%/4-11%, and lower doses to most OARs, by approximately 1-7%, except for the comparable HI of PTV60 to DCS IMRT plans. Compared with VMAT plans, the BDF technique provided comparable HI, CI and most-OAR sparing, except for the superior HI of PTV70, by approximately 13%. The BDF technique produced more MUs and reduced the planning time.

CONCLUSION

The BDF optimization technique for IMRT of advanced laryngeal cancer can improve target dose homogeneity and conformity, spare most OARs and is efficient.

ADVANCES IN KNOWLEDGE

A novel optimization technique for improving IMRT was assessed and found to be effective and efficient.

Improving target coverage and organ-at-risk sparing in intensity-modulated radiotherapy for cervical oesophageal cancer using a simple optimisation method

PURPOSE

To assess the performance of a simple optimisation method for improving target coverage and organ-at-risk (OAR) sparing in intensity-modulated radiotherapy (IMRT) for cervical oesophageal cancer.

METHODS

For 20 selected patients, clinically acceptable original IMRT plans (Original plans) were created, and two optimisation methods were adopted to improve the plans: 1) a base dose function (BDF)-based method, in which the treatment plans were re-optimised based on the original plans, and 2) a dose-controlling structure (DCS)-based method, in which the original plans were re-optimised by assigning additional constraints for hot and cold spots. The Original, BDF-based and DCS-based plans were compared with regard to target dose homogeneity, conformity, OAR sparing, planning time and monitor units (MUs). Dosimetric verifications were performed and delivery times were recorded for the BDF-based and DCS-based plans.

RESULTS

The BDF-based plans provided significantly superior dose homogeneity and conformity compared with both the DCS-based and Original plans. The BDF-based method further reduced the doses delivered to the OARs by approximately 1-3%. The re-optimisation time was reduced by approximately 28%, but the MUs and delivery time were slightly increased. All verification tests were passed and no significant differences were found.

CONCLUSION

The BDF-based method for the optimisation of IMRT for cervical oesophageal cancer can achieve significantly better dose distributions with better planning efficiency at the expense of slightly more MUs.

Dosimetric Evaluation of a Simple Planning Technique for Improving Intensity-Modulated Radiotherapy for Nasopharyngeal Cancer

PURPOSE

To evaluate the dosimetric outcomes of a simple planning technique for improving intensity-modulated radiotherapy (IMRT) for nasopharyngeal cancer (NPC).

METHODS

For 39 NPC cases, generally acceptable original plans were generated and were improved by the two planning techniques, respectively: (1) a basal-dose-compensation (BDC) technique, in which the treatment plans were re-optimized based on the original plans; (2) a local-dose-control (LDC) technique, in which the original plans were re-optimized with constraints for hot and cold spots. The BDC, original, and LDC plans were then compared regarding homogeneity index (HI) and conformity index (CI) of planning target volumes (PTVs), organ-at-risk (OAR) sparing and monitor units (MUs) per fraction. The whole planning times were also compared between the BDC and LDC plans.

RESULTS

The BDC plans had superior HIs / CIs, by 13-24% / 3-243%, respectively, over the original plans. Compared to the LDC plans, the BDC plans provided better HIs only for PTVnx (the PTV of nasopharyngeal primary tumor) by 11% and better CIs for all PTVs by 2-134%. The BDC technique spared most OARs, by 1-9%. The average MUs of the BDC, original, and LDC plans were 2149, 2068 and 2179, respectively. The average whole planning times were 48 and 69 minutes for the BDC and LDC plans, respectively.

CONCLUSIONS

For the IMRT of nasopharyngeal cancer, the BDC planning technique can improve target dose homogeneity, conformity and OAR sparing, with better planning efficiency.

The effect of gantry spacing resolution on plan quality in a single modulated arc optimization

Volumetric‐modulated arc technique (VMAT) is an efficient form of IMRT delivery. It is advantageous over conventional IMRT in terms of treatment delivery time. This study investigates the relation between the number of segments and plan quality in VMAT optimization for a single modulated arc. Five prostate, five lung, and five head‐and‐neck (HN) patient plans were studied retrospectively. For each case, four VMAT plans were generated. The plans differed only in the number of control points used in the optimization process. The control points were spaced 2°, 3°, 4°, and 6° apart, respectively. All of the optimization parameters were the same among the four schemes. The 2° spacing plan was used as a reference to which the other three plans were compared. The plan quality was assessed by comparison of dose indices (DIs) and generalized equivalent uniform doses (gEUDs) for targets and critical structures. All optimization schemes generated clinically acceptable plans. The differences between the majority of reference and compared DIs and gEUDs were within 3%. DIs and gEUDs which differed in excess of 3% corresponded to dose levels well below the organ tolerances. The DI and the gEUD differences increased with an increase in plan complexity from prostates to HNs. Optimization with gantry spacing resolution of 4° seems to be a very balanced alternative between plan quality and plan complexity.

