Influence of breast composition and interseed attenuation in dose calculations for post-implant assessment of permanent breast103Pd seed implant

Impact on the estimated dose of different tissue assignment strategies during partial breast irradiations with INTRABEAM

PURPOSE

Partial breast irradiations with electronic brachytherapy or kilovoltage intraoperative radiotherapy devices such as Axxent or INTRABEAM are becoming more common every day. Breast is mainly composed of glandular and adipose tissues, which are not always clearly disentangled in planning breast CTs. In these cases, breast tissues are replaced with an average soft tissue, or even water. However, at kilovoltage energies, this may lead to large differences in the delivered dose, due to the dominance of photoelectric effect. Therefore, the aim of this work was to study the effect on the dose prescribed in breast with the INTRABEAM device using different soft tissue assignment strategies that would replace the adipose and glandular tissues that constitute the breast in cases where these tissues cannot be adequately distinguished in a CT scan.

METHODS AND MATERIALS

Dose was computed with a Monte Carlo code in five patients with a 3 cm diameter INTRABEAM spherical applicator. Tissues within the breast were assigned following six different strategies: one based on the TG-43 recommendations, representing the whole breast as water of unity density, another one also water-based but with CT derived density, and the other four also based on CT-derived densities, using a single tissue resulting from different mixes of glandular and adipose tissues. These were compared against the reference dose computed in an accurately segmented CT, following TG-186 recommendations. Relative differences and dose ratios between the reference and the other tissue assignment strategies were obtained in three regions of interest inside the breast.

RESULTS AND CONCLUSIONS

Dose planning in water-based tissues was found inaccurate for breast treatment with INTRABEAM, as it would incur in up to 30% under-prescription of dose. If accurate soft tissue assignments in the breast cannot be safely done, a single-tissue composition of 80% adipose and 20% glandular tissue, or even a 100% adipose tissue, would be recommended to avoid dose under-prescription.

Modern Tools for Modern Brachytherapy

This review aims to showcase the brachytherapy tools and technologies that have emerged during the last 10 years. Soft-tissue contrast using magnetic resonance and ultrasound imaging has seen enormous growth in use to plan all forms of brachytherapy. The era of image-guided brachytherapy has encouraged the development of advanced applicators and given rise to the growth of individualised 3D printing to achieve reproducible and predictable implants. These advances increase the quality of implants to better direct radiation to target volumes while sparing normal tissue. Applicator reconstruction has moved beyond manual digitising, to drag and drop of three-dimensional applicator models with embedded pre-defined source pathways, ready for auto-recognition and automation. The simplified TG-43 dose calculation formalism directly linked to reference air kerma rate of high-energy sources in the medium water remains clinically robust. Model-based dose calculation algorithms accounting for tissue heterogeneity and applicator material will advance the field of brachytherapy dosimetry to become more clinically accurate. Improved dose-optimising toolkits contribute to the real-time and adaptive planning portfolio that harmonises and expedites the entire image-guided brachytherapy process. Traditional planning strategies remain relevant to validate emerging technologies and should continue to be incorporated in practice, particularly for cervical cancer. Overall, technological developments need commissioning and validation to make the best use of the advanced features by understanding their strengths and limitations. Brachytherapy has become high-tech and modern by respecting tradition and remaining accessible to all.

Personalized brachytherapy dose reconstruction using deep learning

BACKGROUND AND PURPOSE

Accurate calculation of the absorbed dose delivered to the tumor and normal tissues improves treatment gain factor, which is the major advantage of brachytherapy over external radiation therapy. To address the simplifications of TG-43 assumptions that ignore the dosimetric impact of medium heterogeneities, we proposed a deep learning (DL)-based approach, which improves the accuracy while requiring a reasonable computation time.

