Nelson Miksys

Metallic artifact mitigation and organ-constrained tissue assignment for Monte Carlo calculations of permanent implant lung brachytherapy.

PURPOSE To investigate methods of generating accurate patient-specific computational phantoms for the Monte Carlo calculation of lung brachytherapy patient dose distributions. METHODS Four metallic artifact mitigation methods are applied to six lung brachytherapy patient computed tomography (CT) images: simple threshold replacement (STR) identifies high CT values in the vicinity of the seeds and replaces them with estimated true values; fan beam virtual sinogram replaces artifact-affected values in a virtual sinogram and performs a filtered back-projection to generate a corrected image; 3D median filter replaces voxel values that differ from the median value in a region of interest surrounding the voxel and then applies a second filter to reduce noise; and a combination of fan beam virtual sinogram and STR. Computational phantoms are generated from artifact-corrected and uncorrected images using several tissue assignment schemes: both lung-contour constrained and unconstrained global schemes are considered. Voxel mass densities are assigned based on voxel CT number or using the nominal tissue mass densities. Dose distributions are calculated using the EGSnrc user-code BrachyDose for (125)I, (103)Pd, and (131)Cs seeds and are compared directly as well as through dose volume histograms and dose metrics for target volumes surrounding surgical sutures. RESULTS Metallic artifact mitigation techniques vary in ability to reduce artifacts while preserving tissue detail. Notably, images corrected with the fan beam virtual sinogram have reduced artifacts but residual artifacts near sources remain requiring additional use of STR; the 3D median filter removes artifacts but simultaneously removes detail in lung and bone. Doses vary considerably between computational phantoms with the largest differences arising from artifact-affected voxels assigned to bone in the vicinity of the seeds. Consequently, when metallic artifact reduction and constrained tissue assignment within lung contours are employed in generated phantoms, this erroneous assignment is reduced, generally resulting in higher doses. Lung-constrained tissue assignment also results in increased doses in regions of interest due to a reduction in the erroneous assignment of adipose to voxels within lung contours. Differences in dose metrics calculated for different computational phantoms are sensitive to radionuclide photon spectra with the largest differences for (103)Pd seeds and smallest but still considerable differences for (131)Cs seeds. CONCLUSIONS Despite producing differences in CT images, dose metrics calculated using the STR, fan beam + STR, and 3D median filter techniques produce similar dose metrics. Results suggest that the accuracy of dose distributions for permanent implant lung brachytherapy is improved by applying lung-constrained tissue assignment schemes to metallic artifact corrected images.

Development of virtual patient models for permanent implant brachytherapy Monte Carlo dose calculations: interdependence of CT image artifact mitigation and tissue assignment.

This work investigates and compares CT image metallic artifact reduction (MAR) methods and tissue assignment schemes (TAS) for the development of virtual patient models for permanent implant brachytherapy Monte Carlo (MC) dose calculations. Four MAR techniques are investigated to mitigate seed artifacts from post-implant CT images of a homogeneous phantom and eight prostate patients: a raw sinogram approach using the original CT scanner data and three methods (simple threshold replacement (STR), 3D median filter, and virtual sinogram) requiring only the reconstructed CT image. Virtual patient models are developed using six TAS ranging from the AAPM-ESTRO-ABG TG-186 basic approach of assigning uniform density tissues (resulting in a model not dependent on MAR) to more complex models assigning prostate, calcification, and mixtures of prostate and calcification using CT-derived densities. The EGSnrc user-code BrachyDose is employed to calculate dose distributions. All four MAR methods eliminate bright seed spot artifacts, and the image-based methods provide comparable mitigation of artifacts compared with the raw sinogram approach. However, each MAR technique has limitations: STR is unable to mitigate low CT number artifacts, the median filter blurs the image which challenges the preservation of tissue heterogeneities, and both sinogram approaches introduce new streaks. Large local dose differences are generally due to differences in voxel tissue-type rather than mass density. The largest differences in target dose metrics (D90, V100, V150), over 50% lower compared to the other models, are when uncorrected CT images are used with TAS that consider calcifications. Metrics found using models which include calcifications are generally a few percent lower than prostate-only models. Generally, metrics from any MAR method and any TAS which considers calcifications agree within 6%. Overall, the studied MAR methods and TAS show promise for further retrospective MC dose calculation studies for various permanent implant brachytherapy treatments.

Patient-specific Monte Carlo dose calculations for (103)Pd breast brachytherapy.

