Validation of the MC-GPU Monte Carlo code against the PENELOPE/penEasy code system and benchmarking against experimental conditions for typical radiation qualities and setups in interventional radiology and cardiology

Comparative effectiveness of digital variance and subtraction angiography in lower limb angiography: A Monte Carlo modelling approach

OBJECTIVE

By modelling patient exposures of interventional procedures, this study compares the reduction of radiation detriment between Digital Variance Angiography (DVA) and Digital Subtraction Angiography (DSA).

METHODS

The paper presents a retrospective risk assessment using an in-house developed tool on 107 patient exposures from a clinical trial of DVA used to diagnose peripheral arterial disease (PAD). DICOM exposure parameters were used to initiate the PENELOPE (PENetration and Energy LOss of Positrons and Electrons) Monte Carlo simulation, radiation quality and quantity, and irradiation geometry. The effective dose and the lifetime attributable risk (LAR) for cancer incidence and mortality are calculated based on the International Commission on Radiation Protection's (ICRP) 103 recommendations and the Committee on the Biological Effects of Ionising Radiations' latest (BEIR VII) report, respectively.

RESULTS

The study found that procedures conducted using DVA significantly reduce the radiation exposure of patients, compared to DSA. The collective effective dose for the DVA group was 58% lower than that for the DSA group. Correspondingly, the LAR of different organs showed a substantial decrease for cancer incidence (25-75%) and mortality (51-84%).

CONCLUSION

DVA demonstrates a considerable reduction in physical dosimetric quantities and consequently effective dose and cancer risk, suggesting its potential as a safer alternative to DSA in interventional radiology. The use of DVA supports the optimisation of patient radiation protection and aligns with the principles of ALARA (as low as reasonably achievable).

Evaluation of radiation protection effectivity in a cardiac angiography room using visualized scattered radiation distribution

In this study, we devised a radiation protection tool specifically designed for healthcare professionals and students engaged in cardiac catheterization to easily monitor and evaluate scattered radiation distribution across diverse C-arm angles and arbitrary physician associated staff positions-scrub nurse and technologist positions. In this study, scattered radiation distributions in an angiography room were calculated using the Monte Carlo simulation of particle and heavy ion transport code system (PHITS) code. Four visualizations were performed under different C-arm angles with and without radiation protection: (1) a dose profile, (2) a 2D cross-section, (3) a 3D scattered radiation distribution, and (4) a 4D scattered radiation distribution. The simulation results detailing the scattered radiation distribution in PHITS were exported in Visualization Toolkit format and visualized through the open-source visualization application ParaView for analysis. Visualization of the scattered dose showed that dose distribution depends on the C-arm angle and the x-ray machine output parameters (kV, mAs s-1, beam filtration) which depend upon beam angulation to the patient body. When irradiating in the posterior-anterior direction, the protective curtain decreased the dose by 62% at a point 80 cm from the floor, where the physician's gonads are positioned. Placing the protection board close to the x-ray tube reduced the dose by 24% at a location 160 cm from the floor, where the lens of the eye is situated. Notably, positioning the protection board adjacent to the physician resulted in a 95.4% reduction in incident air kerma. These visualization displays can be combined to understand the spread and direction of the scattered radiation distribution and to determine where and how to operate and place radiation protection devices, accounting for the different beam angulations encountered in interventional cases. This study showed that scatter visualization could be a radiation protection teaching aid for students and medical staff in angiography rooms.

Simulation of X-ray projections on GPU: Benchmarking gVirtualXray with clinically realistic phantoms

BACKGROUND AND OBJECTIVES

This study provides a quantitative comparison of images created using gVirtualXray (gVXR) to both Monte Carlo (MC) and real images of clinically realistic phantoms. gVirtualXray is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes.

METHODS

Images are generated with gVirtualXray and compared with a corresponding ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. When real images are involved, the simulations are used in an image registration framework so that the two images are aligned.

RESULTS

The mean absolute percentage error (MAPE) between the images simulated with gVirtualXray and MC is 3.12%, the zero-mean normalised cross-correlation (ZNCC) is 99.96% and the structural similarity index (SSIM) is 0.99. The run-time is 10 days for MC and 23 ms with gVirtualXray. Images simulated using surface models segmented from a CT scan of the Lungman chest phantom were similar to (i) DRRs computed from the CT volume and (ii) an actual digital radiograph. CT slices reconstructed from images simulated with gVirtualXray were comparable to the corresponding slices of the original CT volume.

