Project Title: To be added
Project Title: Proton therapy range verification with polymer gel and point detector array dosimetry
Project Title: Modelling and Improving Lung Cancer treatment Outcomes Using Bayesian Network Averaging
Project Title: To be added.
Project Title: To be added.
Project Title: Rotating shield high dose rate brachytherapy with Gd-153 and Yb-169 .
Project Title: Multi-Points Plastic Scintillation Detectors for In Vivo Dosimetry.
Project Title: The decomposition of FDG-PET based differential uptake volume histograms in lung cancer patients
Project Title: Dynamic trajectory optimization for volumetric modulated arc therapy
Project Title: Improvement of Monte Carlo (MC) electron transport at low energies for accurate modeling of cellular radiation damage
Project Title: Small field dosimetry for GammaKnife
Project Title: Addressing the risk of secondary neutrons in radiotherapy through spectral measurements and track structure simulations .
Project Title: Development of a three-dimensional scintillation dosimetry system for external beam radiotherapy.
Project Title: Magnetic Resonance Imaging-based Dose Calculation and Verification for Four Dimensional Adaptive Radiotherapy.
Project Title: Quantum Dots as radiation nano-dosimeters/Caracterisation de la reponse des points quantiques CdSe soumis a la radiation ionisante
Project Title: Microdosimetrical and Radiobiological Comparison Between Different RadiationModalities in Radiation Therapy.
Project Title: Development of a Monte Carlo Platform for Dose Calculation and Dose Optimization in Brachytherapy using Graphic Processing Units
Project Title: Diffusion weighted magnetic resonance imaging of microstructures
Project Title: Quantitative susceptibility mapping with applications in cancer.
Project Title: Investigation of Cherenkov emission with applications in dosimetry and imaging in radiation therapy.
Project Title: Monte Carlo and experimental measurements to validate dose calculation algorithms in brachytherapy .
Project Title: To be added .
Project Title: Quantitative dynamic contrast enhanced MRI with reference region methods .
The objective of this work is to develop and implement trajectory-based radiotherapy in which the patient can be dynamically translated and/or rotated while radiation therapy is being delivered. The requisite calculation infrastructure for the optimization of trajectoryGbased treatments does not yet exist. Monte Carlo (MC) methods will be used to establish a beam model for different treatments (i.e. flattening filter free and regular treatment beams on the TrueBeam linear accelerator). From this work, MC calculated beamlets will be derived and inverse planned fluence pattern optimization will be applied to simple trajectories (i.e. synchronous circular motion of couch and gantry). Complex trajectories such as those that might be encountered in cranial stereotactic radiosurgery (SRS) will also be studied. In particular an efficient algorithm for the exploration of the parameter space that maps out permissible couch/gantry position combinations is required. An optimized trajectory will be calculated based on constraints imposed by radiobiological considerations (i.e. tumour control probability and normal tissue complication probability (NTCP)) as well as mechanical limitations of the linear accelerator (i.e. maximum permissible ranges of motion, travel speeds and a requirement for a “smooth” delivery). Finally, planning studies will be performed to evaluate the improvement in NTCP that could be expected based on improvements in dose conformity offered by both optimized trajectories and smaller projected MLC leaf widths using this treatment technique.
1. Joel Mullins†, Francois DeBlois, and A Syme (2017) Experimental characterization of the dosimetric leaf gap, Biomedical Physics & Engineering Express 2(6): 065013 .