PACS number: 87.55.de

Lung dose for minimally moving thoracic lesions treated with respiration gating

PURPOSE

To evaluate incidental doses to benign lung tissue for patients with minimally moving lung lesions treated with respiratory gating.

METHODS AND MATERIALS

Seventeen lung patient plans were studied retrospectively. Tumor motion was less than 5 mm in all cases. For each patient, mid-ventilation (MidVen) and mid-inhalation (MidInh) breathing phases were reconstructed. The MidInh phase was centered on the end-of-inhale (EOI) phase within a 30% gating window. Planning target volumes, heart, and spinal cord were delineated on the MidVen phase and transferred to the MidInh phase. Lungs were contoured separately on each phase. Intensity-modulated radiotherapy plans were generated on the MidVen phases. The plans were transferred to the MidInh phase, and doses were recomputed. The evaluation metric was based on dose indices, volume indices, generalized equivalent uniform doses, and mass indices for targets and critical structures. Statistical tests were used to establish the significance of the differences between the reference (MidVen) and compared (MidInh) dose distributions.

RESULTS

Statistical tests demonstrated that the indices evaluated for targets, cord, and heart differed by within 2.3%. The index differences in the lungs, however, are in excess of 6%, indicating the potentially achievable lung sparing and/or dose escalation.

CONCLUSIONS

Respiratory gating is a clinical option for patients with minimally moving lung lesions treated at EOI. Gating will be more beneficial for larger tumors, since dose escalation in those cases will result in a larger increase in the tumor control probability.

Applying graphics processor units to Monte Carlo dose calculation in radiation therapy

We investigate the potential in using of using a graphics processor unit (GPU) for Monte-Carlo (MC)-based radiation dose calculations. The percent depth dose (PDD) of photons in a medium with known absorption and scattering coefficients is computed using a MC simulation running on both a standard CPU and a GPU. We demonstrate that the GPU's capability for massive parallel processing provides a significant acceleration in the MC calculation, and offers a significant advantage for distributed stochastic simulations on a single computer. Harnessing this potential of GPUs will help in the early adoption of MC for routine planning in a clinical environment.

Intensity- and energy-modulated electron radiotherapy by means of an xMLC for head and neck shallow tumors

The purpose of this paper is to assess the feasibility of delivering intensity- and energy-modulated electron radiation treatment (MERT) by a photon multileaf collimator (xMLC) and to evaluate the improvements obtained in shallow head and neck (HN) tumors. Four HN patient cases covering different clinical situations were planned by MERT, which used an in-house treatment planning system that utilized Monte Carlo dose calculation. The cases included one oronasal, two parotid and one middle ear tumors. The resulting dose-volume histograms were compared with those obtained from conventional photon and electron treatment techniques in our clinic, which included IMRT, electron beam and mixed beams, most of them using fixed-thickness bolus. Experimental verification was performed with plane-parallel ionization chambers for absolute dose verification, and a PTW ionization chamber array and radiochromic film for relative dosimetry. A MC-based treatment planning system for target with compromised volumes in depth and laterally has been validated. A quality assurance protocol for individual MERT plans was launched. Relative MC dose distributions showed a high agreement with film measurements and absolute ion chamber dose measurements performed at a reference point agreed with MC calculations within 2% in all cases. Clinically acceptable PTV coverage and organ-at-risk sparing were achieved by using the proposed MERT approach. MERT treatment plans, based on delivery of intensity-modulated electron beam using the xMLC, for superficial head and neck tumors, demonstrated comparable or improved PTV dose homogeneity with significantly lower dose to normal tissues. The clinical implementation of this technique will be able to offer a viable alternative for the treatment of shallow head and neck tumors.

