Introduction: Interventional procedures are associated with potentially high radiation doses to the skin. º Monte Carlo codes of radiation transport are considered to be one of the most reliable tools available to assess doses. However, they are usually too time consuming for use in clinical practice. This work presents the validation of the fast Monte Carlo code MC-GPU for application in interventional radiology. Methodologies: MC-GPU calculations were compared against the well-validated Monte Carlo simulation code PENELOPE/penEasy by simulating the organ dose distribution in a voxelized anthropomorphic phantom. In a second phase, the code was compared against thermoluminescent measurements performed on slab phantoms, both in a calibration laboratory and at a hospital. Results: The results obtained from the two simulation codes show very good agreement, differences in the output were within 1%, whereas the calculation time on the MC-GPU was 2500 times shorter. Comparison with measurements is of the order of 10%, within the associated uncertainty. Conclusions: It has been verified that MC-GPU provides good estimates of the dose when compared to PENELOPE program. It is also shown that it presents very good performance when assessing organ doses in very short times, less than one minute, in real clinical set-ups. Future steps would be to simulate complex procedures with several projections.
PyMCGPU-IR is an innovative occupational dose monitoring tool for interventional radiology procedures. It reads the radiation data from the Radiation Dose Structured Report of the procedure and combines this information with the position of the monitored worker recorded using a 3D camera system. This information is used as an input file for the fast Monte Carlo radiation transport code MCGPU-IR in order to assess the organ doses, Hp(10) and Hp(0.07), as well as the effective dose. In this study, Hp(10) measurements of the first operator during an endovascular aortic aneurysm repair procedure and a coronary angiography using a ceiling suspended shield are compared to PyMCGPU-IR calculations. Differences in the two reported examples are found to be within 15%, which is considered as being very satisfactory. The study highlights the promising advantages of PyMCGPU-IR, although there are still several improvements that need to be implemented before its final clinical use.
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