Performance testing of dosimeters used in interventional radiology: Results from the VERIDIC project

Organ-based estimation and minimization of clinician's X-ray dose

PURPOSE

Hybrid surgeries, allowing real-time visualization of patient inner anatomy, are possible through the use of intraoperative X-ray imaging. However, the intensive use of X-rays can have undesired consequences for the clinicians or the patient in the operating room (OR).

METHODS

In this paper, we provide a tool to visualize the X-rays and to optimally place protective shields in the hybrid operating room to reduce the clinician's dose according to their most sensitive body parts. We first acquire measurements in a hybrid operating room with dosimeters placed at different locations on a mannequin simulating a clinician. We demonstrate that a small displacement of a protective shield has significant consequences on the dose received by a clinician. Then, we reproduce the scene virtually and use Monte Carlo simulations to estimate the dose received by the clinician. Finally, we optimally place protective shields with a Nelder-Mead-based numerical optimization algorithm.

RESULTS

The results show a high sensitivity of the clinician's dose to protective shield placement. Numerical optimization of the shields' placement can help to reduce the dose and show a decrease between 79 and 89% of the exposition when comparing no external shield protection and our optimal external shield position.

CONCLUSION

Our work can help to raise awareness of the risks induced by X-rays during intraoperative surgery and reduce the dose received by the clinicians. In future work, our approach can be linked with human pose estimation algorithms to trace surgeons' moves, estimate dynamically the dose and summarize it in a surgical report, giving the dose for important organs.

Experimental Validation of an Analytical Program and a Monte Carlo Simulation for the Computation of the Far Out-of-Field Dose in External Beam Photon Therapy Applied to Pediatric Patients

Background

The out-of-the-field absorbed dose affects the probability of primary second radiation-induced cancers. This is particularly relevant in the case of pediatric treatments. There are currently no methods employed in the clinical routine for the computation of dose distributions from stray radiation in radiotherapy. To overcome this limitation in the framework of conventional teletherapy with photon beams, two computational tools have been developed—one based on an analytical approach and another depending on a fast Monte Carlo algorithm. The purpose of this work is to evaluate the accuracy of these approaches by comparison with experimental data obtained from anthropomorphic phantom irradiations.

Materials and Methods

An anthropomorphic phantom representing a 5-year-old child (ATOM, CIRS) was irradiated considering a brain tumor using a Varian TrueBeam linac. Two treatments for the same planned target volume (PTV) were considered, namely, intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). In all cases, the irradiation was conducted with a 6-MV energy beam using the flattening filter for a prescribed dose of 3.6 Gy to the PTV. The phantom had natLiF : Mg, Cu, P (MCP-N) thermoluminescent dosimeters (TLDs) in its 180 holes. The uncertainty of the experimental data was around 20%, which was mostly attributed to the MCP-N energy dependence. To calculate the out-of-field dose, an analytical algorithm was implemented to be run from a Varian Eclipse TPS. This algorithm considers that all anatomical structures are filled with water, with the exception of the lungs which are made of air. The fast Monte Carlo code dose planning method was also used for computing the out-of-field dose. It was executed from the dose verification system PRIMO using a phase-space file containing 3x109 histories, reaching an average standard statistical uncertainty of less than 0.2% (coverage factor k = 1 ) on all voxels scoring more than 50% of the maximum dose. The standard statistical uncertainty of out-of-field voxels in the Monte Carlo simulation did not exceed 5%. For the Monte Carlo simulation the actual chemical composition of the materials used in ATOM, as provided by the manufacturer, was employed.

Results

In the out-of-the-field region, the absorbed dose was on average four orders of magnitude lower than the dose at the PTV. For the two modalities employed, the discrepancy between the central values of the TLDs located in the out-of-the-field region and the corresponding positions in the analytic model were in general less than 40%. The discrepancy in the lung doses was more pronounced for IMRT. The same comparison between the experimental and the Monte Carlo data yielded differences which are, in general, smaller than 20%. It was observed that the VMAT irradiation produces the smallest out-of-the-field dose when compared to IMRT.

Conclusions

The proposed computational methods for the routine calculation of the out-of-the-field dose produce results that are similar, in most cases, with the experimental data. It has been experimentally found that the VMAT irradiation produces the smallest out-of-the-field dose when compared to IMRT for a given PTV.

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.

UNCERTAINTY ASSOCIATED WITH THE USE OF SOFTWARE SOLUTIONS UTILIZING DICOM RDSR FOR SKIN DOSE ASSESSMENT IN INTERVENTIONAL RADIOLOGY AND CARDIOLOGY

PURPOSE

The purpose of this work is to provide a comprehensive analysis of uncertainties associated with the use of software solutions utilizing DICOM RDSRs for skin dose assessment in the interventional fluoroscopic environment.

METHODS AND RESULTS

Three different scenarios have been defined for determining the overall uncertainty, each with a specific assumption on the maximum deviations of factors affecting the calculated dose. Relative expanded uncertainty has been calculated using two approaches: the law of propagation of uncertainty and the propagation of distributions based on the Monte Carlo method. According to the propagation of uncertainty, it is estimated that the lowest possible relative expanded uncertainty of ~13% (at the 95% level of confidence, i.e. with the coverage factor of k = 2 assuming normal distribution) could only be achieved if all sources of uncertainties are carefully controlled, whereas maximum relative expanded uncertainty could reach up to 61% if none of the influencing parameters are controlled properly. When the influencing parameters are reasonably well-controlled, realistic relative expanded uncertainty amounts to 28%. Values for the relative expanded uncertainty obtained from the Monte Carlo propagation of distributions concur with the results obtained from the propagation of uncertainty to within 3% in all three considered scenarios, validating the assumption of normality.

CONCLUSIONS

The overall skin dose relative uncertainty has been found to range from 13 to 61%, emphasizing the importance of adequate analysis and control of all relevant uncertainty sources.