Algorithm for correcting optimization convergence errors in Eclipse

IMRT plans generated in Eclipse use a fast algorithm to evaluate dose for optimization and a more accurate algorithm for a final dose calculation, the Analytical Anisotropic Algorithm. The use of a fast optimization algorithm introduces optimization convergence errors into an IMRT plan. Eclipse has a feature where optimization may be performed on top of an existing base plan. This feature allows for the possibility of arriving at a recursive solution to optimization that relies on the accuracy of the final dose calculation algorithm and not the optimizer algorithm. When an IMRT plan is used as a base plan for a second optimization, the second optimization can compensate for heterogeneity and modulator errors in the original base plan. Plans with the same field arrangement as the initial base plan may be added together by adding the initial plan optimal fluence to the dose correcting plan optimal fluence.A simple procedure to correct for optimization errors is presented that may be implemented in the Eclipse treatment planning system, along with an Excel spreadsheet to add optimized fluence maps together.

Volumetric modulated Arc therapy (VMAT) treatment planning for superficial tumors

The physician's planning objective is often a uniform dose distribution throughout the planning target volume (PTV), including superficial PTVs on or near the surface of a patient's body. Varian's Eclipse treatment planning system uses a progressive resolution optimizer (PRO), version 8.2.23, for RapidArc dynamic multileaf collimator volumetric modulated arc therapy planning. Because the PRO is a fast optimizer, optimization convergence errors (OCEs) produce dose nonuniformity in the superficial area of the PTV. We present a postsurgical cranial case demonstrating the recursive method our clinic uses to produce RapidArc treatment plans. The initial RapidArc treatment plan generated using one 360 degrees arc resulted in substantial dose nonuniformity in the superficial section of the PTV. We demonstrate the use of multiple arcs to produce improved dose uniformity in this region. We also compare the results of this superficial dose compensation method to the results of a recursive method of dose correction that we developed in-house to correct optimization convergence errors in static intensity-modulated radiation therapy treatment plans. The results show that up to 4 arcs may be necessary to provide uniform dose to the surface of the PTV with the current version of the PRO.

Monte Carlo dose verification of prostate patients treated with simultaneous integrated boost intensity modulated radiation therapy

BACKGROUND

To evaluate the dosimetric differences between Superposition/Convolution (SC) and Monte Carlo (MC) calculated dose distributions for simultaneous integrated boost (SIB) prostate cancer intensity modulated radiotherapy (IMRT) compared to experimental (film) measurements and the implications for clinical treatments.

METHODS

Twenty-two prostate patients treated with an in-house SIB-IMRT protocol were selected. SC-based plans used for treatment were re-evaluated with EGS4-based MC calculations for treatment verification. Accuracy was evaluated with-respect-to film-based dosimetry. Comparisons used gamma (gamma)-index, distance-to-agreement (DTA), and superimposed dose distributions. The treatment plans were also compared based on dose-volume indices and 3-D gamma index for targets and critical structures.

RESULTS

Flat-phantom comparisons demonstrated that the MC algorithm predicted measurements better than the SC algorithm. The average PTVprostate D98 agreement between SC and MC was 1.2% +/- 1.1. For rectum, the average differences in SC and MC calculated D50 ranged from -3.6% to 3.4%. For small bowel, there were up to 30.2% +/- 40.7 (range: 0.2%, 115%) differences between SC and MC calculated average D50 index. For femurs, the differences in average D50 reached up to 8.6% +/- 3.6 (range: 1.2%, 14.5%). For PTVprostate and PTVnodes, the average gamma scores were >95.0%.

CONCLUSION

MC agrees better with film measurements than SC. Although, on average, SC-calculated doses agreed with MC calculations within the targets within 2%, there were deviations up to 5% for some patient's treatment plans. For some patients, the magnitude of such deviations might decrease the intended target dose levels that are required for the treatment protocol, placing the patients in different dose levels that do not satisfy the protocol dose requirements.

Coverage-based treatment planning: optimizing the IMRT PTV to meet a CTV coverage criterion