With the advent of combined MRIGLinac systems becoming a reality, we believe that establishing a work-flow for MRIG based dose calculation and verification will improve tumour localization, reduce radiation dose to patients from image acquisition, and improve the accuracy of dose delivery through the adaptive process. The aims of the project are as follows:
In order to achieve aim (1), we plan on first investigating a variety of algorithms for the accuracy of registration (rigid and deformable) between a pre-planning CT set and an MRI mset to transpose electron density values to our MRI data. We will also explore the option of MRI-only simulation with no CT registration using atlas-based techniques such as the one outlined by Dowling et al1, or hybrid techniques combining fundamental and empirical methods. For aim (2), with a strong emphasis on general-purpose computing on graphics processing units (GPGPU), we will evaluate the performance and accuracy of segmentation algorithms for organ contouring on MR images over CT images. For dose calculations and optimization, we plan on using a Macro Monte Carlo technique, based on the work of Jabbari, developed during the course of my Master’s thesis to produce ands optimal treatment plan guided by the MRI data set. Finally, for aim (3), we will validate the workflow by looking specifically at Head & Neck cancer cases, which are known to have significant anatomical changes during the course of their treatment.
Quantification of physical uncertainties in calculating the position of the Bragg peak is essential to the safe and effective use of proton therapy. To achieve our research objectives of establishing clinical methods to reduce range uncertainty for proton therapy, we propose two complementary methods of range verification: (1) polymer gel dosimeters analyzed with both optical computed tomography (OCT) and magnetic resonance imaging (MRI) to evaluate the three-dimensional dose distribution of a therapeutic proton delivery prior to treatment, and (2) in vivo point detector measurements to perform adaptive beam energy adjustments for pediatric patients receiving passively scattered proton craniospainal irradiation.
Experimental Approach: Particle simulation studies using the Tool for Particle Simulation (TOPAS)
platform will optimize positioning of the point detector in the patient, establish minimum requisite dose for positional accuracy, and quantify range-mixing uncertainty. A fabrication protocol for the polymer gels will be established and validated in photon and electron beams at our institution. A T2 sequencing protocol for a 3.0 T MRI scanner to image the gels will be optimized by working with our institution’s MR physicist and the Philips as well as an OCT scanning protocol as part of a research agreement with the Modus Medical Devices.
Impact: The point detector array system performs a final range verification measurement, allowing for an adaptive adjustment of the beam energy. For sites where an intracavitary measurement is
impractical, a polymer gel dosimetry protocol could provide three-dimensional range verification for patient-specific quality assurance.
1. A. Toltz, Hoesl M, Schuemann J, Seuntjens J, Lu HM, Paganetti H (2017) Time-resolved diode dosimetry calibration through Monte Carlo modeling for in vivo passive scattered proton therapy range verification, J Appl Clin Med Phys. 2017 Nov;18(6):200-205.. doi: 10.1002/acm2.12210. [Epub ahead of print].
Monte Carlo (MC) simulations of radiation transport at low energies are of increasing importance for understanding radiation-induced damage of nanometer-scale cellular structures, such as the DNA (Nikjoo et al 2006,2008; Goodhead, 2006). Our approach will be todevelop detailed models of low-energy electron transport which are consistent with quantum theory. To first order we will follow the approach proposed by Liljequist, where quantum modeling is compared to classical track structure modeling in simplified systems. Research will entail the appropriate comparison metrics relevant for a specific end-point, e.g., DNA damage modeling versus damage in larger cellular structures. Classical track structure modeling largely only models elastic scattering. Therefore, a second topic of research will be to study the impact of inelastic scattering in track structure modeling. More realistic models of electron scattering in condensed media such as suggested by Caron and Sanche (2003) and Kaplan and Miterev (1985) will be applied to improve on existing approximate models of electron scattering in which water molecules are considered to be point scatterers and scattering is modeled as s-wave scattering. Finally, the new approach will be tested and validated against the established method.
Purpose: To investigate from first principles, corroborated by Monte Carlo simulations and experimental measurements, the feasibility of developing a Cherenkov emission (CE) dosimetry protocol and in vivo imaging system for radiotherapy.