CONCLUSIONS

When scattering can be ignored, accurate images that would take days using MC can be generated in milliseconds with gVirtualXray. This speed of execution enables the use of repetitive simulations with varying parameters, e.g. to generate training data for a deep-learning algorithm, and to minimise the objective function of an optimisation problem in image registration. The use of surface models enables the combination of X-ray simulation with real-time soft-tissue deformation and character animation, which can be deployed in virtual reality applications.

Validation of organ dose calculations with PyMCGPU-IR in realistic interventional set-ups

INTRODUCTION

Interventional radiology procedures are associated with high skin dose exposure. The 2013/59/EURATOM Directive establishes that the equipment used for interventional radiology must have a device or a feature informing the practitioner of relevant parameters for assessing patient dose at the end of the procedure. This work presents and validates PyMCGPU-IR, a patient dose monitoring tool for interventional cardiology and radiology procedures based on MC-GPU. MC-GPU is a freely available Monte Carlo (MC) code of photon transport in a voxelized geometry which uses the computational power of commodity Graphics Processing Unit cards (GPU) to accelerate calculations.

METHODOLOGIES

PyMCGPU-IR was validated against two different experimental set-ups. The first one consisted of skin dose measurements for different beam angulations on an adult Rando Alderson anthropomorphic phantom. The second consisted of organ dose measurements in three clinical procedures using the Rando Alderson phantom.

RESULTS

The results obtained for the skin dose measurements show differences below 6%. For the clinical procedures the differences are within 20% for most cases.

CONCLUSIONS

PyMCGPU-IR offers both, high performance and accuracy for dose assessment when compared with skin and organ dose measurements. It also allows the calculation of dose values at specific positions and organs, the dose distribution and the location of the maximum doses per organ. In addition, PyMCGPU-IR overcomes the time limitations of CPU-based MC codes.

Technical note: MC-GPU breast dosimetry validations with other Monte Carlo codes and phase space file implementation

PURPOSE

To validate the MC-GPU Monte Carlo (MC) code for dosimetric studies in X-ray breast imaging modalities: mammography, digital breast tomosynthesis, contrast enhanced digital mammography, and breast-CT. Moreover, to implement and validate a phase space file generation routine.

METHODS

The MC-GPU code (v. 1.5 DBT) was modified in order to generate phase space files and to be compatible with PENELOPE v. 2018 derived cross-section database. Simulations were performed with homogeneous and anthropomorphic breast phantoms for different breast imaging techniques. The glandular dose was computed for each case and compared with results from the PENELOPE (v. 2014) + penEasy (v. 2015) and egs _ brachy (EGSnrc) MC codes. Afterward, several phase space files were generated with MC-GPU and the scored photon spectra were compared between the codes. The phase space files generated in MC-GPU were used in PENELOPE and EGSnrc to calculate the glandular dose, and compared with the original dose scored in MC-GPU.

RESULTS

MC-GPU showed good agreement with the other codes when calculating the glandular dose distribution for mammography, mean glandular dose for digital breast tomosynthesis, and normalized glandular dose for breast-CT. The latter case showed average/maximum relative differences of 2.3%/27%, respectively, compared to other literature works, with the larger differences observed at low energies (around 10 keV). The recorded photon spectra entering a voxel were similar (within statistical uncertainties) between the three MC codes. Finally, the reconstructed glandular dose in a voxel from a phase space file differs by less than 0.65%, with an average of 0.18%-0.22% between the different MC codes, agreement within approximately 2 σ statistical uncertainties. In some scenarios, the simulations performed in MC-GPU were from 20 up to 40 times faster than those performed by PENELOPE.

CONCLUSIONS

The results indicate that MC-GPU code is suitable for breast dosimetric studies for different X-ray breast imaging modalities, with the advantage of a high performance derived from GPUs. The phase space file implementation was validated and is compatible with the IAEA standard, allowing multiscale MC simulations with a combination of CPU and GPU codes.