MATERIALS AND METHODS

We developed a Monte Carlo (MC)-based personalized brachytherapy dosimetry simulator (PBrDoseSim), deployed to generate patient-specific dose distributions. A deep neural network (DNN) was trained to predict personalized dose distributions derived from MC simulations, serving as ground truth. The paired channel input used for the training is composed of dose distribution kernel in water medium along with the full-volumetric density maps obtained from CT images reflecting medium heterogeneity.

RESULTS

The predicted single-dwell dose kernels were in good agreement with MC-based kernels serving as reference, achieving a mean relative absolute error (MRAE) and mean absolute error (MAE) of 1.16 ± 0.42% and 4.2 ± 2.7 × 10^-4 (Gy.sec^-1/voxel), respectively. The MRAE of the dose volume histograms (DVHs) between the DNN and MC calculations in the clinical target volume were 1.8 ± 0.86%, 0.56 ± 0.56%, and 1.48 ± 0.72% for D90, V150, and V100, respectively. For bladder, sigmoid, and rectum, the MRAE of D5cc between the DNN and MC calculations were 2.7 ± 1.7%, 1.9 ± 1.3%, and 2.1 ± 1.7%, respectively.

CONCLUSION

The proposed DNN-based personalized brachytherapy dosimetry approach exhibited comparable performance to the MC method while overcoming the computational burden of MC calculations and oversimplifications of TG-43.

Dosimetric investigation of 103Pd permanent breast seed implant brachytherapy based on Monte Carlo calculations

PURPOSE

Permanent breast seed implant using ¹⁰³Pd is emerging as an effective adjuvant radiation technique for early stage breast cancer. However, clinical dose evaluations follow the water-based TG-43 approach with its considerable approximations. Toward clinical adoption of advanced TG-186 model-based dose evaluations, this study presents a comprehensive investigation for permanent breast seed implant considering both target and normal tissue doses.

METHODS AND MATERIALS

Dose calculations are performed with the free open-source Monte Carlo (MC) code, egs_brachy, using two types of virtual patient models: TG43sim (simulated TG-43 conditions) and MCref (heterogeneous tissue modeling from patient CT, seeds at implant angle) for 35 patients. The sensitivity of dose metrics to seed orientation and tissue segmentation are assessed.

RESULTS

In the target volume, D₉₀ is 14.1 ± 5.8% lower with MCref than with TG43sim, on average. Conversely, normal tissue doses are generally higher with MCref than with TG43sim, for example, by 22 ± 13% for skin D_1cm2, 82 ± 7% for ribs D_max, and 71 ± 23% for heart D_1cm3. Discrepancies between MCref and TG43sim doses vary over the patient cohort, as well as with the tissue and metric considered. Skin doses are particularly sensitive to seed orientation, with average difference of 4% (maximum 28%) in D_1cm2 for seeds modeled vertically (egs_brachy default) compared with those aligned with implant angle.

CONCLUSIONS

TG-43 dose evaluations generally underestimate doses to critical normal organs/tissues while overestimating target doses. There is considerable variation in MCref and TG43sim on a patient-by-patient basis, motivating clinical adoption of patient-specific MC dose calculations. The MCref framework presented herein provides a consistent modeling approach for clinical implementation of advanced TG-186 dose calculations.

Model-Based Dose Calculation Algorithms for Brachytherapy Dosimetry

The purpose of this study was to review the limitations of dose calculation formalisms for photon-emitting brachytherapy sources based on the American Association of Physicists in Medicine (AAPM) Task Group No. 43 (TG-43) report and to provide recommendations to transition to model-based dose calculation algorithms. Additionally, an overview of these algorithms and approaches is presented. The influence of tissue and seed/applicator heterogeneities on brachytherapy dose distributions for breast, gynecologic, head and neck, rectum, and prostate cancers as well as eye plaques and electronic brachytherapy treatments were investigated by comparing dose calculations based on the TG-43 formalism and model-based dose calculation algorithms.

