Reliable detection of fluence anomalies in EPID-based IMRT pretreatment quality assurance using pixel intensity deviations.

PURPOSE: This work uses repeat images of intensity modulated radiation therapy (IMRT) fields to quantify fluence anomalies (i.e., delivery errors) that can be reliably detected in electronic portal images used for IMRT pretreatment quality assurance. METHODS: Repeat images of 11 clinical IMRT fields are acquired on a Varian Trilogy linear accelerator at energies of 6 MV and 18 MV. Acquired images are corrected for output variations and registered to minimize the impact of linear accelerator and electronic portal imaging device (EPID) positioning deviations. Detection studies are performed in which rectangular anomalies of various sizes are inserted into the images. The performance of detection strategies based on pixel intensity deviations (PIDs) and gamma indices is evaluated using receiver operating characteristic analysis. RESULTS: Residual differences between registered images are due to interfraction positional deviations of jaws and multileaf collimator leaves, plus imager noise. Positional deviations produce large intensity differences that degrade anomaly detection. Gradient effects are suppressed in PIDs using gradient scaling. Background noise is suppressed using median filtering. In the majority of images, PID-based detection strategies can reliably detect fluence anomalies of ≥5% in ∼1 mm(2) areas and ≥2% in ∼20 mm(2) areas. CONCLUSIONS: The ability to detect small dose differences (≤2%) depends strongly on the level of background noise. This in turn depends on the accuracy of image registration, the quality of the reference image, and field properties. The longer term aim of this work is to develop accurate and reliable methods of detecting IMRT delivery errors and variations. The ability to resolve small anomalies will allow the accuracy of advanced treatment techniques, such as image guided, adaptive, and arc therapies, to be quantified.

A method to evaluate dose errors introduced by dose mapping processes for mass conserving deformations.

PURPOSE: To present a method to evaluate the dose mapping error introduced by the dose mapping process. In addition, apply the method to evaluate the dose mapping error introduced by the 4D dose calculation process implemented in a research version of commercial treatment planning system for a patient case. METHODS: The average dose accumulated in a finite volume should be unchanged when the dose delivered to one anatomic instance of that volume is mapped to a different anatomic instance-provided that the tissue deformation between the anatomic instances is mass conserving. The average dose to a finite volume on image S is defined as d(S)=e(s)/m(S), where e(S) is the energy deposited in the mass m(S) contained in the volume. Since mass and energy should be conserved, when d(S) is mapped to an image R(d(S→R)=d(R)), the mean dose mapping error is defined as Δd(m)=|d(R)-d(S)|=|e(R)/m(R)-e(S)/m(S)|, where the e(R) and e(S) are integral doses (energy deposited), and m(R) and m(S) are the masses within the region of interest (ROI) on image R and the corresponding ROI on image S, where R and S are the two anatomic instances from the same patient. Alternatively, application of simple differential propagation yields the differential dose mapping error, Δd(d)=|∂d∂e*Δe+∂d∂m*Δm|=|(e(S)-e(R))m(R)-(m(S)-m(R))m(R) (2)*e(R)|=α|d(R)-d(S)| with α=m(S)/m(R). A 4D treatment plan on a ten-phase 4D-CT lung patient is used to demonstrate the dose mapping error evaluations for a patient case, in which the accumulated dose, D(R)=∑(S=0) (9)d(S→R), and associated error values (ΔD(m) and ΔD(d)) are calculated for a uniformly spaced set of ROIs. RESULTS: For the single sample patient dose distribution, the average accumulated differential dose mapping error is 4.3%, the average absolute differential dose mapping error is 10.8%, and the average accumulated mean dose mapping error is 5.0%. Accumulated differential dose mapping errors within the gross tumor volume (GTV) and planning target volume (PTV) are lower, 0.73% and 2.33%, respectively. CONCLUSIONS: A method has been presented to evaluate the dose mapping error introduced by the dose mapping process. This method has been applied to evaluate the 4D dose calculation process implemented in a commercial treatment planning system. The method could potentially be developed as a fully-automatic QA method in image guided adaptive radiation therapy (IGART).

Coverage optimized planning: probabilistic treatment planning based on dose coverage histogram criteria.

