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This work shows the effectiveness of Monte Carlo methods for calibrating a dual-energy CT (DECT) system on a heterogeneous phantom for radiotherapy. The reference phantom comprising 20 inserts of different materials and densities representing human tissues inserted in a plastic water cylinder has been considered. The reliability of the model, built with the Penelope code, has been verified by comparing the results of the simulations at 80 kVp and 140 kVp with the experimental data acquired from the CT scans and dosimetry measurements. The effective atomic number from the various materials is also considered a control parameter. Tests for calibration and dose calculation are also based on a homogeneous phantom in PMMA and measurements in air with an ionization chamber. The Monte Carlo simulations coupled with dose evaluations allowed the calibration of the dose deposition effects on the considered tissue models. [ABSTRACT FROM AUTHOR]

Aim: The present work concerns the comparison of the performances of three systems for dosimetry in RPT that use different techniques for absorbed dose calculation (organ-level dosimetry, voxel-level dose kernel convolution and Monte Carlo simulations). The aim was to assess the importance of the choice of the most adequate calculation modality, providing recommendations about the choice of the computation tool. Methods: The performances were evaluated both on phantoms and patients in a multi-level approach. Different phantoms filled with a 177Lu-radioactive solution were used: a homogeneous cylindrical phantom, a phantom with organ-shaped inserts and two cylindrical phantoms with inserts different for shape and volume. A total of 70 patients with NETs treated by PRRT with 177Lu-DOTATOC were retrospectively analysed. Results: The comparisons were performed mainly between the mean values of the absorbed dose in the regions of interest. A general better agreement was obtained between Dose kernel convolution and Monte Carlo simulations results rather than between either of these two and organ-level dosimetry, both for phantoms and patients. Phantoms measurements also showed the discrepancies mainly depend on the geometry of the inserts (e.g. shape and volume). For patients, differences were more pronounced than phantoms and higher inter/intra patient variability was observed. Conclusion: This study suggests that voxel-level techniques for dosimetry calculation are potentially more accurate and personalized than organ-level methods. In particular, a voxel-convolution method provides good results in a short time of calculation, while Monte Carlo based computation should be conducted with very fast calculation systems for a possible use in clinics, despite its intrinsic higher accuracy. Attention to the calculation modality is recommended in case of clinical regions of interest with irregular shape and far from spherical geometry, in which Monte Carlo seems to be more accurate than voxel-convolution methods. [ABSTRACT FROM AUTHOR]

The use of Monte Carlo methods for the set up of Treatment Planning Systems (TPS) in radiotherapy applications is a current standard. The most advanced modeling techniques aim at directly link the output of CT scans to a patient-specific model build up instead of standard phantoms and look-up tables. This link represents a critical step since even the most accurate segmentation and organ volume definition must be translated into a suitable input for the Monte Carlo code. During that step, the segmented volume is usually mapped on a regular geometrical lattice (voxels). A more sophisticated option appears to be a volume description based on an Unstructured Mesh (UM) geometry typical of current finite element codes. In this paper, we compared the two approaches analyzing the bias induced by the different choices. Starting from anonymous patient DICOM files coming from a CT scan, suitable segmentation and volume definition have been carried out and voxel and UM-based equivalent models for the Monte Carlo code MCNP6 have been built. Some computational phantoms, covering some significant portion of the human body (head and lower limb), have been used as a benchmark of the dose distribution obtained from X-ray sources commonly used in orthovoltage applications. The specific clinical application has been chosen because, despite being used on a growing number of patients and with multi-Gy doses, treatment planning is still based on poorly characterized dose mapping systems such as look-up tables or low-resolution computational phantoms for MC codes. Also, experimental measurements on phantom slabs irradiated by an X-ray source were carried out preliminarily to validate the simulated radiation source. As shown in the results, the UM computational phantoms (built through the Simpleware SCAN-IP™ tool) can reduce the bias induced by the regularity of the classical cubic voxel geometry, give a more accurate description of volumes and complex surfaces and, thanks to an optimized discretization of the volumes, are also able to reduce the computational work. For the same UM models, various comparisons with different voxel sizes have been produced to investigate the dose distributions and evaluate the voxelization effects. The comparison shows a convergence pattern of the voxel model to the UM one. It has been possible to verify that the most significant bias occurs where the dose gradient is higher, along the beam borders and at tissue interfaces. The simulations showed that the UM can be really effective and reliable to compute the dose distributions within computational anthropomorphic phantoms obtained from the patient's CT scans. [ABSTRACT FROM AUTHOR]

Several kinds of isotopes are generated during radiotherapy treatments with high-energy electron sources due to the onset of many nuclear reactions. These isotopes are often unstable, can appear both in the device and in the treatment chamber materials and, as a consequence of the decay process, involving also gamma-ray emissions, some additional dose is given to the patient and to the radiotherapy unit staff. These effects have been experimentally monitored with a LaBr detector for gamma spectrometry. Then the measurement setup and data have been benchmarked through Monte Carlo (MC) simulations, with the MCNPX code, aiming to evaluate all kinds of activation, due to both photons and photoneutrons. All the MC activation estimates have been parameterized with respect to the187W produced in the primary collimator of the accelerator. The simulation results obtained with MCNPX have shown a good agreement with the experimental measurements. The results suggest a possible general approach to perform the activation analysis by coupling the experimental spectrometric measurements with MC calculations to properly identify photopeaks and source components. [ABSTRACT FROM PUBLISHER]