This work retrospectively investigates patient-specific Monte Carlo (MC) dose calculations for (103)Pd permanent implant breast brachytherapy, exploring various necessary assumptions for deriving virtual patient models: post-implant CT image metallic artifact reduction (MAR), tissue assignment schemes (TAS), and elemental tissue compositions. Three MAR methods (thresholding, 3D median filter, virtual sinogram) are applied to CT images; resulting images are compared to each other and to uncorrected images. Virtual patient models are then derived by application of different TAS ranging from TG-186 basic recommendations (mixed adipose and gland tissue at uniform literature-derived density) to detailed schemes (segmented adipose and gland with CT-derived densities). For detailed schemes, alternate mass density segmentation thresholds between adipose and gland are considered. Several literature-derived elemental compositions for adipose, gland and skin are compared. MC models derived from uncorrected CT images can yield large errors in dose calculations especially when used with detailed TAS. Differences in MAR method result in large differences in local doses when variations in CT number cause differences in tissue assignment. Between different MAR models (same TAS), PTV [Formula: see text] and skin [Formula: see text] each vary by up to 6%. Basic TAS (mixed adipose/gland tissue) generally yield higher dose metrics than detailed segmented schemes: PTV [Formula: see text] and skin [Formula: see text] are higher by up to 13% and 9% respectively. Employing alternate adipose, gland and skin elemental compositions can cause variations in PTV [Formula: see text] of up to 11% and skin [Formula: see text] of up to 30%. Overall, AAPM TG-43 overestimates dose to the PTV ([Formula: see text] on average 10% and up to 27%) and underestimates dose to the skin ([Formula: see text] on average 29% and up to 48%) compared to the various MC models derived using the post-MAR CT images studied herein. The considerable differences between TG-43 and MC models underline the importance of patient-specific MC dose calculations for permanent implant breast brachytherapy. Further, the sensitivity of these MC dose calculations due to necessary assumptions illustrates the importance of developing a consensus modelling approach.

SU-E-T-156: Can Sr-90 Check Sources Replace Co-60 Measurements for Monitoring of Reference Chamber Stability?

Purpose: To determine the ultimate precision of a system for monitoring reference-class ion chamber stability using a commercial Sr-90 check source. Methods: A detailed investigation of a commercial Sr-90 check source (PTW48002) was carried out using a series of Farmer-type ionization chambers. Investigations included: positioning repeatability (angular variation as chamber is rotated in source, variation in ionization current with vertical alignment); chamber settling; short and long term repeatability Results: i) Measurement precision – the ionization current was typically 10 pA, and therefore a high-precision electrometer is required to prevent electrometer noise/resolution/leakage biaising the results. ii) Chamber settling - the chamber response stabilizes after approximately 10 minutes, which is longer than reported for linac beams and is likely due to the low doserate of the source.iii) The measured response depended at the 1 % level on the orientation of the chamber with respect to the source. However, consistent positioning resulted in repeatability at the 0.05 % level. Care was also required to ensure that the chamber was consistently positioned vertically with respect to the source. The sensitivity to vertical position was found to be > 1 % per mm.iv)With a uniform procedure the long-term (> 6 month) repeatability was found to be better than 0.1 % for multiple chamber types and potentially a precision of 0.05 % is achievable. Conclusion: A Sr-90 check source is easy to use and is a viable alternative to Co-60 for monitoring reference chamber stability.

SU‐E‐T‐732: The Effects of Calcification Modeling for Monte Carlo Dose Calculations of Low Dose Rate Prostate Brachytherapy

Purpose: Retrospective Monte Carlo calculations recently revealed that tissue calcifications cause significant dose perturbations in low dose rate prostate brachytherapy patients. This work investigates dose differences due to calcification modeling schemes of varying complexity for MC dose calculations. Methods: A prostate cancer patient with a large prostatic calcification (∼2.3 cm3) treated with 125I seeds was studied. MC calculations were performed using the EGSnrc user-code BrachyDose with dose scored to medium in medium. Computational phantoms were generated from CT images acquired one month post-implant. Tissues were assigned to voxels within structure contours based on CT number: prostate and calcification within the target, air and muscle within the rectum, urinary bladder within the bladder, and ICRU 46 soft tissue and bone in remaining voxels. Target models included: water only (TG-43), prostate tissue with CT-derived mass densities except for higher calcification densities over¬ridden with prostate nominal density, only prostate with unmodified CT-derived densities, prostate and 100% calcification with CT-derived densities (PC), and prostate and incremental mixtures of prostate and calcification with CT-derived densities (PCmix). Results: Local dose differences of up to a factor of 3 or more were found between the various models including significant dose differences between PC and PCmix inside and adjacent to the calcification. Dose metrics differ between the models with the minimum dose that 90% of the target received (D90) differing by 4% between PC and PCmix. Adjacent to calcification, doses for calcification models were approximately 50% less than those calculated by TG-43. D90 for calcification models was approximately 80–90% the value of that calculated by TG-43. Conclusions: Considerable dose differences were found between calculations using various calcification modeling schemes for a low dose rate prostate brachytherapy patient with prostatic calcifications, highlighting the importance of detailed and accurate calcification modeling for MC dose calculations of these treatments.