This work (i) proposes a probabilistic treatment planning framework, termed coverage optimized planning (COP), based on dose coverage histogram (DCH) criteria; (ii) describes a concrete proof-of-concept implementation of COP within the PINNACLE treatment planning system; and (iii) for a set of 28 prostate anatomies, compares COP plans generated with this implementation to traditional PTV-based plans generated with planning criteria approximating those in the high dose arm of the Radiation Therapy Oncology Group 0126 protocol. Let Dv denote the dose delivered to fractional volume v of a structure. In conventional intensity modulated radiation therapy planning, Dv has a unique value derived from the static (planned) dose distribution. In the presence of geometric uncertainties (e.g., setup errors) Dv assumes a range of values. The DCH is the complementary cumulative distribution function of D(v+). DCHs are similar to dose volume histograms (DVHs). Whereas a DVH plots volume v versus dose D, a DCH plots coverage probability Q versus D. For a given patient, Q is the probability (i.e., percentage of geometric uncertainties) for which the realized value of Dv exceeds D. PTV-based treatment plans can be converted to COP plans by replacing DVH optimization criteria with corresponding DCH criteria. In this approach, PTVs and planning organ at risk volumes are discarded, and DCH criteria are instead applied directly to clinical target volumes (CTVs) or organs at risk (OARs). Plans are optimized using a similar strategy as for DVH criteria. The specific implementation is described. COP was found to produce better plans than standard PTV-based plans, in the following sense. While target OAR dose tradeoff curves were equivalent to those for PTV-based plans, COP plans were able to exploit slack in OAR doses, i.e., cases where OAR doses were below their optimization limits, to increase target coverage. Specifically, because COP plans were not constrained by a predefined PTV, they were able to provide wider dosimetric margins around the CTV, by pushing OAR doses up to, but not beyond, their optimization limits. COP plans demonstrated improved target coverage when averaged over all 28 prostate anatomies, indicating that the COP approach can provide benefits for many patients. However, the degree to which slack OAR doses can be exploited to increase target coverage will vary according to the individual patient anatomy. The proof-of-concept COP implementation investigated here utilized a probabilistic DCH criteria only for the CTV minimum dose criterion. All other optimization criteria were conventional DVH criteria. In a mature COP implementation, all optimization criteria will be DCH criteria, enabling direct planning control over probabilistic dose distributions. Further research is necessary to determine the benefits of COP planning, in terms of tumor control probability and/or normal tissue complication probabilities.

A pseudoinverse deformation vector field generator and its applications.

PURPOSE: To present, implement, and test a self-consistent pseudoinverse displacement vector field (PIDVF) generator, which preserves the location of information mapped back-and-forth between image sets. METHODS: The algorithm is an iterative scheme based on nearest neighbor interpolation and a subsequent iterative search. Performance of the algorithm is benchmarked using a lung 4DCT data set with six CT images from different breathing phases and eight CT images for a single prostrate patient acquired on different days. A diffeomorphic deformable image registration is used to validate our PIDVFs. Additionally, the PIDVF is used to measure the self-consistency of two nondiffeomorphic algorithms which do not use a self-consistency constraint: The ITK Demons algorithm for the lung patient images and an in-house B-Spline algorithm for the prostate patient images. Both Demons and B-Spline have been QAed through contour comparison. Self-consistency is determined by using a DIR to generate a displacement vector field (DVF) between reference image R and study image S (DVF(R-S)). The same DIR is used to generate DVF(S-R). Additionally, our PIDVF generator is used to create PIDVF(S-R). Back-and-forth mapping of a set of points (used as surrogates of contours) using DVF(R-S) and DVF(S-R) is compared to back-and-forth mapping performed with DVF(R-S) and PIDVF(S-R). The Euclidean distances between the original unmapped points and the mapped points are used as a self-consistency measure. RESULTS: Test results demonstrate that the consistency error observed in back-and-forth mappings can be reduced two to nine times in point mapping and 1.5 to three times in dose mapping when the PIDVF is used in place of the B-Spline algorithm. These self-consistency improvements are not affected by the exchanging of R and S. It is also demonstrated that differences between DVF(S-R) and PIDVF(S-R) can be used as a criteria to check the quality of the DVF. CONCLUSIONS: Use of DVF and its PIDVF will improve the self-consistency of points, contour, and dose mappings in image guided adaptive therapy.

