Joseph A. Moore

Using fluence separation to account for energy spectra dependence in computing dosimetric a-Si EPID images for IMRT fields

This study develops a method to improve the dosimetric accuracy of computed images for an amorphous silicon flat-panel imager. Radially dependent kernels derived from Monte Carlo simulations are convolved with the treatment-planning systems energy fluence. Multileaf collimator (MLC) beam hardening is accounted for by having separate kernels for open and blocked portions of MLC fields. Field-size-dependent output factors are used to account for the field-size dependence of scatter within the imager. Gamma analysis was used to evaluate open and sliding window test fields and intensity modulated patient fields. For each tested field, at least 99.6% of the points had gamma < 1 with a 3%, 3-mm criteria. With a 2%, 2-mm criteria, between 81% and 100% of points had gamma < 1. Patient intensity modulated test fields had 94%-100% of the points with gamma < 1 with a 2%, 2-mm criteria for all six fields tested. This study demonstrates that including the dependencies of kernel and fluence on radius and beam hardening in the convolution improves its accuracy compared with the use of radial and beam-hardening independent kernels; it also demonstrates that the resultant accuracy of the convolution method is sufficient for pretreatment, intensity modulated patient field verification.

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.

Comparisons of treatment optimization directly incorporating random patient setup uncertainty with a margin‐based approach

The purpose of this study is to incorporate the dosimetric effect of random patient positioning uncertainties directly into a commercial treatment planning systems IMRT plan optimization algorithm through probabilistic treatment planning (PTP) and compare coverage of this method with margin-based planning. In this work, PTP eliminates explicit margins and optimizes directly on the estimated integral treatment dose to determine optimal patient dose in the presence of setup uncertainties. Twenty-eight prostate patient plans adhering to the RTOG-0126 criteria are optimized using both margin-based and PTP methods. Only random errors are considered. For margin-based plans, the planning target volume is created by expanding the clinical target volume (CTV) by 2.1 mm to accommodate the simulated 3 mm random setup uncertainty. Random setup uncertainties are incorporated into IMRT dose evaluation by convolving each beams incident fluence with a sigma = 3 mm Gaussian prior to dose calculation. PTP optimization uses the convolved fluence to estimate dose to ensure CTV coverage during plan optimization. PTP-based plans are compared to margin-based plans with equal CTV coverage in the presence of setup errors based on dose-volume metrics. The sensitivity of the optimized plans to patient-specific setup uncertainty variations is assessed by evaluating dose metrics for dose distributions corresponding to halving and doubling of the random setup uncertainty used in the optimization. Margin-based and PTP-based plans show similar target coverage. A physician review shows that PTP is preferred for 21 patients, margin-based plans are preferred in 2 patients, no preference is expressed for 1 patient, and both autogenerated plans are rejected for 4 patients. For the PTP-based plans, the average CTV receiving the prescription dose decreases by 0.5%, while the mean dose to the CTV increases by 0.7%. The CTV tumor control probability (TCP) is the same for both methods with the exception of one case in which PTP gave a slightly higher TCP. For critical structures that do not meet the optimization criteria, PTP shows a decrease in the volume receiving the maximum specified dose. PTP reduces local normal tissue volumes receiving the maximum dose on average by 48%. PTP results in lower mean dose to all critical structures for all plans. PTP results in a 2.5% increase in the probability of uncomplicated control (P+), along with a 1.9% reduction in rectum normal tissue complication probability (NTCP), and a 0.7% reduction in bladder NTCP. PTP-based plans show improved conformality as compared with margin-based plans with an average PTP-based dosimetric margin at 7100 cGy of 0.65 cm compared with the margin-based 0.90 cm and a PTP-based dosimetric margin at 3960 cGy of 1.60 cm compared with the margin-based 1.90 cm. PTP-based plans show similar sensitivity to variations of the uncertainty during treatment from the uncertainty used in planning as compared to margin-based plans. For equal target coverage, when compared to margin-based plans, PTP results in equal or lower doses to normal structures. PTP results in more conformal plans than margin-based plans and shows similar sensitivity to variations in uncertainty.

