Fast GPU-based Monte Carlo simulations for LDR prostate brachytherapy

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy

Objective.In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability.Approach.Data from 200 prostate patients, treated with125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input data. The dataset was randomly split into training (150), validation (25) and test (25) sets. The aleatoric (associated with the input data) and epistemic (associated with the model) uncertainties of the DL models were assessed.Main results.For the full test set, with respect to the MC reference, the predicted prostateD90metric had mean differences of -0.64% and 0.08% for the UNet and ResUNet TSE models, respectively. In voxel-by-voxel comparisons, the average global dose difference ratio in the [-1%, 1%] range included 91.0% and 93.0% of voxels for the UNet and the ResUNet TSE, respectively. One forward pass or prediction took 4 ms for a 3D dose volume of 2.56 M voxels (128 × 160 × 128). The ResUNet TSE model closely encoded the well-known physics of the problem as seen in a set of uncertainty maps. The ResUNet TSE rectum D2cchad the largest uncertainty metric of 0.0042.Significance.The proposed DL models serve as rapid dose predictors that consider the patient anatomy and interseed attenuation effects. The derived uncertainty is interpretable, highlighting areas where DL models may struggle to provide accurate estimations. The uncertainty analysis offers a comprehensive evaluation tool for dose predictor model assessment.

Embedding expertise knowledge into inverse treatment planning for low-dose-rate brachytherapy of hepatic malignancies

BACKGROUND

Leveraging the precision of its radiation dose distribution and the minimization of postoperative complications, low-dose-rate (LDR) permanent seed brachytherapy is progressively adopted in addressing hepatic malignancies.

PURPOSE

The present study endeavors to devise a sophisticated treatment planning system (TPS) to optimize LDR brachytherapy for hepatic lesions.

METHODS

Our TPS encompasses four integral modules: multi-organ segmentation, seed distribution initialization, puncture pathway selection, and inverse dose planning. By amalgamating an array of deep learning models, the segmentation module proficiently labels 17 discrete abdominal targets within the images. We introduce a knowledge-based seed distribution initialization methodology that discerns the most analogous tumor shape in the reference treatment plan from the knowledge base. Subsequently, the seed distribution from the reference plan is transmuted to the current case, thus establishing seed distribution initialization. Furthermore, we parameterize the puncture needles and seeds, while concurrently constraining the puncture needle angle through the employment of a virtual puncture panel to augment planning algorithm efficiency. We also presented a user interface that includes a range of interactive features, seamlessly integrated with the treatment planning generation function.

RESULTS

The multi-organ segmentation module, which is trained by 50 cases of in-house CT scans and 694 cases of publicly available CT scans, achieved average Dice of 0.80 and Hausdorff distance of 5.2 mm in testing datasets. The results demonstrate that knowledge-based initialization exhibits a marked enhancement in expediting the convergence rate. Our TPS also demonstrates a dominant advantage in dose-volume-histogram criteria and execution time in comparison to commercial TPS.

CONCLUSION

The study proposes an innovative treatment planning system for low-dose-rate permanent seed brachytherapy for hepatic malignancies. We show that the generated treatment plans meet clinical requirement.

A Monte Carlo dose recalculation pipeline for durable datasets: an I-125 LDR prostate brachytherapy use case

Monte Carlo (MC) dose datasets are valuable for large-scale dosimetric studies. This work aims to build and validate a DICOM-compliant automated MC dose recalculation pipeline with an application to the production of I-125 low dose-rate prostate brachytherapy MC datasets. Built as a self-contained application, the recalculation pipeline ingested clinical DICOM-RT studies, reproduced the treatment into the Monte Carlo simulation, and outputted a traceable and durable dose distribution in the DICOM dose format. MC simulations with TG43-equivalent conditions using both TOPAS andegs_brachyMC codes were compared to TG43 calculations to validate the pipeline. The consistency of the pipeline when generating TG186 simulations was measured by comparing simulations made with both MC codes. Finally,egs_brachysimulations were run on a 240-patient cohort to simulate a large-scale application of the pipeline. Compared to line source TG43 calculations, simulations with both MC codes had more than 90% of voxels with a global difference under ±1%. Differences of 2.1% and less were seen in dosimetric indices when comparing TG186 simulations from both MC codes. The large-scale comparison ofegs_brachysimulations with treatment planning system dose calculation seen the same dose overestimation of TG43 calculations showed in previous studies. The MC dose recalculation pipeline built and validated against TG43 calculations in this work efficiently produced durable MC dose datasets. Since the dataset could reproduce previous dosimetric studies within 15 h at a rate of 20 cases per 25 min, the pipeline is a promising tool for future large-scale dosimetric studies.