Coupling I‐125 permanent implant prostate brachytherapy Monte Carlo dose calculations with radiobiological models

Purpose: To investigate the coupling of radiobiological models with patient‐specific Monte Carlo (MC) dose calculations for permanent implant prostate brachytherapy (PIPB). To compare radiobiological indices evaluated with different radiobiological models using MC and simulated AAPM TG‐43 dose calculations. Methods: Three‐dimensional dose distributions previously computed using MC techniques with two types of patient models, TG43sim (AAPM TG‐43 water‐based conditions) and MCDmm (realistic tissues and interseed effects), for 613 PIPB patients are coupled with biological dose and tumour control probability (TCP) models. Two approaches and their extensions are considered to evaluate biological doses, biologically effective dose (BED) and isoeffective dose (IED), as well as two methods to evaluate TCP. Three novel extensions of equivalent uniform biologically effective dose (EUBED) are suggested which consider the spatial distribution of doses within the target volume. Adopted radiobiological model parameter values (α, β, etc) are those suggested by AAPM TG‐137, and sensitivity to parameter choice is discussed. Results: MCDmm dose calculations can reveal low doses in the prostate target volume, due to tissue heterogeneities or inter‐seed effects; considering these low doses in EUBED calculations can lower TCP estimates by up to 70%, with largest differences in patients with calcifications. There are large variations in biological doses and TCPs evaluated over the 613 patient cohort for each radiobiological model considered, reflecting the spectrum of physical doses calculated for these patients with either MCDmm or TG43sim. Depending on the model details, BED, IED and EUBED are, on average, 6.0–9.8%, 7.4–9.2% and 1.8–15% higher, respectively, with TG43sim than MCDmm. TCP estimates computed using MCDmm dose distributions are much lower than expected based on past treatment outcome studies, suggesting a need to re‐assess model parameters when evaluating radiobiological indices coupled with heterogeneous tissue model‐based dose calculations. Conclusions: Cohort average differences in biological dose and TCP estimates between radiobiological models are generally larger than differences for any one radiobiological model evaluated with TG43sim or MCDmm dose calculations. However, heterogeneous tissue dose calculations, like MCDmm, can identify clinically‐relevant low dose volumes, e.g., in patients with calcifications, which would otherwise be missed with TG‐43. In addition to affecting physical dose distributions, these low dose volumes can largely impact radiobiological dose and TCP estimates, which further motivates the clinical implementation of model‐based dose calculations for PIPB.

WE‐C‐108‐09: Patient‐Specific Monte Carlo Dosimetry for I–125 Prostate Brachytherapy

PURPOSE To investigate dose distributions in the prostate and surrounding tissues for I-125 brachytherapy using Monte Carlo (MC) calculations with patient-specific phantoms derived from CT images. We explore the effectiveness of different techniques to mitigate streaking artifacts due to brachytherapy sources in post-implant CT images. METHODS Nine patients (45-87 sources) are considered. Streaking artifacts in post-implant CT images are mitigated using various metallic artifact reduction (MAR) techniques: raw sinogram, fan beam virtual sinogram and 3D median filter. Segmented structures (CTV, rectum and bladder) guide the assignment of tissues (air, muscle, prostate, calcification, average tissue and bone) to develop patient-specific MC phantoms. The EGSnrc user-code Brachydose and GEANT4 user-code ALGEBRA are employed for MC dose calculations using patient-specific phantoms derived from CT images. Dose distributions generated with patient models derived using different MAR techniques and different calculation methods (MC, TG-43) are compared directly, and using recommended dose metrics. RESULTS Application of each MAR technique to patient CT data results in comparable mitigation of streaking artifacts within the treatment volume in CT images. MC calculations based on uncorrected CT data Result in high dose spikes (>200%) in the treatment volume and lower doses (40-70%) in surrounding tissues compared to MC calculations with corrected phantoms. Dose distributions within the prostate from MC simulations using phantoms generated with different MAR techniques are comparable to each other but differ from TG-43 calculations with significant (10%+) local differences. CONCLUSION Mitigation of streaking artifacts in CT images is necessary for patient-specific MC dose calculations; however, dose distributions and clinical metrics in the target and OAR are insensitive to the particular MAR technique applied. Differences between MC and TG-43 dose distributions within the target volume and surrounding organs for I-125 prostate brachytherapy underline the importance of patient-specific model-based dose calculations for treatment planning and evaluation. NSERC, CCSRI, The Canada Research Chairs Program.