This work demonstrates an iterative approach-referred to as coverage-based treatment planning-designed to produce treatment plans that ensure target coverage for a specified percentage of setup errors. In this approach the clinical target volume to planning target volume (CTV-to-PTV) margin is iteratively adjusted until the specified CTV coverage is achieved. The advantage of this approach is that it automatically compensates for the dosimetric margin around the CTV, i.e., the extra margin that is created when the dose distribution extends beyond the PTV. When applied to 27 prostate plans, this approach reduced the average CTV-to-PTV margin from 5 to 2.8 mm. This reduction in PTV size produced a corresponding decrease in the volume of normal tissue receiving high dose. The total volume of tissue receiving > or =65 Gy was reduced on average by 19.3% or about 48 cc. Individual reductions varied from 8.7% to 28.6%. The volume of bladder receiving > or =60 Gy was reduced on average by 5.6% (reductions for individuals varied from 1.7% to 10.6%), and the volume of periprostatic rectum receiving > or =65 Gy was reduced on average by 4.9% (reductions for individuals varied from 0.9% to 12.3%). The iterative method proposed here represents a step toward a probabilistic treatment planning algorithm which can generate dose distributions (i.e., treated volumes) that closely approximate a specified level of coverage in the presence of geometric uncertainties. The general principles of coverage-based treatment planning are applicable to arbitrary treatment sites and delivery techniques. Importantly, observed deviations between coverage implied by specified CTV-to-PTV margins and coverage achieved by a given treatment plan imply a generic need to perform coverage probability analysis on a per-plan basis to ensure that the desired level of coverage is achieved.

Evaluation of dose prediction errors and optimization convergence errors of deliverable-based head-and-neck IMRT plans computed with a superposition/convolution dose algorithm

The purpose of this study is to evaluate dose prediction errors (DPEs) and optimization convergence errors (OCEs) resulting from use of a superposition/convolution dose calculation algorithm in deliverable intensity-modulated radiation therapy (IMRT) optimization for head-and-neck (HN) patients. Thirteen HN IMRT patient plans were retrospectively reoptimized. The IMRT optimization was performed in three sequential steps: (1) fast optimization in which an initial nondeliverable IMRT solution was achieved and then converted to multileaf collimator (MLC) leaf sequences; (2) mixed deliverable optimization that used a Monte Carlo (MC) algorithm to account for the incident photon fluence modulation by the MLC, whereas a superposition/convolution (SC) dose calculation algorithm was utilized for the patient dose calculations; and (3) MC deliverable-based optimization in which both fluence and patient dose calculations were performed with a MC algorithm. DPEs of the mixed method were quantified by evaluating the differences between the mixed optimization SC dose result and a MC dose recalculation of the mixed optimization solution. OCEs of the mixed method were quantified by evaluating the differences between the MC recalculation of the mixed optimization solution and the final MC optimization solution. The results were analyzed through dose volume indices derived from the cumulative dose-volume histograms for selected anatomic structures. Statistical equivalence tests were used to determine the significance of the DPEs and the OCEs. Furthermore, a correlation analysis between DPEs and OCEs was performed. The evaluated DPEs were within +/- 2.8% while the OCEs were within 5.5%, indicating that OCEs can be clinically significant even when DPEs are clinically insignificant. The full MC-dose-based optimization reduced normal tissue dose by as much as 8.5% compared with the mixed-method optimization results. The DPEs and the OCEs in the targets had correlation coefficients greater than 0.71, and there was no correlation for the organs at risk. Because full MC-based optimization results in lower normal tissue doses, this method proves advantageous for HN IMRT optimization.

The effect of statistical noise on IMRT plan quality and convergence for MC-based and MC-correction-based optimized treatment plans

Monte Carlo (MC) is rarely used for IMRT plan optimization outside of research centres due to the extensive computational resources or long computation times required to complete the process. Time can be reduced by degrading the statistical precision of the MC dose calculation used within the optimization loop. However, this eventually introduces optimization convergence errors (OCEs). This study determines the statistical noise levels tolerated during MC-IMRT optimization under the condition that the optimized plan has OCEs <100 cGy (1.5% of the prescription dose) for MC-optimized IMRT treatment plans.Seven-field prostate IMRT treatment plans for 10 prostate patients are used in this study. Pre-optimization is performed for deliverable beams with a pencil-beam (PB) dose algorithm. Further deliverable-based optimization proceeds using: (1) MC-based optimization, where dose is recomputed with MC after each intensity update or (2) a once-corrected (OC) MC-hybrid optimization, where a MC dose computation defines beam-by-beam dose correction matrices that are used during a PB-based optimization. Optimizations are performed with nominal per beam MC statistical precisions of 2, 5, 8, 10, 15, and 20%. Following optimizer convergence, beams are re-computed with MC using 2% per beam nominal statistical precision and the 2 PTV and 10 OAR dose indices used in the optimization objective function are tallied. For both the MC-optimization and OC-optimization methods, statistical equivalence tests found that OCEs are less than 1.5% of the prescription dose for plans optimized with nominal statistical uncertainties of up to 10% per beam. The achieved statistical uncertainty in the patient for the 10% per beam simulations from the combination of the 7 beams is ~3% with respect to maximum dose for voxels with D>0.5D(max). The MC dose computation time for the OC-optimization is only 6.2 minutes on a single 3 Ghz processor with results clinically equivalent to high precision MC computations.