A novel and fast methodology to calculate doses in LDR brachytherapy

We present the concept of a new methodology for faster simulation of the doses in brachytherapy with permanent implants, based on the knowledge of the seeds arrangement, adding previously simulated doses in an equivalent medium in terms of the atomic composition of the organ in question. To perform the doses calculations we use Monte Carlo simulations. We simulated a cylindrical I-125 seed and compared our results against published data. Our proposal is to have the doses simulated previously in different arrangement of seed-absorbents, and then, considering the spacial positions of the seeds after the implants, these doses can be directly added, obtaining a very fast computation of the total dose. Two phantoms of prostates with permanent implant seeds in 2D and 3D arrangements were simulated. The results of the proposed methodology were compared with two complete Monte Carlo simulations in 2D and 3D designs. Differences in doses were analysed, obtaining statistical discrepancies of less than 1% and reducing the simulation time by more than 4 orders of magnitude. With the proposed methodology, it is possible to perform rapid dose calculations in brachytherapy, using laptop or desktop computers.

Validation of irtGPUMCD, a GPU-based Monte Carlo internal dosimetry framework for radionuclide therapy

PURPOSE

Monte Carlo (MC) simulations are highly desirable for dose treatment planning and evaluation in radiation oncology. This is true also in emerging nuclear medicine applications such as internal radiotherapy with radionuclides. The purpose of this study is the validation of irtGPUMCD, a GPU-based MC code for dose calculations in internal radiotherapy.

METHODS

The female and male phantoms of the International Commission on Radiological Protection (ICRP 110) were used as benchmarking geometries for this study focused on ¹⁷⁷Lu and including ^99mTc and ¹³¹I. Dose calculations were also conducted for a real patient. For phantoms, twelve anatomical structures were considered as target/source organs. The S-values were evaluated with irtGPUMCD simulations (10⁸ photons), with gamma branching ratios of ICRP 107 publication. The ¹⁷⁷Lu electrons S-values were calculated for source organs only, based on local deposition of dose in irtGPUMCD. The S-value relative difference between irtGPUMCD and IDAC-DOSE were evaluated for all targets/sources considered. A DVHs comparison with GATE was conducted. An exponential track length estimator was introduced in irtGPUMCD to increase computational efficiency.

RESULTS

The relative S-value differences between irtGPUMCD and IDAC-DOSE were <5% while this comparison with GATE was <1%. The DVHs dosimetric indices comparison between GATE and irtGPUMCD for the patient led to an excellent agreement (<2%). The time required for the simulation of 10⁸ photons was 1.5 min for the female phantom, and one minute for the real patient (<1% uncertainty). These results are promising and let envision the use of irtGPUMCD for internal dosimetry in clinical applications.

DVH-Based Inverse Planning Using Monte Carlo Dosimetry for LDR Prostate Brachytherapy

PURPOSE

Inverse planning is an integral part of modern low-dose-rate brachytherapy. Current clinical planning systems do not exploit the total dose information and largely use the American Association of Physicists in Medicine TG-43 dosimetry formalism to ensure clinically acceptable planning times. Thus, suboptimal plans may be derived as a result of TG-43-related dose overestimation and nonconformity with dose distribution requirements. The purpose of this study was to propose an inverse planning approach that can improve planning quality by combining dose-volume information and precision without compromising the overall execution times.

METHODS AND MATERIALS

The dose map was generated by accumulating precomputed Monte Carlo (MC) dose kernels for each candidate source implantation site. The MC computational burden was reduced by using graphics processing unit acceleration, allowing accurate dosimetry calculations to be performed in the intraoperative environment. The proposed dose-volume histogram (DVH) fast simulated annealing optimization algorithm was evaluated using clinical plans that were delivered to 18 patients who underwent low-dose-rate prostate brachytherapy.

RESULTS

Our method generated plans in 37.5 ± 3.2 seconds with similar prostate dose coverage, improved prostate dose homogeneity of up to 6.1%, and lower dose to the urethra of up to 4.0%.

CONCLUSIONS

A DVH-based optimization algorithm using MC dosimetry was developed. The inclusion of the DVH requirements allowed for increased control over the optimization outcome. The optimal plan's quality was further improved by considering tissue heterogeneity.

Determining optimal eluter design by modeling physical dose enhancement in brachytherapy

PURPOSE

In situ drug release concurrent with radiation therapy has been proposed to enhance the therapeutic ratio of permanent prostate brachytherapy. Both brachytherapy sources and brachytherapy spacers have been proposed as potential eluters to release compounds, such as nanoparticles or chemotherapeutic agents. The relative effectiveness of the approaches has not been compared yet. This work models the physical dose enhancement of implantable eluters in conjunction with brachytherapy to determine which delivery mechanism provides greatest opportunity to enhance the therapeutic ratio.

MATERIALS AND METHODS

The combined effect of implanted eluters and radioactive sources were modeled in a manner that allowed the comparison of the relative effectiveness of different types of implantable eluters over a range of parameters. Prostate geometry, source, and spacer positions were extracted from treatment plans used for 125 I permanent prostate implants. Compound concentrations were calculated using steady-state solution to the diffusion equation including an elimination term characterized by the diffusion-elimination modulus (ϕb ). Does enhancement was assumed to be dependent on compound concentration up to a saturation concentration (csat ). Equivalent uniform dose (EUD) was used as an objective to determine the optimal configuration of eluters for a range of diffusion-elimination moduli, concentrations, and number of eluters. The compound delivery vehicle that produced the greatest enhanced dose was tallied for points in parameter space mentioned to determine the conditions under whether there are situations where one approach is preferable to the other.

