Monte Carlo calculation based on hydrogen composition of the tissue for MV photon radiotherapy

Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning

PURPOSE

This work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.

METHODS

The MLC-equipped M6 Cyberknife was modelled and integrated in Moderato, our in-house platform offering independent verification of radiotherapy dose distributions. The model was validated by comparing TPS dose distributions with Moderato and by film measurements. Using this model, a machine learning algorithm was trained to find electron beam parameters for other M6 devices, by simulating dose curves with varying spot size and energy. The algorithm was optimized using cross-validation and tested with measurements from other institutions equipped with a M6 Cyberknife.

RESULTS

Optimal agreement in the Monte Carlo model was reached for a monoenergetic electron beam of 6.75 MeV with Gaussian spatial distribution of 2.4 mm FWHM. Clinical plan dose distributions from Moderato agreed within 2% with the TPS, and film measurements confirmed the accuracy of the model. Cross-validation of the prediction algorithm produced mean absolute errors of 0.1 MeV and 0.3 mm for beam energy and spot size respectively. Prediction-based simulated dose curves for other centres agreed within 3% with measurements, except for one device where differences up to 6% were detected.

CONCLUSIONS

The M6 Cyberknife was integrated in Moderato and validated through dose re-calculations and film measurements. The prediction algorithm was successfully applied to obtain electron beam parameters for other M6 devices. This method would prove useful to speed up modelling of new machines in Monte Carlo systems.

[MRI-based radiotherapy planning]

MRI-based radiotherapy planning is a topical subject due to the introduction of a new generation of treatment machines combining a linear accelerator and a MRI. One of the issues for introducing MRI in this task is the lack of information to provide tissue density information required for dose calculation. To cope with this issue, two strategies may be distinguished from the literature. Either a synthetic CT scan is generated from the MRI to plan the dose, or a dose is generated from the MRI based on physical underpinnings. Within the first group, three approaches appear: bulk density mapping assign a homogeneous density to different volumes of interest manually defined on a patient MRI; machine learning-based approaches model local relationship between CT and MRI image intensities from multiple data, then applying the model to a new MRI; atlas-based approaches use a co-registered training data set (CT-MRI) which are registered to a new MRI to create a pseudo CT from spatial correspondences in a final fusion step. Within the second group, physics-based approaches aim at computing the dose directly from the hydrogen contained within the tissues, quantified by MRI. Excepting the physics approach, all these methods generate a synthetic CT called "pseudo CT", on which radiotherapy planning will be finally realized. This literature review shows that atlas- and machine learning-based approaches appear more accurate dosimetrically. Bulk density approaches are not appropriate for bone localization. The fastest methods are machine learning and the slowest are atlas-based approaches. The less automatized are bulk density assignation methods. The physical approaches appear very promising methods. Finally, the validation of these methods is crucial for a clinical practice, in particular in the perspective of adaptive radiotherapy delivered by a linear accelerator combined with an MRI scanner.

Use of an in-house Monte Carlo platform to assess the clinical impact of algorithm-related dose differences on DVH constraints

PURPOSE

The aim of the present work is to evaluate a semi-automatic prescription and validation system of treatment plans for complex delivery techniques, integrated in a Monte Carlo platform, and to investigate the clinical impact of dose differences due to the calculation algorithms, by assessing the changes in DVH constraints.

METHODS

A new prescription module was implemented into the Moderato system, an in-house Monte Carlo platform, with corresponding dose constraints generated depending on the anatomical region and fractionation scheme considered. The platform was tested on 83 cases treated with Cyberknife and Tomotherapy machines, to assess whether dose variations between the re-calculated dose and the Treatment Planning System might impact the dose constraints on the sensitive structures.

RESULTS

Dose differences were small (within 3%) between calculation algorithms in most of the thoracic, pelvic and abdominal cases, both for the Cyberknife and Tomotherapy machines. On the other hand, spinal and head and neck treatments presented a few significant dose deviations for constraints on small volumes, such as the optic pathways and the spinal cord. These differences range from -11% to +6%, inducing constraint violations of up to 8% over the dose limit.

CONCLUSIONS

The Moderato platform offers an interesting tool for plan quality validation, with a prescription module highlighting crucial features in the structures list, and a Monte Carlo dose re-calculation for complex modern techniques. Due to the high number of warnings appearing in some situations, display optimization is required in practice.

Dosimetric characterization of MRI-only treatment planning for brain tumors in atlas-based pseudo-CT images generated from standard T1-weighted MR images

PURPOSE

Magnetic resonance imaging (MRI)-only radiotherapy treatment planning requires accurate pseudo-CT (pCT) images for precise dose calculation. The current work introduced an atlas-based method combined with MR intensity information. pCT analyses and Monte Carlo dose calculations for intracranial stereotactic treatments were performed.

METHODS

Twenty-two patients, representing 35 tumor targets, were scanned using a 3D T1-weighted MRI sequence according to the clinical protocol. The MR atlas image was registered to the MR patient image using a deformable algorithm, and the deformation was then applied to the atlas CT. Two methods were applied. The first method (MRdef) was based on deformations only, while the second (MRint) also used the actual MR intensities. pCT analysis was performed using the mean (absolute) error, as well as an in-house tool based on a gamma index. Dose differences between pCT and true CT were analyzed using dose-volume histogram (DVH) parameters, statistical tests, the gamma index, and probability density functions. An unusual case, where the patient underwent an operation (part of the skull bone was removed), was studied in detail.

RESULTS

Soft tissues presented a mean error inferior to 50 HUs, while low-density tissues and bones presented discrepancies up to 600 HUs for hard bone. The MRdef method led to significant dose differences compared with the true CT (p-value < 0.05; Wilcoxon-signed-rank test). The MRint method performed better. The DVH parameter differences compared with CT were between -2.9% and 3.1%, except for two cases where the tumors were located within the sphenoid bone. For these cases, the dose errors were up to 6.6% and 5.4% (D₉₈ and D₉₅). Furthermore, for 85% of the tested patients, the mean dose to the planning target volume agreed within 2% with the calculation using the actual CT. Fictitious bone was generated in the unusual case using atlas-based methods.

CONCLUSIONS

Generally, the atlas-based method led to acceptable dose distributions. The use of common T1 sequences allows the implementation of this method in clinical routine. However, unusual patient anatomy may produce large dose calculation errors. The detection of large anatomic discrepancies using MR image subtraction can be realized, but an alternative way to produce synthetic CT numbers in these regions is still required.