Monte Carlo study of the relationship between skin dose and optically stimulated luminescence dosimeter dose in Pd-103 permanent breast seed implant brachytherapy

PURPOSE

To establish a method for estimating skin dose for patients with permanent breast seed implant based on in vivo optically stimulated luminescence dosimeters (OSLDs) measurements.

METHODS AND MATERIALS

Monte Carlo simulations were performed in a simple breast phantom using the EGSnrc user code egs_brachy. Realistic models of the IsoAid Advantage Pd-103 brachytherapy source and Landauer nanoDot OSLD were created to model in vivo skin dose measurements where an OSLD would be placed on the skin of a patient with permanent breast seed implant following implantation. Doses to a 0.2 cm³ volume of skin beneath the OSLD and to the sensitive volume within the OSLD were calculated, and the ratio of these values was found for various seed positions inside the breast phantom. The maximum value of this ratio may be used as a conversion factor that would allow skin dose to be estimated from in vivo OSLD measurements.

RESULTS

Conversion factors of 0.5 and 1.44 are recommended for OSLDs calibrated to dose to Al₂O₃ and water, respectively, at the point of measurement in the OSLD. These factors were not significantly affected by the addition of extra seeds in the dose calculations.

CONCLUSIONS

A method for estimating skin dose from OSLD measurements was proposed. Individual institutions should calibrate OSLDs to Pd-103 seeds to apply the results of this work clinically.

The influence of tissue composition uncertainty on dose distributions in brachytherapy

BACKGROUND AND PURPOSE

Model-based dose calculation algorithms (MBDCAs) have evolved from serving as a research tool into clinical practice in brachytherapy. This study investigates primary sources of tissue elemental compositions used as input to MBDCAs and the impact of their variability on MBDCA-based dosimetry.

MATERIALS AND METHODS

Relevant studies were retrieved through PubMed. Minimum dose delivered to 90% of the target (D₉₀), minimum dose delivered to the hottest specified volume for organs at risk (OAR) and mass energy-absorption coefficients (μ_en/ρ) generated by using EGSnrc "g" user-code were compared to assess the impact of compositional variability.

RESULTS

Elemental composition for hydrogen, carbon, oxygen and nitrogen are derived from the gross contents of fats, proteins and carbohydrates for any given tissue, the compositions of which are taken from literature dating back to 1940-1950. Heavier elements are derived from studies performed in the 1950-1960. Variability in elemental composition impacts greatly D₉₀ for target tissues and doses to OAR for brachytherapy with low energy sources and less for ¹⁹²Ir-based brachytherapy. Discrepancies in μ_en/ρ are also indicative of dose differences.

CONCLUSIONS

Updated elemental compositions are needed to optimize MBDCA-based dosimetry. Until then, tissue compositions based on gross simplifications in early studies will dominate the uncertainties in tissue heterogeneity.

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.

Clinical Significance of Accounting for Tissue Heterogeneity in Permanent Breast Seed Implant Brachytherapy Planning

PURPOSE

The inhomogeneity correction factor (ICF) method provides heterogeneity correction for the fast calculation TG43 formalism in seed brachytherapy. This study compared ICF-corrected plans to their standard TG43 counterparts, looking at their capacity to assess inadequate coverage and/or risk of any skin toxicities for patients who received permanent breast seed implant (PBSI).

METHODS AND MATERIALS

Two-month postimplant computed tomography scans and plans of 140 PBSI patients were used to calculate dose distributions by using the TG43 and the ICF methods. Multiple dose-volume histogram (DVH) parameters of clinical target volume (CTV) and skin were extracted and compared for both ICF and TG43 dose distributions. Short-term (desquamation and erythema) and long-term (telangiectasia) skin toxicity data were available on 125 and 110 of the patients, respectively, at the time of the study. The predictive value of each DVH parameter of skin was evaluated using the area under the receiver operating characteristic (ROC) curve for each toxicity endpoint.