XIORT-MC: A real-time MC-based dose computation tool for low- energy X-rays intraoperative radiation therapy

PURPOSE

The INTRABEAM system is a miniature accelerator for low-energy X-ray Intra-Operative Radiation Therapy (IORT), and it could benefit from a fast and accurate dose computation tool. With regards to accuracy, dose computed with Monte Carlo (MC) simulations are the gold standard, however, they require a large computational effort and consequently they are not suitable for real-time dose planning. This work presents a comparison of the implementation on Graphics Processing Unit (GPU) of two different dose calculation algorithms based on MC phase-space (PHSP) information to compute dose distributions for the INTRABEAM device within seconds and with the accuracy of realistic MC simulations.

METHODS

The MC-based algorithms we present incorporate photoelectric, Compton and Rayleigh effects for the interaction of low-energy X-rays. XIORT-MC (X-ray Intra-Operative Radiation Therapy Monte Carlo) includes two dose calculation algorithms; a Woodcock-based MC algorithm (WC-MC) and a Hybrid MC algorithm (HMC), and it is implemented in CPU and in GPU. Detailed MC simulations have been generated to validate our tool in homogeneous and heterogeneous conditions with all INTRABEAM applicators, including three clinically realistic CT-based simulations. A performance study has been done to determine the acceleration reached with the code, in both CPU and GPU implementations.

RESULTS

Dose distributions were obtained with the HMC and the WC-MC and compared to standard reference MC simulations with more than 95% voxels fulfilling a 7%-0.5 mm gamma evaluation in all the cases considered. The CPU-HMC is 100 times more efficient than the reference MC, and the CPU-WC-MC is about 50 times more efficient. With the GPU implementation, the particle tracking of the WC-MC is faster than the HMC, with the extraction of the particle's information from the PHSP file taking a major part of the time. However, thanks to the variance reduction techniques implemented in the HMC, up to 400 times less particles are needed in the HMC to reach the same level of noise than the WC-MC. Therefore, in our implementation for INTRABEAM energies, the HMC is about 1.3 times more efficient than the WC-MC in an NVIDIA GeForce GTX 1080 Ti card and about 5.5 times more efficient in an NVIDIA GeForce RTX 3090. Dose with noise below 5% has been obtained in realistic situations in less than 5 s with the WC-MC and in less than 0.5 s with the HMC.

CONCLUSIONS

The XIORT-MC is a dose computation tool designed to take full advantage of modern GPUs, making possible to obtain MC-grade accurate dose distributions within seconds. Its high speed allows a real-time dose calculation that includes the realistic effects of the beam in voxelized geometries of patients. It can be used as a dose-planning tool in the operating room during a XIORT treatment with any INTRABEAM device.

Fast Monte Carlo codes for occupational dosimetry in interventional radiology

PURPOSE

Interventional radiology techniques cause radiation exposure both to patient and personnel. The radiation dose to the operator is usually measured with dosimeters located at specific points above or below the lead aprons. The aim of this study is to develop and validate two fast Monte Carlo (MC) codes for radiation transport in order to improve the assessment of individual doses in interventional radiology. The proposed methodology reduces the number of required dosemeters and provides immediate dose results.

METHODS

Two fast MC simulation codes, PENELOPE/penEasyIR and MCGPU-IR, have been developed. Both codes have been validated by comparing fast MC calculations with the multipurpose PENELOPE MC code and with measurements during a realistic interventional procedure.

RESULTS

The new codes were tested with a computation time of about 120 s to estimate operator doses while a standard simulation needs several days to obtain similar uncertainties. When compared with the standard calculation in simple set-ups, MCGPU-IR tends to underestimate doses (up to 5%), while PENELOPE/penEasyIR overestimates them (up to 18%). When comparing both fast MC codes with experimental values in realistic set-ups, differences are within 25%. These differences are within accepted uncertainties in individual monitoring.

CONCLUSION

The study highlights the fact that computational dosimetry based on the use of fast MC codes can provide good estimates of the personal dose equivalent and overcome some of the limitations of occupational monitoring in interventional radiology. Notably, MCGPU-IR calculates both organ doses and effective dose, providing a better estimate of radiation risk.