An algorithm for kilovoltage x-ray dose calculations with applications in kV-CBCT scans and 2D planar projected radiographs

A novel analytical method for computing dose from kilovoltage beams used in Image-Guided radiation therapy

PURPOSE

A modified convolution/superposition algorithm is proposed to compute dose from the kilovoltage beams used in IGRT. The algorithm uses material-specific energy deposition kernels instead of water-energy deposition kernels.

METHODS

Monte Carlo simulation was used to model the Elekta XVI unit and determine dose deposition characteristics of its kilovoltage beams. The dosimetric results were compared with ion chamber measurements. The dose from the kilovoltage beams was then computed using convolution/superposition along with material-specific energy deposition kernels and compared with Monte Carlo and measurements. The material-specific energy deposition kernels were previously generated using Monte Carlo.

RESULTS

The obtained gamma indices (using 2%/2mm criteria for 95% of points) were lower than 1 in almost all instances which indicates good agreement between simulated and measured depth doses and profiles. The comparisons of the algorithm with measurements in a homogeneous solid water slab phantom, and that with Monte Carlo in a head and neck CT dataset produced acceptable results. The calculated point doses were within 4.2% of measurements in the homogeneous phantom. Gamma analysis of the calculated vs. Monte Carlo simulations in the head and neck phantom resulted in 94% of points passing with a 2%/2mm criteria.

CONCLUSIONS

The proposed method offers sufficient accuracy in kilovoltage beams dose calculations and has the potential to supplement the conventional megavoltage convolution/superposition algorithms for dose calculations in low energy range.

Generation of material-specific energy deposition kernels for kilovoltage x-ray dose calculations

PURPOSE

Dose calculation of kilovoltage x rays used in Image-Guided Radiotherapy has been investigated in recent years using various methods. Among these methods are model-based ones that suffer from inaccuracies in high-density materials and at interfaces when used in the kilovoltage energy range. The main reason for this is the use of water energy deposition kernels and simplifications employed such as density scaling in heterogeneous media. The purpose of this study was to produce and characterize material-specific energy deposition kernels, which could be used for dose calculations in this energy range. These kernels will also have utility in dose calculations in superficial radiation therapy and orthovoltage beams utilized in small animal irradiators.

METHODS

Water energy deposition kernels with various resolutions; and high-resolution, material-specific energy deposition kernels were generated in the energy range of 10-150 kVp, using the EGSnrc Monte Carlo toolkit. The generated energy deposition kernels were further characterized by calculating the effective depth of penetration, the effective radial distance, and the effective lateral distance. A simple benchmarking of the kernels against Monte Caro calculations has also been performed.

RESULTS

There was good agreement with previously reported water kernels, as well as between kernels with different resolution. The evaluation of effective depth of penetration, and radial and laterals distances, defines the relationship between energy, material density, and the shape of the material-specific kernels. The shape of these kernels becomes more forwardly scattered as the energy and material density are increased. The comparison of the dose calculated using the kernels with Monte Carlo provides acceptable results.

CONCLUSIONS

Water and material-specific energy deposition kernels in the kilovoltage energy range have been generated, characterized, and compared to previous work. These kernels will have utility in dose calculations in this energy range once algorithms capable of employing them are fully developed.

Artificial intelligence in radiotherapy: a technological review

Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.

Image guidance doses delivered during radiotherapy: Quantification, management, and reduction: Report of the AAPM Therapy Physics Committee Task Group 180

BACKGROUND

With radiotherapy having entered the era of image guidance, or image-guided radiation therapy (IGRT), imaging procedures are routinely performed for patient positioning and target localization. The imaging dose delivered may result in excessive dose to sensitive organs and potentially increase the chance of secondary cancers and, therefore, needs to be managed.

AIMS

This task group was charged with: a) providing an overview on imaging dose, including megavoltage electronic portal imaging (MV EPI), kilovoltage digital radiography (kV DR), Tomotherapy MV-CT, megavoltage cone-beam CT (MV-CBCT) and kilovoltage cone-beam CT (kV-CBCT), and b) providing general guidelines for commissioning dose calculation methods and managing imaging dose to patients.

MATERIALS & METHODS

We briefly review the dose to radiotherapy (RT) patients resulting from different image guidance procedures and list typical organ doses resulting from MV and kV image acquisition procedures.

RESULTS

We provide recommendations for managing the imaging dose, including different methods for its calculation, and techniques for reducing it. The recommended threshold beyond which imaging dose should be considered in the treatment planning process is 5% of the therapeutic target dose.

DISCUSSION

Although the imaging dose resulting from current kV acquisition procedures is generally below this threshold, the ALARA principle should always be applied in practice. Medical physicists should make radiation oncologists aware of the imaging doses delivered to patients under their care.

CONCLUSION

Balancing ALARA with the requirement for effective target localization requires that imaging dose be managed based on the consideration of weighing risks and benefits to the patient.

A point kernel algorithm for microbeam radiation therapy

Microbeam radiation therapy (MRT) is a treatment approach in radiation therapy where the treatment field is spatially fractionated into arrays of a few tens of micrometre wide planar beams of unusually high peak doses separated by low dose regions of several hundred micrometre width. In preclinical studies, this treatment approach has proven to spare normal tissue more effectively than conventional radiation therapy, while being equally efficient in tumour control. So far dose calculations in MRT, a prerequisite for future clinical applications are based on Monte Carlo simulations. However, they are computationally expensive, since scoring volumes have to be small. In this article a kernel based dose calculation algorithm is presented that splits the calculation into photon and electron mediated energy transport, and performs the calculation of peak and valley doses in typical MRT treatment fields within a few minutes. Kernels are analytically calculated depending on the energy spectrum and material composition. In various homogeneous materials peak, valley doses and microbeam profiles are calculated and compared to Monte Carlo simulations. For a microbeam exposure of an anthropomorphic head phantom calculated dose values are compared to measurements and Monte Carlo calculations. Except for regions close to material interfaces calculated peak dose values match Monte Carlo results within 4% and valley dose values within 8% deviation. No significant differences are observed between profiles calculated by the kernel algorithm and Monte Carlo simulations. Measurements in the head phantom agree within 4% in the peak and within 10% in the valley region. The presented algorithm is attached to the treatment planning platform VIRTUOS. It was and is used for dose calculations in preclinical and pet-clinical trials at the biomedical beamline ID17 of the European synchrotron radiation facility in Grenoble, France.

Imaging dose from cone beam computed tomography in radiation therapy

Imaging dose in radiation therapy has traditionally been ignored due to its low magnitude and frequency in comparison to therapeutic dose used to treat patients. The advent of modern, volumetric, imaging modalities, often as an integral part of linear accelerators, has facilitated the implementation of image-guided radiation therapy (IGRT), which is often accomplished by daily imaging of patients. Daily imaging results in additional dose delivered to patient that warrants new attention be given to imaging dose. This review summarizes the imaging dose delivered to patients as the result of cone beam computed tomography (CBCT) imaging performed in radiation therapy using current methods and equipment. This review also summarizes methods to calculate the imaging dose, including the use of Monte Carlo (MC) and treatment planning systems (TPS). Peripheral dose from CBCT imaging, dose reduction methods, the use of effective dose in describing imaging dose, and the measurement of CT dose index (CTDI) in CBCT systems are also reviewed.