RESULTS

Dose-volume histogram parameters of CTV, calculated using the ICF method, showed an overall decrease compared to TG43, whereas those of skin showed an increase, confirming previously reported findings of the impact of heterogeneity with low-energy sources. The ICF methodology enabled us to distinguish patients for whom the CTV V100 and V90 are up to 19% lower compared to TG43, which could present a risk of recurrence not detected when heterogeneity are not accounted for. The ICF method also led to an increase in the prediction of desquamation, erythema, and telangiectasia for 91% of skin DVH parameters studied.

CONCLUSIONS

The ICF methodology has the advantage of distinguishing any inadequate dose coverage of CTV due to breast heterogeneity, which can be missed by TG43. Use of ICF correction also led to an increase in prediction accuracy of skin toxicities in most cases.

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.

A simple analytical method for heterogeneity corrections in low dose rate prostate brachytherapy

In low energy brachytherapy, the presence of tissue heterogeneities contributes significantly to the discrepancies observed between treatment plan and delivered dose. In this work, we present a simplified analytical dose calculation algorithm for heterogeneous tissue. We compare it with Monte Carlo computations and assess its suitability for integration in clinical treatment planning systems. The algorithm, named as RayStretch, is based on the classic equivalent path length method and TG-43 reference data. Analytical and Monte Carlo dose calculations using Penelope2008 are compared for a benchmark case: a prostate patient with calcifications. The results show a remarkable agreement between simulation and algorithm, the latter having, in addition, a high calculation speed. The proposed analytical model is compatible with clinical real-time treatment planning systems based on TG-43 consensus datasets for improving dose calculation and treatment quality in heterogeneous tissue. Moreover, the algorithm is applicable for any type of heterogeneities.

Calcifications in low-dose rate prostate seed brachytherapy treatment: post-planning dosimetry and predictive factors

BACKGROUND AND PURPOSE

The brachytherapy dose algorithm of the American Association of Physicists in Medicine Task Group (TG) Report 43 overrides all tissue materials with water. In reality, dose discrepancies will occur around tissue calcifications. This study investigates these perturbations in low dose rate prostate brachytherapy dosimetry.

MATERIALS AND METHODS

43 cancer patients with prostatic calcifications are identified. Geant4 Monte Carlo (MC) simulations are made with materials assigned based on TG186 recommendations. Five dose calculation scenarios are presented: MC in water (MCW), MCW with calcifications, (MCCA), MCCA with seeds (MCCASEED) and full tissue definition and seeds with dose to medium in medium (FMC) and dose to water in medium (FMC-Dw,m).

RESULTS

The mean FMC prostate D90 (V100) difference relative to TG43 is -6.4% (range [-1.8, -14.1]) (-2.6% [-0.3, -6.7]). For MCCA we obtained -3.9% [-1.0, -8.7] (-1.5% [-0.2, -4.1]). The mean urethra D10 difference is -4.5% [-1.3, -9.9] for FMC, -2.4% [-0.7, -5.1] with MCCA. FMC-Dw,m D90 has a -0.45% smaller dose difference than FMC on average. The calcification/prostate volume ratio is a good predictor of dose perturbation (R(2)=0.75).

CONCLUSION

Based on these results, calcifications alter the dose coverage and may have severe dose perturbation that requires recalculation.

Dose heterogeneity correction for low-energy brachytherapy sources using dual-energy CT images

Permanent seed implant brachytherapy is currently used for adjuvant radiotherapy of early stage prostate and breast cancer patients. The current standard for calculation of dose around brachytherapy sources is based on the AAPM TG-43 formalism, which generates the dose in a homogeneous water medium. Recently, AAPM TG-186 emphasized the importance of accounting for tissue heterogeneities. We have previously reported on a methodology where the absorbed dose in tissue can be obtained by multiplying the dose, calculated by the TG-43 formalism, by an inhomogeneity correction factor (ICF). In this work we make use of dual energy CT (DECT) images to extract ICF parameters. The advantage of DECT over conventional CT is that it eliminates the need for tissue segmentation as well as assignment of population based atomic compositions. DECT images of a heterogeneous phantom were acquired and the dose was calculated using both TG-43 and TG-43 [Formula: see text] formalisms. The results were compared to experimental measurements using Gafchromic films in the mid-plane of the phantom. For a seed implant configuration of 8 seeds spaced 1.5 cm apart in a cubic structure, the gamma passing score for 2%/2 mm criteria improved from 40.8% to 90.5% when ICF was applied to TG-43 dose distributions.