RESULTS

The enhanced effect of implanted eluters was calculated for prostate volumes from 14 to 45 cm3 , ϕb from 0.01 to 4 mm-1 , csat from 0.05 to 7.5 times the steady-state compound concentration released from the surface of the eluter. The number of used eluters (ne ) was simulated from 10 to 60 eluters. For the region of (csat , Φ)-space that results in a large fraction of the gland being maximally sensitized, compound eluting spacers or sources produce equal increase in EUD. In the majority of the remaining (csat , Φ)-space, eluting spacers result in a greater EUD than sources even where sources often produce greater maximal physical dose enhancement. Placing eluting implants in planned locations throughout the prostate results in even greater enhancement than using only source or spacer locations.

CONCLUSIONS

Eluting brachytherapy spacers offer an opportunity to increase EUD during the routine brachytherapy process. Incorporating additional needle placements permits compound eluting spacer placement independent of source placement and thereby allowing a further increase in the therapeutic ratio. Additional work is needed to understand the in vivo spatial distribution of compound around eluters, and to incorporate time dependence of both compound release and radiation dose.

Efficiency improvement in proton dose calculations with an equivalent restricted stopping power formalism

The equivalent restricted stopping power formalism is introduced for proton mean energy loss calculations under the continuous slowing down approximation. The objective is the acceleration of Monte Carlo dose calculations by allowing larger steps while preserving accuracy. The fractional energy loss per step length ϵ was obtained with a secant method and a Gauss-Kronrod quadrature estimation of the integral equation relating the mean energy loss to the step length. The midpoint rule of the Newton-Cotes formulae was then used to solve this equation, allowing the creation of a lookup table linking ϵ to the equivalent restricted stopping power L _eq, used here as a key physical quantity. The mean energy loss for any step length was simply defined as the product of the step length with L _eq. Proton inelastic collisions with electrons were added to GPUMCD, a GPU-based Monte Carlo dose calculation code. The proton continuous slowing-down was modelled with the L _eq formalism. GPUMCD was compared to Geant4 in a validation study where ionization processes alone were activated and a voxelized geometry was used. The energy straggling was first switched off to validate the L _eq formalism alone. Dose differences between Geant4 and GPUMCD were smaller than 0.31% for the L _eq formalism. The mean error and the standard deviation were below 0.035% and 0.038% respectively. 99.4 to 100% of GPUMCD dose points were consistent with a 0.3% dose tolerance. GPUMCD 80% falloff positions ([Formula: see text]) matched Geant's [Formula: see text] within 1 μm. With the energy straggling, dose differences were below 2.7% in the Bragg peak falloff and smaller than 0.83% elsewhere. The [Formula: see text] positions matched within 100 μm. The overall computation times to transport one million protons with GPUMCD were 31-173 ms. Under similar conditions, Geant4 computation times were 1.4-20 h. The L _eq formalism led to an intrinsic efficiency gain factor ranging between 30-630, increasing with the prescribed accuracy of simulations. The L _eq formalism allows larger steps leading to a [Formula: see text] algorithmic time complexity. It significantly accelerates Monte Carlo proton transport while preserving accuracy. It therefore constitutes a promising variance reduction technique for computing proton dose distributions in a clinical context.

Monte Carlo dose calculations for high-dose-rate brachytherapy using GPU-accelerated processing

PURPOSE

Current clinical brachytherapy dose calculations are typically based on the Association of American Physicists in Medicine Task Group report 43 (TG-43) guidelines, which approximate patient geometry as an infinitely large water phantom. This ignores patient and applicator geometries and heterogeneities, causing dosimetric errors. Although Monte Carlo (MC) dose calculation is commonly recognized as the most accurate method, its associated long computational time is a major bottleneck for routine clinical applications. This article presents our recent developments of a fast MC dose calculation package for high-dose-rate (HDR) brachytherapy, gBMC, built on a graphics processing unit (GPU) platform.

METHODS AND MATERIALS

gBMC-simulated photon transport in voxelized geometry with physics in (192)Ir HDR brachytherapy energy range considered. A phase-space file was used as a source model. GPU-based parallel computation was used to simultaneously transport multiple photons, one on a GPU thread. We validated gBMC by comparing the dose calculation results in water with that computed TG-43. We also studied heterogeneous phantom cases and a patient case and compared gBMC results with Acuros BV results.

RESULTS

Radial dose function in water calculated by gBMC showed <0.6% relative difference from that of the TG-43 data. Difference in anisotropy function was <1%. In two heterogeneous slab phantoms and one shielded cylinder applicator case, average dose discrepancy between gBMC and Acuros BV was <0.87%. For a tandem and ovoid patient case, good agreement between gBMC and Acruos BV results was observed in both isodose lines and dose-volume histograms. In terms of the efficiency, it took ∼47.5 seconds for gBMC to reach 0.15% statistical uncertainty within the 5% isodose line for the patient case.

CONCLUSIONS

The accuracy and efficiency of a new GPU-based MC dose calculation package, gBMC, for HDR brachytherapy make it attractive for clinical applications.