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.

A method to improve accuracy and precision of water surface identification for photon depth dose measurements.

The objective of this study is to present a method to reduce the setup error inherent in clinical depth dose measurements and, in doing so, to improve entrance dosimetry measurement reliability. Ionization chamber (IC) depth dose measurements are acquired with the depth scan extended into the air above the water surface. An inflection region is obtained in each resulting percent depth ionization (PDI) curve that can be matched against other measurements or to an inflection region obtained from an analogous Monte Carlo (MC) simulation. Measurements are made with various field sizes for the 6 and 18 MV photon beams, with and without a Pb foil in the beam, to determine the sensitivity of the dose inflection region to the beam conditions. The offset between reference and test data set inflection regions is quantified using two separate methods. When comparing sets of measured data, maxima in the second derivative of ionization are compared. When comparing measured data to MC simulation, the offset that minimizes the sum of squared differences between the reference and test curves in the ionization inflection region is found. These methods can be used to quantify the offset between an initial setup (test) position and the true surface (reference) position. The ionization inflection location is found to be insensitive to changes in field size, electron contamination, and beam energy. Data from a single reference condition should be sufficient to identify the surface location. The method of determining IC offsets is general and should be applicable to any IC and other radiation sources. The measurement method could reduce the time and effort required in the initial IC setup at a water surface as setup errors can be corrected offline. Given a reliable set of reference data to compare with, this method could increase the ability of quality assurance (QA) measurements to detect discrepancies in beam output as opposed to discrepancies in IC localization. Application of the measurement method standardizes the procedure for localizing cylindrical ICs at a water surface and thereby improves the reliability of measurements taken with these devices at all depths.

A comparison of HDR brachytherapy and IMRT techniques for dose escalation in prostate cancer: a radiobiological modeling study.

A course of one to three large fractions of high dose rate (HDR) interstitial brachytherapy is an attractive alternative to intensity modulated radiation therapy (IMRT) for delivering boost doses to the prostate in combination with additional external beam irradiation for intermediate risk disease. The purpose of this work is to quantitatively compare single-fraction HDR boosts to biologically equivalent fractionated IMRT boosts, assuming idealized image guided delivery (igIMRT) and conventional delivery (cIMRT). For nine prostate patients, both seven-field IMRT and HDR boosts were planned. The linear-quadratic model was used to compute biologically equivalent dose prescriptions. The cIMRT plan was evaluated as a static plan and with simulated random and setup errors. The authors conclude that HDR delivery produces a therapeutic ratio which is significantly better than the conventional IMRT and comparable to or better than the igIMRT delivery. For the HDR, the rectal gBEUD analysis is strongly influenced by high dose DVH tails. A saturation BED, beyond which no further injury can occur, must be assumed. Modeling of organ motion uncertainties yields mean outcomes similar to static plan outcomes.

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.

Evaluation of dosimetric margins in prostate IMRT treatment plans.

This work introduces a new concept--the dosimetric margin distribution (DMD)--and uses it to explain the sensitivity of a group of prostate IMRT treatment plans to patient setup errors. Prior work simulated the effect of setup errors on 27 prostate IMRT treatment plans and found the plans could tolerate larger setup errors than predicted by the van Herk margin formula. The conjectured reason for this disagreement was a breakdown in van Herk's assumption that the planned dose distribution conforms perfectly to target structures. To resolve the disagreement, this work employed the same 27 plans to evaluate the actual margin distributions that exist between: (i) the clinical target volume (CTV) and planning target volume (PTV) and (ii) the CTV and PTV minimum dose isodose surface. These distributions were evaluated for both prostate and nodal targets. Distribution (ii) is the DMD. The dosimetric margin in a given direction determines the probability that the CTV will be underdosed due to setup errors in that direction. Averaging over 4 pi sr gives the overall probability of CTV coverage. Minimum doses for prostate and nodal PTVs were obtained from dose volume histograms. Corresponding isodose surfaces were created and converted to regions of interest (ROIs). CTV, PTV, and isodose ROIs were saved as mesh files and then imported into a computational geometry application which calculated distances between meshes (i.e., margins) in 614 discrete directions covering 4 pi sr in 10 deg increments. Measured prostate CTV-to-PTV margins were close to the nominal value of 0.5 cm specified in the treatment planning protocol. However, depending on direction, prostate dosimetric margins ranged from 0.5 to 3 cm, reflecting the imperfect conformance of the planned dose distribution to the prostate PTV. For the nodal CTV, the nominal CTV-to-PTV margin employed in treatment planning was again 0.5 cm. However, due to the planning protocol, the nodal PTV follows the surface of the nodal CTV in several places, ensuring that there is no room for rigid body motion of the nodal CTV inside the nodal PTV. Measured nodal CTV-to-PTV margins were therefore zero, while nodal dosimetric margins ranged from 0.2 to 2.8 cm. Prostate and nodal target coverage were found to be well correlated with the measured DMDs, thereby resolving the apparent disagreement with our prior results. The principal conclusion is that target coverage in the presence of setup errors should be evaluated using the DMD, rather than the CTV-to-PTV margin distribution. The DMD is a useful planning metric, which generalizes the ICRU conformity index. DMDs could vary with number of beams, beam arrangements, TPS, and treatment site.