Prospects for in vivo estimation of photon linear attenuation coefficients using postprocessing dual-energy CT imaging on a commercial scanner: comparison of analytic and polyenergetic statistical reconstruction algorithms

PURPOSE

Accurate patient-specific photon cross-section information is needed to support more accurate model-based dose calculation for low energy photon-emitting modalities in medicine such as brachytherapy and kilovoltage x-ray imaging procedures. A postprocessing dual-energy CT (pDECT) technique for noninvasive in vivo estimation of photon linear attenuation coefficients has been experimentally implemented on a commercial CT scanner and its accuracy assessed in idealized phantom geometries.

METHODS

Eight test materials of known composition and density were used to compare pDECT-estimated linear attenuation coefficients to NIST reference values over an energy range from 10 keV to 1 MeV. As statistical image reconstruction (SIR) has been shown to reconstruct images with less random and systematic error than conventional filtered backprojection (FBP), the pDECT technique was implemented with both an in-house polyenergetic SIR algorithm, alternating minimization (AM), as well as a conventional FBP reconstruction algorithm. Improvement from increased spectral separation was also investigated by filtering the high-energy beam with an additional 0.5 mm of tin. The law of propagated uncertainty was employed to assess the sensitivity of the pDECT process to errors in reconstructed images.

RESULTS

Mean pDECT-estimated linear attenuation coefficients for the eight test materials agreed within 1% of NIST reference values for energies from 1 MeV down to 30 keV, with mean errors rising to between 3% and 6% at 10 keV, indicating that the method is unbiased when measurement and calibration phantom geometries are matched. Reconstruction with FBP and AM algorithms conferred similar mean pDECT accuracy. However, single-voxel pDECT estimates reconstructed on a 1 × 1 × 3 mm(3) grid are shown to be highly sensitive to reconstructed image uncertainty; in some cases pDECT attenuation coefficient estimates exhibited standard deviations on the order of 20% around the mean. Reconstruction with the statistical AM algorithm led to standard deviations roughly 40% to 60% less than FBP reconstruction. Additional tin filtration of the high energy beam exhibits similar pDECT estimation accuracy as the unfiltered beam, even when scanning with only 25% of the dose. Using the law of propagated uncertainty, low Z materials are found to be more sensitive to image reconstruction errors than high Z materials. Furthermore, it is estimated that reconstructed CT image uncertainty must be limited to less than 0.25% to achieve a target linear-attenuation coefficient estimation uncertainty of 3% at 28 keV.

CONCLUSIONS

That pDECT supports mean linear attenuation coefficient measurement accuracies of 1% of reference values for energies greater than 30 keV is encouraging. However, the sensitivity of the pDECT measurements to noise and systematic errors in reconstructed CT images warrants further investigation in more complex phantom geometries. The investigated statistical reconstruction algorithm, AM, reduced random measurement uncertainty relative to FBP owing to improved noise performance. These early results also support efforts to increase DE spectral separation, which can further reduce the pDECT sensitivity to measurement uncertainty.

Review of clinical brachytherapy uncertainties: analysis guidelines of GEC-ESTRO and the AAPM

Background and purpose

A substantial reduction of uncertainties in clinical brachytherapy should result in improved outcome in terms of increased local control and reduced side effects. Types of uncertainties have to be identified, grouped, and quantified.