The Dosimetric Effect of Intrafraction Prostate Motion on Step-and-Shoot Intensity-Modulated Radiation Therapy Plans: Magnitude, Correlation With Motion Parameters, and Comparison With Helical Tomotherapy Plans

PURPOSE To determine the daily and cumulative dosimetric effects of intrafraction prostate motion on step-and-shoot (SNS) intensity-modulated radiation therapy (IMRT) plans, to evaluate the correlation of dosimetric effect with motion-based metrics, and to compare on a fraction-by-fraction basis the dosimetric effect induced in SNS and helical tomotherapy plans. METHODS AND MATERIALS Intrafraction prostate motion data from 486 fractions and 15 patients were available. A motion-encoded dose calculation technique was used to determine the variation of the clinical target volume (CTV) D(95%) values with respect to the static plan for SNS plans. The motion data were analyzed separately, and the correlation coefficients between various motion-based metrics and the dosimetric effect were determined. The dosimetric impact was compared with that incurred during another IMRT technique to assess correlation across different delivery techniques. RESULTS The mean (±1 standard deviation [SD]) change in D(95%) in the CTV over all 486 fractions was 0.2 ± 0.5%. After the delivery of five and 12 fractions, the mean (±1 SD) changes over the 15 patients in CTV D(95%) were 0.0 ± 0.2% and 0.1 ± 0.2%, respectively. The correlation coefficients between the CTV D(95%) changes and the evaluated motion metrics were, in general, poor and ranged from r = -0.2 to r = -0.39. Dosimetric effects introduced by identical motion in SNS and helical tomotherapy IMRT techniques were poorly correlated with a correlation coefficient of r = 0.32 for the CTV. CONCLUSIONS The dosimetric impact of intrafraction prostate motion on the CTV is, in general, small. In only 4% of all fractions did the dosimetric consequence exceed 1% in the CTV. As expected, the cumulative effect was further reduced with fractionation. The poor correlations between the calculated motion parameters and the subsequent dosimetric effect implies that motion-based thresholds are of limited value in predicting the dosimetric impact of intrafraction motion. The dosimetric effects between the two evaluated delivery techniques were poorly correlated.

A computational method for estimating the dosimetric effect of intra-fraction motion on step-and-shoot IMRT and compensator plans

Intra-fraction organ motion during intensity-modulated radiation therapy (IMRT) treatment can cause differences between the planned and the delivered dose distribution. To investigate the extent of these dosimetric changes, a computational model was developed and validated. The computational method allows for calculation of the rigid motion perturbed three-dimensional dose distribution in the CT volume and therefore a dose volume histogram-based assessment of the dosimetric impact of intra-fraction motion on a rigidly moving body. The method was developed and validated for both step-and-shoot IMRT and solid compensator IMRT treatment plans. For each segment (or beam), fluence maps were exported from the treatment planning system. Fluence maps were shifted according to the target position deduced from a motion track. These shifted, motion-encoded fluence maps were then re-imported into the treatment planning system and were used to calculate the motion-encoded dose distribution. To validate the accuracy of the motion-encoded dose distribution the treatment plan was delivered to a moving cylindrical phantom using a programmed four-dimensional motion phantom. Extended dose response (EDR-2) film was used to measure a planar dose distribution for comparison with the calculated motion-encoded distribution using a gamma index analysis (3% dose difference, 3 mm distance-to-agreement). A series of motion tracks incorporating both inter-beam step-function shifts and continuous sinusoidal motion were tested. The method was shown to accurately predict the films dose distribution for all of the tested motion tracks, both for the step-and-shoot IMRT and compensator plans. The average gamma analysis pass rate for the measured dose distribution with respect to the calculated motion-encoded distribution was 98.3 +/- 0.7%. For static delivery the average film-to-calculation pass rate was 98.7 +/- 0.2%. In summary, a computational technique has been developed to calculate the dosimetric effect of intra-fraction motion. This technique has the potential to evaluate a given plans sensitivity to anticipated organ motion. With knowledge of the organs motion it can also be used as a tool to assess the impact of measured intra-fraction motion after dose delivery.