Quantification of the impact of MLC modeling and tissue heterogeneities on dynamic IMRT dose calculations.

This study quantifies the dose prediction errors (DPEs) in dynamic IMRT dose calculations resulting from (a) use of an intensity matrix to estimate the multi-leaf collimator (MLC) modulated photon fluence (DPE(IGfluence) instead of an explicit MLC particle transport, and (b) handling of tissue heterogeneities (DPE(hetero)) by superposition/convolution (SC) and pencil beam (PB) dose calculation algorithms. Monte Carlo (MC) computed doses are used as reference standards. Eighteen head-and-neck dynamic MLC IMRT treatment plans are investigated. DPEs are evaluated via comparing the dose received by 98% of the GTV (GTV D 98%), the CTV D 95%, the nodal D 90%, the cord and the brainstem D 02%, the parotid D 50%, the parotid mean dose (D (Mean)), and generalized equivalent uniform doses (gEUDs) for the above structures. For the MC-generated intensity grids, DPE(IGfluence) is within +/- 2.1% for all targets and critical structures. The SC algorithm DPE(hetero) is within +/- 3% for 98.3% of the indices tallied, and within +/- 3.4% for all of the tallied indices. The PB algorithm DPE(hetero) is within +/- 3% for 92% of the tallied indices. Statistical equivalence tests indicate that PB DPE(hetero) requires a +/- 3.6% interval to state equivalence with the MC standard, while the intervals are < 1.5% for SC DPE(hetero) and DPE(IGfluence). Overall, these results indicate that SC and MC IMRT dose calculations which use MC-derived intensity matrices for fluence prediction do not introduce significant dose errors compared with full Monte Carlo dose computations; however, PB algorithms may result in clinically significant dose deviations.

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.

A strategy to correct for intrafraction target translation in conformal prostate radiotherapy: simulation results.

A strategy is proposed in which intrafraction internal target translation is corrected for by repositioning the multileaf collimator position aperture to conform to the new target pose in the beam projection, and the beam monitor units are adjusted to account for the change in the geometric relationship between the target and the beam. The purpose of this study was to investigate the dosimetric stability of the prostate and critical structures in the presence of internal target translation using the dynamic compensation strategy. Twenty-five previously treated prostate cancer patients were replanned using a four-field conformal technique to deliver 72 Gy to 95% of the planning target volume (PTV). Internal translation was introduced by displacing the prostate PTV (no rotation or deformation was considered). Thirty-six randomly selected isotropic displacements of magnitude 0.5, 1.0, 1.5 and 2.0 cm were sampled for each patient, for a total of 3600 errors. Due to their anatomic relation to the prostate, the rectum and bladder contours were also moved with the same magnitude and direction as the prostate. The dynamic compensation strategy was used to correct each of these errors by conforming the beam apertures to the new target pose and adjusting the monitor units using inverse-square and off-axis factor corrections. The dynamic compensation strategy plans were then compared to the original treatment plans via dose-volume histogram (DVH) analysis. Changes of more than 5% of the prescription dose (3.6 Gy) were deemed clinically significant. Compared to the original treatment plans, the dynamic compensation strategy produced small discrepancies in isodose distributions and DVH analyses for all structures considered apart from the femoral heads. These differences increased with the magnitude of the internal motion. Coverage of the PTV was excellent: D5, D95, and Dmean were not increased or decreased by more than 5% of the prescription dose for any of the 3600 simulated internal motion shifts. Dose increases for adjacent organs at risk were rare. D33 of the rectum and D20 of the bladder were increased by more than 5% of the prescription dose in 9 and 1 instances of the 3600 sampled internal motion shifts, respectively. Dmean of the right femoral head increased by more than 5% of the prescription dose in 651 (18%) internal motion shifts, predominantly due to the projection of the lateral beams through the femoral head for anterior prostate motion. However, D2 was not increased by more than 5% for any of the internal motion shifts. These data demonstrate the robustness of the proposed dynamic compensation strategy for correction of internal motion in conformal prostate radiotherapy, with minimal deviation from the original treatment plans even for errors exceeding those commonly encountered in the clinic. The compensation strategy could be performed automatically with appropriate enhancements to available delivery software.