Methods

A detailed literature review was performed to identify uncertainty components and their relative importance to the combined overall uncertainty.

Results

Very few components (e.g., source strength and afterloader timer) are independent of clinical disease site and location of administered dose. While the influence of medium on dose calculation can be substantial for low energy sources or non-deeply seated implants, the influence of medium is of minor importance for high-energy sources in the pelvic region. The level of uncertainties due to target, organ, applicator, and/or source movement in relation to the geometry assumed for treatment planning is highly dependent on fractionation and the level of image guided adaptive treatment. Most studies to date report the results in a manner that allows no direct reproduction and further comparison with other studies. Often, no distinction is made between variations, uncertainties, and errors or mistakes. The literature review facilitated the drafting of recommendations for uniform uncertainty reporting in clinical BT, which are also provided. The recommended comprehensive uncertainty investigations are key to obtain a general impression of uncertainties, and may help to identify elements of the brachytherapy treatment process that need improvement in terms of diminishing their dosimetric uncertainties. It is recommended to present data on the analyzed parameters (distance shifts, volume changes, source or applicator position, etc.), and also their influence on absorbed dose for clinically-relevant dose parameters (e.g., target parameters such as D₉₀ or OAR doses). Publications on brachytherapy should include a statement of total dose uncertainty for the entire treatment course, taking into account the fractionation schedule and level of image guidance for adaptation.

Conclusions

This report on brachytherapy clinical uncertainties represents a working project developed by the Brachytherapy Physics Quality Assurances System (BRAPHYQS) subcommittee to the Physics Committee within GEC-ESTRO. Further, this report has been reviewed and approved by the American Association of Physicists in Medicine.

Experimental implementation of a polyenergetic statistical reconstruction algorithm for a commercial fan-beam CT scanner

PURPOSE

To present a framework for characterizing the data needed to implement a polyenergetic model-based statistical reconstruction algorithm, Alternating Minimization (AM), on a commercial fan-beam CT scanner and a novel method for assessing the accuracy of the commissioned data model.

METHODS

The X-ray spectra for three tube potentials on the Philips Brilliance CT scanner were estimated by fitting a semi-empirical X-ray spectrum model to transmission measurements. Spectral variations due to the bowtie filter were computationally modeled. Eight homogeneous cylinders of PMMA, Teflon and water with varying diameters were scanned at each energy. Central-axis scatter was measured for each cylinder using a beam-stop technique. AM reconstruction with a single-basis object-model matched to the scanned cylinder's composition allows assessment of the accuracy of the AM algorithm's polyenergetic data model. Filtered-backprojection (FBP) was also performed to compare consistency metrics such as uniformity and object-size dependence.

RESULTS

The spectrum model fit measured transmission curves with residual root-mean-square-error of 1.20%-1.34% for the three scanning energies. The estimated spectrum and scatter data supported polyenergetic AM reconstruction of the test cylinders to within 0.5% of expected in the matched object-model reconstruction test. In comparison to FBP, polyenergetic AM exhibited better uniformity and less object-size dependence.

CONCLUSIONS

Reconstruction using a matched object-model illustrate that the polyenergetic AM algorithm's data model was commissioned to within 0.5% of an expected ground truth. These results support ongoing and future research with polyenergetic AM reconstruction of commercial fan-beam CT data for quantitative CT applications.

Fast patient-specific Monte Carlo brachytherapy dose calculations via the correlated sampling variance reduction technique

PURPOSE

To demonstrate potential of correlated sampling Monte Carlo (CMC) simulation to improve the calculation efficiency for permanent seed brachytherapy (PSB) implants without loss of accuracy.