Methods: Monte Carlo (MC) simulations of 4-18 MeV electrons incident on water were carried out in Geant4. Percent depth Cherenkov emission (PDCE) and dose (PDD) were scored. Analytical PDD prediction models were derived from first principles and evaluated with our simulation data. Experimental techniques for validation of these models are examined. Experimental phantoms include water and tissue-simulating phantom composed of water, Intralipid®, and beef blood. The detector system comprises an optical fiber and diffraction-grating CCD spectrometer. A spectral shift to the NIR window of biological tissue was carried out with CdSe/ZnS quantum dots (QDs), emitting at (650±10) nm.
Results: The MC simulations showed that, for all energies in the drop-off region, PDD was non-linear with PDCE at the same depth and linear with PDCE when a constant depth shift is applied. The build-up region behaviour was not investigated. Based on these findings, two PDD prediction models – non-linear (same depth) and linear (depth shift) – were derived. The PDD prediction power of the non-linear method over all depths up to the practical range varied from < 2% with 4 MeV to < 0.5% with 18 MeV electrons. The PDD prediction power of the depth shift method ranged from 3% with 4 MeV to 1% with 18 MeV electrons at the inflection point, with a minimum from 2% to 0.1% slightly upstream. These errors correspond to < 0.1 mm. Due to the angular anisotropy of the Cherenkov signal, experimental validation of these methods would require 3D acquisition or the use of an isotropically emitting fluorophore. CE by an 18-MeV beam was effectively NIR-shifted in water and a tissue-simulating phantom, exhibiting a signal increase at 650 nm for QD depths up to 20 mm in the latter.
Conclusion: We present robust quantitative prediction models, derived from first-principles and supported by simulation and measurement, for relative dose from Cherenkov emission by high-energy electrons and we demonstrate the use of QDs to improve CE detectability in tissue. This constitutes a major step towards development of protocols for routine clinical quality assurance as well as real-time in vivo Cherenkov dosimetry and imaging in radiotherapy.
1. Yana Zlateva and Issam El Naqa (2015) Cherenkov emission dosimetry for electron beam radiotherapy: a Monte Carlo feasibility study of relative and absolute dose prediction, Chapter from book Modeling current density maps in the heart (pp.828-831), DOI 10.1007/978-3-319-19387-8_203.
Les CT à énergie multiple (MECT) ou à double-énergie (DECT) deviendront rapidement accessible dans les départements de physique des radiations. Ils peuvent potentiellement amener des améliorations majeures entre autre pour la délimitation des zones tumorales ainsi que pour augmenter la précision de mesure du pouvoir d’arrêt massique en proton thérapie et carbone thérapie. Les premières étapes de l’étude démontrent que l’utilisation de DECT réduit l’erreur sur le pouvoir d’arrêt massique de 3% à 1.0%. De plus, l’étude à aussi suggérer que l’utilisation en combinaison avec les CT en proton peut réduire cette erreur d’un facteur 5. Les CT en proton et en carbone sont développés maintenant en utilisant la connaissance des DECT pour augmenter la précision sur les pouvoirs d’arrêt massique.
Les MECT peuvent aussi avoir un rôle majeur à jouer dans la délimitation de la tumeur entre autre à cause de large variance entre les observateurs lorsqu’ils délimitent la tumeur. Cette variance peut être aussi large que 10-15% et peut mener à de très larges volumes tumoraux non-nécessaires dus à ces incertitudes. Le projet va investiguer l’utilisation de plusieurs images CT à énergie unique (30,40 et 50 keV) pour augmenter le contraste entre la tumeur et les organes autours ayant une densité approximativement équivalente. La majorité du projet sera fait en Monte Carlo et/ou avec les logiciels d’imageries du département de Radio-Oncologie et Radiologie du MGH.