Convolution method and CTV-to-PTV margins for finite fractions and small systematic errors.

The van Herk margin formula (VHMF) relies on the accuracy of the convolution method (CM) to determine clinical target volume (CTV) to planning target volume (PTV) margins. This work (1) evaluates the accuracy of the CM and VHMF as a function of the number of fractions N and other parameters, and (2) proposes an alternative margin algorithm which ensures target coverage for a wider range of parameter values. Dose coverage was evaluated for a spherical target with uniform margin, using the same simplified dose model and CTV coverage criterion as were used in development of the VHMF. Systematic and random setup errors were assumed to be normally distributed with standard deviations Sigma and sigma. For clinically relevant combinations of sigma, Sigma and N, margins were determined by requiring that 90% of treatment course simulations have a CTV minimum dose greater than or equal to the static PTV minimum dose. Simulation results were compared with the VHMF and the alternative margin algorithm. The CM and VHMF were found to be accurate for parameter values satisfying the approximate criterion: sigma[1 - gammaN/25] < 0.2, where gamma = Sigma/sigma. They were found to be inaccurate for sigma[1 - gammaN/25] > 0.2, because they failed to account for the non-negligible dose variability associated with random setup errors. These criteria are applicable when sigma greater than or approximately egual sigma(P), where sigma(P) = 0.32 cm is the standard deviation of the normal dose penumbra. (Qualitative behaviour of the CM and VHMF will remain the same, though the criteria might vary if sigma(P) takes values other than 0.32 cm.) When sigma << sigma(P), dose variability due to random setup errors becomes negligible, and the CM and VHMF are valid regardless of the values of Sigma and N. When sigma greater than or approximately egual sigma(P), consistent with the above criteria, it was found that the VHMF can underestimate margins for large sigma, small Sigma and small N. A potential consequence of this underestimate is that the CTV minimum dose can fall below its planned value in more than the prescribed 10% of treatments. The proposed alternative margin algorithm provides better margin estimates and CTV coverage over the parameter ranges examined here. This algorithm is not amenable to expression as a simple formula (e.g., as a linear combination of Sigma and sigma). However, it can be easily calculated. For 0.1 cm < or = sigma < or = 0.75 cm, 0 < or = gamma < or = 1 and 5 < or = N < or = 30, the VHMF underestimates margins by as much as 33%. With the alternative margin algorithm, the maximum underestimate is 7%. These results suggest that the VHMF should be used with caution for hypofractionated treatment and in adaptive therapy.

Analytic IMRT dose calculations utilizing Monte Carlo to predict MLC fluence modulation.