METHODS

CMC was implemented within an in-house MC code family (PTRAN) and used to compute 3D dose distributions for two patient cases: a clinical PSB postimplant prostate CT imaging study and a simulated post lumpectomy breast PSB implant planned on a screening dedicated breast cone-beam CT patient exam. CMC tallies the dose difference, ΔD, between highly correlated histories in homogeneous and heterogeneous geometries. The heterogeneous geometry histories were derived from photon collisions sampled in a geometrically identical but purely homogeneous medium geometry, by altering their particle weights to correct for bias. The prostate case consisted of 78 Model-6711 (125)I seeds. The breast case consisted of 87 Model-200 (103)Pd seeds embedded around a simulated lumpectomy cavity. Systematic and random errors in CMC were unfolded using low-uncertainty uncorrelated MC (UMC) as the benchmark. CMC efficiency gains, relative to UMC, were computed for all voxels, and the mean was classified in regions that received minimum doses greater than 20%, 50%, and 90% of D(90), as well as for various anatomical regions.

RESULTS

Systematic errors in CMC relative to UMC were less than 0.6% for 99% of the voxels and 0.04% for 100% of the voxels for the prostate and breast cases, respectively. For a 1 × 1 × 1 mm(3) dose grid, efficiency gains were realized in all structures with 38.1- and 59.8-fold average gains within the prostate and breast clinical target volumes (CTVs), respectively. Greater than 99% of the voxels within the prostate and breast CTVs experienced an efficiency gain. Additionally, it was shown that efficiency losses were confined to low dose regions while the largest gains were located where little difference exists between the homogeneous and heterogeneous doses. On an AMD 1090T processor, computing times of 38 and 21 sec were required to achieve an average statistical uncertainty of 2% within the prostate (1 × 1 × 1 mm(3)) and breast (0.67 × 0.67 × 0.8 mm(3)) CTVs, respectively.

CONCLUSIONS

CMC supports an additional average 38-60 fold improvement in average efficiency relative to conventional uncorrelated MC techniques, although some voxels experience no gain or even efficiency losses. However, for the two investigated case studies, the maximum variance within clinically significant structures was always reduced (on average by a factor of 6) in the therapeutic dose range generally. CMC takes only seconds to produce an accurate, high-resolution, low-uncertainly dose distribution for the low-energy PSB implants investigated in this study.

Consequences of dose heterogeneity on the biological efficiency of ¹⁰³Pd permanent breast seed implants

Brachytherapy is associated with highly heterogeneous spatial dose distributions. This heterogeneity is usually ignored when estimating the biological effective dose (BED). In addition, the heterogeneities of the medium including the tissue heterogeneity (TH) and the interseed attenuation (ISA) are also contributing to the heterogeneity of the dose distribution, but they are both ignored in Task Group 43 (TG43)-based protocols. This study investigates the effect of dose heterogeneity, TH and ISA on metrics that are commonly used to quantify biological efficiency in brachytherapy. The special case of 29 breast cancer patients treated with permanent (103)Pd seed implant is considered here. BED is compared to equivalent uniform BED (EUBED) capable of considering the spatial heterogeneity of the dose distribution. The effects of TH and ISA on biological efficiency of treatments are taken into account by comparing TG43 with Monte Carlo (MC) dose calculations for each patient. The effect of clonogenic repopulation is also considered. The analysis is performed for different sets of (α/β, α) ratios of (2, 0.3), (4, 0.27) and (10, 0.3) [Gy, Gy(-1)] covering the whole range of reported α/β values in the literature. BED is sometimes larger and sometimes smaller than EUBED(TG43) indicating that the effect of the dose heterogeneity is not similar among patients. The effect of the dose heterogeneity can be characterized by using the D(99) dose metric. For each set of the radiobiological parameters considered, a D(99) threshold is found over which dose heterogeneity will cause an overestimation of the biological efficiencies while the inverse happens for smaller D(99) values. EUBED(MC) is always larger than EUBED(TG43) indicating that by neglecting TH and ISA in TG43-based dosimetry algorithms, the biological efficiencies may be underestimated by about 10 Gy. Overall, by going from BED to the more accurate EUBED(MC) there is a gain of about 9.6 to 13 Gy on the biological efficiency. The efficiency gain is about 10.8 to 14 Gy when the repopulation is considered. Dose heterogeneity does not have a constant impact on the biological efficiencies and may under- or overestimate the efficacy in different patients. However, the combined effect of neglecting dose heterogeneity, TH and ISA results in underestimation of the biological efficiencies in permanent breast seed implants.