Brachytherapy allows for the delivery of large dose of radiation in a reduced number of fractions. Trials that further increase the dose to 1x19Gy as a monotherapy treatment for prostate are about to start. While not always delivery such large fraction, brachytherapy is often associated to a smaller numbers of larger dose compared to EBRT, which can extend of weeks. Thus any differences between the planned and delivered doses will have a large clinical impact. In vivo dosimetry is the only method to quantify the delivered dose. Our group has developed a world-renowned expertise is developing plastic scintillation dosimeter (PSD) and a commercial product (Exradin W1) steaming from that research is now available to EBRT dose measurements. More recently, we have proposed a new hyperspectral technology that allows us to have multiple plastic scintillating elements (mPSDs) on a single clear light collecting fiber. In this project, a database of spectrum and other properties will be built from the characteristics of known commercially available plastic scintillators, as well as crystalline scintillators in term of emission wavelength and emission efficiency. This should allow full numerical modeling of combinations of two, three or more mPSDs apparatus. To extend the number of viable options, the possibility of adding wavelength filters to scintillators to modify the spectra seen at the photodetector level will further be explored. This should provide us with the best potential mPSDs candidates to be built and tested experimentally. The accuracy of this process will also need to be assessed.
The introduction of advanced model-based dose calculation algorithms (MBDC) in BT will have profound and lasting consequences on current clinical practice. These codes allows medical physicists to visualize the effect of accurate dose calculation relative to TG43, but they are usually far from easy to operate and generally slow which limits their use for treatment optimization. Even when such algorithms are available, the treatment plan optimization is performed using the much faster TG43 protocol. We believe that unless fundamental research and developments are undertaken, these novel algorithms will only allow visualizing the dosimetry errors induced by TG43 but will not allow taking any actions during treatment plan optimization.
Current state-of-the-art MC codes while fast (20 to 60 seconds per calculation for low energy seed implants only) are tools to be used in dose optimization tasks. The only commercial MBDC algorithm can take up to 10 minutes to complete a dose calculation for a complex case of high energy 182Ir BT. Thus at this time no MBDC algorithms can be used to accomplish the necessary number of iterations needed by modern inverse planning algorithms within a reasonable time span (a few minutes maximum). A paradigm shift is needed and we are proposing migrating to GPU.
The proposed PhD project will tackle this shortcoming by the development and the validation of a fast and accurate graphic processing unit (GPU, which are massively parallel processors used in the video game industry) Monte Carlo dose calculation algorithm and GPU-based inverse planning algorithm.
Diffusion weighted magnetic resonance imaging (DW-MRI) is widely used for neural and oncological diagnosis and treatment evaluation. Recent development of advanced DW-MRI techniques such as AxCaliber and ActiveAx, enable the extraction of mean axonal diameter and axonal density in the human brain. The idea behind AxCaliber is that axons of different diameters will experience the switch between intra-axonal restricted diffusion to extra-axonal hindered diffusion at a different diffusion time (the time between diffusion encoding gradient pulses). By varying the diffusion time, thereby allowing water to diffuse for different amounts of time before signal collection, the estimation of the axonal diameter distribution is feasible. I believe that the samelogic applies to cancer cells. Intracellular water molecules have extensive interactions with cell membranes and intracellular compounds. Water may form 3D arrays in the presence of interfaces with charged materials such as organelle membranes or protein molecules, which hinders water motion to a greater extent compare to extracellular water molecules. By measuring diffusion over a range of different time periods, I propose that we can estimate the average cell diameters.The aim of my PhD project is to develop the technique for estimating cell diameters, to translate this technique to oncological imaging and to employ a model of water diffusion within cancer cells to estimate their diameter distribution within various sub-regions of a tumor. I will start by scanning tumor samples ex vivo, obtained with the help of clinical collaborators via tissue biopsy from an ongoing study of soft tissue sarcoma. I will also perform analysis of the cell diameter distribution of the same histology sample with optical microscopy to validate my measurements from DW-MRI. Lastly, I will conduct Monte-Carlo simulations of water diffusion to strengthen our understanding of water diffusion in the tumor microenvironment.