A hybrid dose-computation method is designed which accurately accounts for multileaf collimator (MLC)-induced intensity modulation in intensity modulated radiation therapy (IMRT) dose calculations. The method employs Monte Carlo (MC) modeling to determine the fluence modulation caused by the delivery of dynamic or multisegmental (step-and-shoot) MLC fields, and a conventional dose-computation algorithm to estimate the delivered dose to a phantom or a patient. Thus, it determines the IMRT fluence prediction accuracy achievable by analytic methods in the limit that the analytic method includes all details of the MLC leaf transport and scatter. The hybrid method is validated and benchmarked by comparison with in-phantom film dose measurements, as well as dose calculations from two in-house, and two commercial treatment planning system analytic fluence estimation methods. All computation methods utilize the same dose algorithm to calculate dose to a phantom, varying only in the estimation of the MLC modulation of the incident photon energy fluence. Gamma analysis, with respect to measured two-dimensional (2D) dose planes, is used to benchmark each algorithm's performance. The analyzed fields include static and dynamic test patterns, as well as fields from ten DMLC IMRT treatment plans (79 fields) and five SMLC treatment plans (29 fields). The test fields (fully closed MLC, picket fence, sliding windows of different size, and leaf-tip profiles) cover the extremes of MLC usage during IMRT, while the patient fields represent realistic clinical conditions. Of the methods tested, the hybrid method most accurately reproduces measurements. For the hybrid method, 79 of 79 DMLC field calculations have gamma < 1 (3%/3 mm) for more than 95% of the points (per field) while for SMLC fields, 27 of 29 pass the same criteria. The analytic energy fluence estimation methods show inferior pass rates, with 76 of 79 DMLC and 24 of 29 SMLC fields having more than 95% of the test points with gamma < or = 1 (3%/3 mm). Paired one-way ANOVA tests of the gamma analysis results found that the hybrid method better predicts measurements in terms of both the fraction of points with gamma < or = 1 and the average gamma for both 2%/2 mm and 3%/3 mm criteria. These results quantify the enhancement in accuracy in IMRT dose calculations when MC is used to model the MLC field modulation.

A dynamic compensation strategy to correct patient-positioning errors in conformal prostate radiotherapy.

Traditionally, pretreatment detected patient-positioning errors have been corrected by repositioning the couch to align the patient to the treatment beam. We investigated an alternative strategy: aligning the beam to the patient by repositioning the dynamic multileaf collimator and adjusting the beam weights, termed dynamic compensation. The purpose of this study was to determine the geometric range of positioning errors for which the dynamic compensation method is valid in prostate cancer patients treated with three-dimensional conformal radiotherapy. Twenty-five previously treated prostate cancer patients were replanned using a four-field technique to deliver 72 Gy to 95% of the planning target volume (PTV). Patient-positioning errors were introduced by shifting the patient reference frame with respect to the treatment isocenter. Thirty-six randomly selected isotropic displacements with magnitudes of 1.0, 2.0, 4.0, 6.0, 8.0, and 10.0 cm were sampled for each patient, for a total of 5400 errors. Dynamic compensation was used to correct each of these errors by conforming the beam apertures to the new target position and adjusting the monitor units using inverse-square and off-axis factor corrections. The dynamic compensation plans were then compared with the original treatment plans via dose-volume histogram (DVH) analysis. Changes of more than 5% of the prescription dose, 3.6 Gy, were deemed significant. Compared with the original treatment plans, dynamic compensation produced small discrepancies in isodose distributions and DVH analyses. These differences increased with the magnitudes of the initial patient-positioning errors. Coverage of the PTV was excellent: D95 and Dmean were not increased or decreased by more than 5% of the prescription dose, and D5 was not decreased by more than 5% of the prescription dose for any of the 5400 simulated positioning errors. D5 was increased by more than 5% of the prescription dose in only three of the 5400 positioning errors, all three occurring with a positioning error of 10.0 cm. Dose increases for adjacent organs at risk were more common. D33 of the rectum and the periprostatic rectum was increased by more than 5% of the prescription dose in 235 (4.4%) and 212 (3.9%) of the 5400 positioning errors, respectively. D10 of the right femoral head increased by more than 5% of the prescription dose in 444 (8.2%) positioning errors, and the degree of change was highly related to individual patient anatomy and simulation position. For the bladder D20, there were three increases of more than 5% of the prescription dose. These data demonstrate the robustness of dynamic compensation for correction of patient-positioning errors in four-field conformal prostate radiotherapy, with minimal deviation from the original treatment plans even for errors greatly exceeding those commonly encountered in the clinic. Dynamic compensation can be performed remotely, thus eliminating errors that may result from unnecessary increases in treatment time or from secondary patient motion induced by couch motion during the repositioning process. Further, the ability of dynamic compensation to correct large positioning errors has implications for the accuracy necessary during the initial patient setup and, hence, patient throughput for prostate radiotherapy.