Extracting atomic numbers and electron densities from a dual source dual energy CT scanner: experiments and a simulation model

BACKGROUND AND PURPOSE

Dual energy CT (DECT) imaging can provide both the electron density ρ(e) and effective atomic number Z(eff), thus facilitating tissue type identification. This paper investigates the accuracy of a dual source DECT scanner by means of measurements and simulations. Previous simulation work suggested improved Monte Carlo dose calculation accuracy when compared to single energy CT for low energy photon brachytherapy, but lacked validation. As such, we aim to validate our DECT simulation model in this work.

MATERIALS AND METHODS

A cylindrical phantom containing tissue mimicking inserts was scanned with a second generation dual source scanner (SOMATOM Definition FLASH) to obtain Z(eff) and ρ(e). A model of the scanner was designed in ImaSim, a CT simulation program, and was used to simulate the experiment.

RESULTS

Accuracy of measured Z(eff) (labelled Z) was found to vary from -10% to 10% from low to high Z tissue substitutes while the accuracy on ρ(e) from DECT was about 2.5%. Our simulation reproduced the experiments within ±5% for both Z and ρ(e).

CONCLUSIONS

A clinical DECT scanner was able to extract Z and ρ(e) of tissue substitutes. Our simulation tool replicates the experiments within a reasonable accuracy.

The difference of scoring dose to water or tissues in Monte Carlo dose calculations for low energy brachytherapy photon sources

PURPOSE

The goal of this work is to compare D(m,m) (radiation transported in medium; dose scored in medium) and D(w,m) (radiation transported in medium; dose scored in water) obtained from Monte Carlo (MC) simulations for a subset of human tissues of interest in low energy photon brachytherapy. Using low dose rate seeds and an electronic brachytherapy source (EBS), the authors quantify the large cavity theory conversion factors required. The authors also assess whether ap plying large cavity theory utilizing the sources' initial photon spectra and average photon energy induces errors related to spatial spectral variations. First, ideal spherical geometries were investigated, followed by clinical brachytherapy LDR seed implants for breast and prostate cancer patients.

METHODS

Two types of dose calculations are performed with the GEANT4 MC code. (1) For several human tissues, dose profiles are obtained in spherical geometries centered on four types of low energy brachytherapy sources: 125I, 103Pd, and 131Cs seeds, as well as an EBS operating at 50 kV. Ratios of D(w,m) over D(m,m) are evaluated in the 0-6 cm range. In addition to mean tissue composition, compositions corresponding to one standard deviation from the mean are also studied. (2) Four clinical breast (using 103Pd) and prostate (using 125I) brachytherapy seed implants are considered. MC dose calculations are performed based on postimplant CT scans using prostate and breast tissue compositions. PTV D90 values are compared for D(w,m) and D(m,m).

RESULTS

(1) Differences (D(w,m)/D(m,m)-1) of -3% to 70% are observed for the investigated tissues. For a given tissue, D(w,m)/D(m,m) is similar for all sources within 4% and does not vary more than 2% with distance due to very moderate spectral shifts. Variations of tissue composition about the assumed mean composition influence the conversion factors up to 38%. (2) The ratio of D90(w,m) over D90(m,m) for clinical implants matches D(w,m)/D(m,m) at 1 cm from the single point sources,

CONCLUSIONS

Given the small variation with distance, using conversion factors based on the emitted photon spectrum (or its mean energy) of a given source introduces minimal error. The large differences observed between scoring schemes underline the need for guidelines on choice of media for dose reporting. Providing such guidelines is beyond the scope of this work.