Comparisons of treatment optimization directly incorporating systematic patient setup uncertainty with a margin-based approach: Systematic probabilistic treatment planning comparisons

PURPOSE To develop a probabilistic treatment planning (PTP) method which is robust to systematic patient setup errors and to compare PTP plans with plans generated using a planning target volume (PTV) margin optimized to give the same target coverage probability as the PTP plan. METHODS Plans adhering to the RTOG-0126 protocol are developed for 28 prostate patients using PTP and margin-based planning. For PTP, an objective function that simultaneously considers multiple possible patient positions is developed. PTP plans are optimized using clinical target volume (CTV) structures and organ at risk (OAR) structures. The desired CTV coverage probability is 95%. Plans that cannot achieve a 95% CTV coverage probability are re-optimized with a desired CTV coverage probability reduced by 5% until the desired CTV coverage probability is achieved. Margin-based plans are created which achieve the same CTV coverage probability as the PTP plans by iterative adjustment of the CTV-to-PTV margin. Postoptimization, probabilistic dose-volume coverage metrics are used to compare the plans. RESULTS For equivalent target coverage probability, PTP plans significantly reduce coverage probability for rectum objectives (-17% for D(35) < 65 Gy, p = 0.0010; -23% for D(25) < 70 Gy, p < 0.0001; and -27% for D(15) < 75 Gy, p < 0.0001). Physician assessment indicates PTP plans are entirely preferred 71% of the time while margin-based plans are entirely preferred 7% of the time. CONCLUSIONS For plans having the same target coverage probability, PTP has potential to reduce rectal doses while maintaining CTV coverage probability. In blind comparisons, physicians prefer PTP plans over optimized margin plans.

Multiple anatomy optimization of accumulated dose.

PURPOSE To investigate the potential advantages of multiple anatomy optimization (MAO) for lung cancer radiation therapy compared to the internal target volume (ITV) approach. METHODS MAO aims to optimize a single fluence to be delivered under free-breathing conditions such that the accumulated dose meets the plan objectives, where accumulated dose is defined as the sum of deformably mapped doses computed on each phase of a single four dimensional computed tomography (4DCT) dataset. Phantom and patient simulation studies were carried out to investigate potential advantages of MAO compared to ITV planning. Through simulated delivery of the ITV- and MAO-plans, target dose variations were also investigated. RESULTS By optimizing the accumulated dose, MAO shows the potential to ensure dose to the moving target meets plan objectives while simultaneously reducing dose to organs at risk (OARs) compared with ITV planning. While consistently superior to the ITV approach, MAO resulted in equivalent OAR dosimetry at planning objective dose levels to within 2% volume in 14/30 plans and to within 3% volume in 19/30 plans for each lung V20, esophagus V25, and heart V30. Despite large variations in per-fraction respiratory phase weights in simulated deliveries at high dose rates (e.g., treating 4/10 phases during single fraction beams) the cumulative clinical target volume (CTV) dose after 30 fractions and per-fraction dose were constant independent of planning technique. In one case considered, however, per-phase CTV dose varied from 74% to 117% of prescription implying the level of ITV-dose heterogeneity may not be appropriate with conventional, free-breathing delivery. CONCLUSIONS MAO incorporates 4DCT information in an optimized dose distribution and can achieve a superior plan in terms of accumulated dose to the moving target and OAR sparing compared to ITV-plans. An appropriate level of dose heterogeneity in MAO plans must be further investigated.

SU‐FF‐T‐131: Assessing the Dosimetric Impact of Intra‐Fraction Prostate Motion On Step‐And‐Shoot IMRT Plans

Purpose: To assess the dosimetric impact of intra‐fraction prostate motion for step‐and‐shoot IMRT plans. Method and Materials: For step‐and‐shoot IMRT plans, fluence maps for each segment are exported from the Pinnacle treatment planning system. Each fluence map segment is modified to simulate the effect of prostate motion observed during the simulated delivery. Modified fluence maps are re‐imported into Pinnacle for dose calculations. Calculated dose distributions are compared with unmodified fluence maps dose distributions to assess the impact of intrafraction motion on the dose delivery. Measured Calypso motion tracks for 16 patients (515 tracks) are used. Changes in prostate (4–6 mm margin) and PTV (zero‐margin) D95% are scored for each fraction and for the cumulative simulated delivery. Results: Average D95% changes (± 1SD) in the PTV and prostate are −0.5±1.1% and −0.2±0.5%. Maximum per‐fraction D95% changes are −12.7% and −6.4%. 12% and 4% of all fractions suffered a PTV and prostate D95% change in excess of 1 percent. Only 2% and 0.2% of all fractions suffered respective changes in excess of 3%. The patient specific cumulative D95% changes in the PTV and prostate average −0.15±0.2% and 0.07±0.15%. The maximum cumulative D95% variations for a single patient are −0.6% and 0.5%. After the delivery of 5 fractions the cumulative D95% variations for a single patient are −1% and 0.4% for the PTV and prostate. Conclusion: Inter‐fraction prostate motion has little effect on the dosimetric target coverage for step‐and‐shoot prostate plans. Very few fractions (2 and 0.2% for the PTV and prostate) suffered D95% changes in excess of 3%. After the delivery of all fractions the effect on the both the PTV and prostate was negligible. (Supported, in part, by by NIH P01CA116602 and T32CA113277)