Clinical comparison of head and neck and prostate IMRT plans using absorbed dose to medium and absorbed dose to water.

Conventional photon radiation therapy dose-calculation algorithms typically compute and report the absorbed dose to water (D(w)). Monte Carlo (MC) dose-calculation algorithms, however, generally compute and report the absorbed dose to the material (D(m)). As MC-calculation algorithms are being introduced into routine clinical usage, the question as to whether there is a clinically significant difference between D(w) and D(m) remains. The goal of the current study is to assess the differences between dose-volume indices for D(m) and D(w) MC-calculated IMRT plans. Ten head-and-neck (H&N) and ten prostate cancer patients were selected for this study. MC calculations were performed using an EGS4-based system. Converting D(m) to D(w) for MC-based calculations was accomplished as a post-MC calculation process. D(w) and D(m) results for target and critical structures were evaluated using the dose-volume-based indices. For H&N IMRT plans, systematic differences between dose-volume indices computed with D(w) and D(m) were up to 2.9% for the PTV prescription dose (D(98)), up to 5.8% for maximum (D(2)) dose to the PTV and up to 2.7% for the critical structure dose indices. For prostate IMRT plans, the systematic differences between D(w)- and D(m)-based computed indices were up to 3.5% for the prescription dose (D(98)) to the PTVs, up to 2.0% for the maximum (D(2)) dose to the PTVs and up to 8% for the femoral heads due to their higher water/bone mass stopping power ratio. This study showed that converting D(m) to D(w) in MC-calculated IMRT treatment plans introduces a systematic error in target and critical structure DVHs. In some cases, this systematic error may reach up to 5.8% for H&N and 8.0% for prostate cases when the hard-bone-containing structures such as femoral heads are present. Ignoring differences between D(m) and D(w) will result in systematic dose errors ranging from 0% to 8%.

Investigation of patient, tumour and treatment variables affecting residual motion for respiratory-gated radiotherapy.

Respiratory gating can reduce the apparent respiratory motion during imaging and treatment; however, residual motion within the gating window remains. Respiratory training can improve respiratory reproducibility and, therefore, the efficacy of respiratory-gated radiotherapy. This study was conducted to determine whether residual motion during respiratory gating is affected by patient, tumour or treatment characteristics. The specific aims of this study were to: (1) identify significant characteristics affecting residual motion, (2) investigate time trends of residual motion over a period of days (inter-session) and (3) investigate time trends of residual motion within the same day (intra-session). Twenty-four lung cancer patients were enrolled in an Institutional Review Board (IRB)-approved protocol. For approximately five sessions, 331 four-minute, respiratory motion traces were acquired with free breathing, audio instructions and audio-visual biofeedback for each patient. The residual motion was quantified by the standard deviation of the displacement within the gating window. The generalized linear model was used to obtain coefficients for each variable within the model and to evaluate the clinical and statistical significance. The statistical significance was determined by a p-value<0.05, while effect sizes of 0.1 cm (one standard deviation) were considered clinically significant. This data analysis was applied to patient, tumour and treatment variables. Inter- and intra-session variations were also investigated. The only variable that was significant for both inhale- and exhale-based gating was disease type. In addition, visual-training displacement, breathing type and Karnofsky performance status (KPS) values were significant for inhale-based gating, and dose-per-fraction was significant for exhale-based gating. Temporal respiratory variations within and between sessions were observed for individual patients. However inter- and intra-session analyses did not show significant time trends on average for any of the variables considered. The lack of significant time trends within and between sessions indicates that on average (1) there is no significant learning period for breathing training, (2) the patients did not experience training-related fatigue and (3) the margin component to account for residual motion during gated radiotherapy appears to remain constant throughout the treatment.

Determination of maximum leaf velocity and acceleration of a dynamic multileaf collimator: implications for 4D radiotherapy.