Computational Challenges for Image-Guided Radiation Therapy: Framework and Current Research

It is arguable that the imaging and delivery hardware necessary for delivering real-time adaptive image-guided radiotherapy is available on high-end linear accelerators. Robust and computationally efficient software is the limiting factor in achieving highly accurate and precise radiotherapy to the constantly changing anatomy of a cancer patient. The limitations are not caused by the availability of algorithms but rather issues of reliability, integration, and calculation time. However, each of the software components is an active area of research and development at academic and commercial centers. This article describes the software solutions in 4 broad areas: deformable image registration, adaptive replanning, real-time image guidance, and dose calculation and accumulation. Given the pace of technological advancement, the integration of these software solutions to develop real-time adaptive image-guided radiotherapy and the associated challenges they bring will be implemented to varying degrees by all major manufacturers over the coming years.

Performance of a hybrid MC dose algorithm for IMRT optimization dose evaluation

This paper presents a hybrid intensity modulated radiation therapy (IMRT) optimization strategy which combines the speed of pencil beam (PB) and the accuracy of Monte Carlo (MC) dose calculations. After an initial deliverable-based optimization using a PB algorithm, doses are recomputed using the VMC++ MC code to determine dose correction factors, which are then utilized during further PB-based optimization. The hybrid method is benchmarked with respect to full MC deliverable-based optimization for ten prostate and ten head-and-neck IMRT plans. Final optimized plans are compared in terms of dose-volume indices used for the plan optimization. Dose prediction errors (DPEs) and optimization convergence errors (OCEs) at intermediate steps of the hybrid sequence are evaluated. The hybrid method is found to produce optimized plans that are clinically equivalent to full MC-based optimization, yet requires only 40% of the number of MC dose calculations. With the hybrid strategy presented here, MC-based optimization results are achieved in 35 min or less on a modest computing cluster. While the initial PB-deliverable-based optimization is found to have DPEs and OCEs of up to 3 Gy relative to the 65-73 Gy prescription doses, application of the first MC correction reduces the average DPEs to less than 0.3 Gy for the prostate plans and less than 0.06 Gy for the head and neck plans. The maximum observed DPE or OCE is 0.7 Gy after 1 MC dose correction, indicating that a single MC dose calculation correction might be sufficient for IMRT optimization.

Evaluation of clinical margins via simulation of patient setup errors in prostate IMRT treatment plans

This work evaluates: (i) the size of random and systematic setup errors that can be absorbed by 5 mm clinical target volume (CTV) to planning target volume (PTV) margins in prostate intensity modulated radiation therapy (IMRT); (ii) agreement between simulation results and published margin recipes; and (iii) whether shifting contours with respect to a static dose distribution accurately predicts dose coverage due to setup errors. In 27 IMRT treatment plans created with 5 mm CTV-to-PTV margins, random setup errors with standard deviations (SDs) of 1.5, 3, 5 and 10 mm were simulated by fluence convolution. Systematic errors with identical SDs were simulated using two methods: (a) shifting the isocenter and recomputing dose (isocenter shift), and (b) shifting patient contours with respect to the static dose distribution (contour shift). Maximum tolerated setup errors were evaluated such that 90% of plans had target coverage equal to the planned PTV coverage. For coverage criteria consistent with published margin formulas, plans with 5 mm margins were found to absorb combined random and systematic SDs = 3 mm. Published recipes require margins of 8-10 mm for 3 mm SDs. For the prostate IMRT cases presented here a 5 mm margin would suffice, indicating that published recipes may be pessimistic. We found significant errors in individual plan doses given by the contour shift method. However, dose population plots (DPPs) given by the contour shift method agreed with the isocenter shift method for all structures except the nodal CTV and small bowel. For the nodal CTV, contour shift DPP differences were due to the structure moving outside the patient. Small bowel DPP errors were an artifact of large relative differences at low doses. Estimating individual plan doses by shifting contours with respect to a static dose distribution is not recommended. However, approximating DPPs is acceptable, provided care is taken with structures such as the nodal CTV which lie close to the surface.