SU‐GG‐T‐122: Implementation of Random Patient Setup Uncertainties Into Pinnacle‐Based IMRT Optimization

Purpose: To incorporate patient positioning uncertainties directly into IMRT plan optimization through probabilistic treatment planning (PTP). Margin‐based planning results in significant normal tissue volumes being exposed to a treatment dose. PTP eliminates explicit margins and operates directly on the expectation value of the integral treatment dose to determine the optimal dose to the patient in the presence of setup uncertainties. Method and Materials: Three prostate patients and one phantom plan are optimized using both margin‐based and PTP methods. Patient plans are designed to adhere to the RTOG‐0126 criteria. Only random errors are considered. For the margin‐based plan, the PTV is created by expanding the CTV by 2.1 mm to accommodate the 3 mm random setup uncertainty simulated. PTP directly utilizes the CTV during plan optimization. Random setup uncertainties are introduced into the Pinnacle IMRT TPS by convolving each beams incident fluence with a σ=3 mm Gaussian. PTP optimization uses the convolved fluence dose. PTPs are compared to PTV‐based margin plans with equal CTV coverage in the presence of setup errors. Normal structure doses are used to quantify PTP and margin‐based differences. Results: Both margin‐based and PTP plans meet 33 of 42 optimization criteria. For critical structures which did not meet the criteria, PTP shows decreased volume receiving the maximum specified dose. PTP reduces normal tissue volumes receiving the maximum dose on average by 57%, while the CTV volume loss is 2.5%. PTP reduces the CTV mean dose by 0.22%. PTP also results in lower doses to structures that meet the optimization criteria. Conclusion: PTP accounting for random patient positioning uncertainties was incorporated into Pinnacle. For equal target coverage, PTP results in equal or lower doses to normal structures than margin‐based plans. (Work supported by NIH P01CA116602 and T32CA113277). Conflict of Interest: An author is employed by Philips Medical Systems.

TU‐C‐T‐6E‐10: Using Fluence‐Separation to Account for Energy Spectra Dependence in Computing Dosimetric ASi EPID Images for IMRT Fields

Purpose: Dosimetric aSi EPIDimages are typically computed using a convolution of energy fluence with an invariant energy deposition kernel. However, Monte Carlo (MC) studies show a strong dependence of the EPIDimager response to energy spectra, which, for highly modulated IMRT fields, severely affects the dosimetric accuracy. To account for this, a method is developed that accounts for the radial‐dependence of the energy deposition kernel and considers open field and MLC hardened components of the energy fluence. Method and Materials: Dosimetric EPIDimages are created by convolving energy fluence with a radially dependent kernel. The energy fluence at the EPIDsurface was determined by extracting the terma in water from Pinnacle. For each fluence element, the energy fluence (Ψ) was divided into two parts — open field energy fluence Ψ o , and MLC blocked field energy fluence Ψ c . Ψ o and Ψ c were convolved separately with their respective energy‐deposition kernels and the results summed. Calculations were compared with measurements for, 3×3–20×20 cm2 fields, rectangular fields, a 10×10 cm2 field centered at (5cm,−5cm)), 3×3–12×12 cm2 MLC‐blocked fields, and dynamic MLC sliding window fields which generate 10×10 cm2 fields with window gaps ranging from 1‐ to 50‐mm .Test cases were compared utilizing profiles and using gamma‐analysis for pixels receiving doses >50 % Dmax. A 3 mm, 3% criteria was used in the gamma analysis. Results: Measured and computed dose profiles agreed for both in‐field and out‐of‐field regions. For the open field test cases, all points evaluated had γ 98.5% of points had γ 1.0 Over 98% of points passed the gamma‐test for most sliding window field. Conclusion: Accurate aSi dosimetric EPIDimages can be computed when energy spectra hardening is accounted for during the image calculation. Conflict of Interest: This work supported in‐part by Varian Medical Systems.