The dynamic multileaf collimator (MLC) can be used for four-dimensional (4D), or tumor tracking radiotherapy. However, the leaf velocity and acceleration limitations become a crucial factor as the MLC leaves need to respond in near real time to the incoming respiration signal. The aims of this paper are to measure maximum leaf velocity, acceleration, and deceleration to obtain the mechanical response times for the MLC, and determine whether the MLC is suitable for 4D radiotherapy. MLC leaf sequence files, requiring the leaves to reach maximum acceleration and velocity during motion, were written. The leaf positions were recorded every 50 ms, from which the maximum leaf velocity, acceleration, and deceleration were derived. The dependence on the velocity and acceleration of the following variables were studied: leaf banks, inner and outer leaves, MLC-MLC variations, gravity, friction, and the stability of measurements over time. Measurement results show that the two leaf banks of a MLC behave similarly, while the inner and outer leaves have significantly different maximum leaf velocities. The MLC-MLC variations and the dependence of gravity on maximum leaf velocity are statistically significant. The average maximum leaf velocity at the isocenter plane of the MLC ranged from 3.3 to 3.9 cm/s. The acceleration and deceleration at the isocenter plane of the MLC ranged from 50 to 69 cm/s2 and 46 to 52 cm/s2, respectively. Interleaf friction had a negligible effect on the results, and the MLC parameters remained stable with time. Equations of motion were derived to determine the ability of the MLC response to fluoroscopymeasured diaphragm motion. Given the present MLC mechanical characteristics, 4D radiotherapy is feasible for up to 97% of respiratory motion. For the largest respiratory motion velocities observed, beam delivery should be temporarily stopped (beam hold).

A comparison of Monte Carlo dose calculation denoising techniques.

Recent studies have demonstrated that Monte Carlo (MC) denoising techniques can reduce MC radiotherapy dose computation time significantly by preferentially eliminating statistical fluctuations ('noise') through smoothing. In this study, we compare new and previously published approaches to MC denoising, including 3D wavelet threshold denoising with sub-band adaptive thresholding, content adaptive mean-median-hybrid (CAMH) filtering, locally adaptive Savitzky-Golay curve-fitting (LASG), anisotropic diffusion (AD) and an iterative reduction of noise (IRON) method formulated as an optimization problem. Several challenging phantom and computed-tomography-based MC dose distributions with varying levels of noise formed the test set. Denoising effectiveness was measured in three ways: by improvements in the mean-square-error (MSE) with respect to a reference (low noise) dose distribution; by the maximum difference from the reference distribution and by the 'Van Dyk' pass/fail criteria of either adequate agreement with the reference image in low-gradient regions (within 2% in our case) or, in high-gradient regions, a distance-to-agreement-within-2% of less than 2 mm. Results varied significantly based on the dose test case: greater reductions in MSE were observed for the relatively smoother phantom-based dose distribution (up to a factor of 16 for the LASG algorithm); smaller reductions were seen for an intensity modulated radiation therapy (IMRT) head and neck case (typically, factors of 2-4). Although several algorithms reduced statistical noise for all test geometries, the LASG method had the best MSE reduction for three of the four test geometries, and performed the best for the Van Dyk criteria. However, the wavelet thresholding method performed better for the head and neck IMRT geometry and also decreased the maximum error more effectively than LASG. In almost all cases, the evaluated methods provided acceleration of MC results towards statistically more accurate results.

The effect of dose calculation uncertainty on the evaluation of radiotherapy plans.

Monte Carlo dose calculations will potentially reduce systematic errors that may be present in currently used dose calculation algorithms. However, Monte Carlo calculations inherently contain random errors, or statistical uncertainty, the level of which decreases inversely with the square root of computation time. Our purpose in this study was to determine the level of uncertainty at which a lung treatment plan is clinically acceptable. The evaluation methods to decide acceptability were visual examination of both isodose lines on CT scans and dose volume histograms (DVHs), and reviewing calculated biological indices. To study the effect of systematic and/or random errors on treatment plan evaluation, a simulated "error-free" reference plan was used as a benchmark. The relationship between Monte Carlo statistical uncertainty and dose was found to be approximately proportional to the square root of the dose. Random and systematic errors were applied to a calculated lung plan, creating dose distributions with statistical uncertainties of between 0% and 16% (1 s.d.) at the maximum dose point and also distributions with systematic errors of -16% to 16% at the maximum dose point. Critical structure DVHs and biological indices are less sensitive to calculation uncertainty than those of the target. Systematic errors affect plan evaluation accuracy significantly more than random errors, suggesting that Monte Carlo dose calculation will improve outcomes in radiotherapy. A statistical uncertainty of 2% or less does not significantly affect isodose lines, DVHs, or biological indices.