1. X Su, D Fang, Y Liu, G Ruan, J Seuntjens, JM Kinsella, SD Tran (2018) Lyophilized bone marrow cell extract functionally restores irradiation-injured salivary glands, Oral Diseases 24:202-206, DOI: 10.1111/odi.12728.
2. Shu Xing, Matthew W. Grol Peter H. Grutter, S. Jeffrey Dixon and Svetlana V. Komarova (2016) Modeling interactions amongst individual P2 receptors to explain complex response patterns over a wide range of ATP concentrations, Frontiers in Physiology 7, article 294, 14 pages, 13 Jul 2016.
1. Mirzakhanian L†, Benmakhlouf H, Tessier F, Seuntjens J. (2018) Determination of kQmsr,Q0fmsr,fref factors for ion chambers used in the calibration of Leksell Gamma Knife Perfexion model using EGSnrc and PENELOPE Monte Carlo codes, Med Phys. 2018 Apr;45(4):1748-1757. doi: 10.1002/mp.12821. Epub 2018 Mar 23.
1. M.A. Renaud, D. Roberge, J. Seuntjens (2015) Latent uncertainties of the pre-calculated track Monte Carlo method, Med. Phys. 42(1): 479.
2. Renaud MA†, Serban M, Seuntjens J (2017) On mixed electron-photon radiation therapy optimisation using the column generation approach, Med Phys. 44(8):4287-4298 doi: 10.1002/mp.12338, Epub 2017 Jun 30.
Abstract: The overall goal of the project is to develop a three-dimensional (3D) radiation dose detector system using a uniform volume of plastic scintillator and a light-field imager for medical physics applications in radiotherapy.
This project builds on the proof of concept that was previously published for a 3D scintillation dosimetry system. As a phantom, the dosimeter uses a water-equivalent plastic scintillator volume, characterized by a fluorescent light yield linearly dependent of its locally absorbed dose. The delivered three-dimensional dose distribution is reconstructed by applying pixel-by-pixel tomographic algorithms to images acquired using a light-field imager; each image contains spatial and directional information of incident photons and thus consists of a multi-focal plane measurement of the scintillator’s light field. To our knowledge, the proposed 3D detector device is currently the only medical physics tool potentially capable of measuring complete three dimensional radiation doses in near real-time. However, more work is needed to improve its performance and make it a usable tool to perform quality assurance of external beam radiation treatments.
The main goal of this PhD project is to develop a second generation prototype with improved temporal and spatial resolutions. A collaboration with the Center for Optics, Photonics and Lasers’ Optical Engineering research group has been established to optimize and design a system meeting the specifications required for such an improvement. Overall, this doctoral project aims to offer very precise, fast and user friendly 3D dose measurements to the radiotherapy community, allowing for truly comprehensive verification and knowledge of delivered radiation dose.
1. M. Goulet, M. Rilling, L. Gingras, S. Beddar, L. Beaulieu et L. Archambault, (2014) Novel, full 3D scintillation dosimetry using a static plenoptic camera, Med. Phys. 41(8) 082101-1-082101-13. Sylvia Fedrouk award, World Congress Toronto, June 7-12, 2015.
Abstract: Dynamic contrast enhanced (DCE) MRI provides information on blood supply in the body. This information is valuable in oncology since tumours are characterized by abnormal blood supply. Quantitative information, such as the rate of blood flow and cellular density, can be obtained by fitting DCE-MRI data to mathematical models. One such model is the reference region model (RRM) which is practical but suffers from a few limitations. Three major limitations will be explored in this project.
The first limitation of the RRM is that it does not account for the blood vessels which run through the tissue. These vessels are small and negligible in most healthy tissues, but tumours can have a high density of vessels. The first aim is to extend the RRM by including a fitting parameter that accounts for these blood vessels.
The second limitation is that the RRM provides values which can have high variability. The second aim is to reduce this variability by reducing the number of fitting parameters through a two-step approach.
The third limitation is that the RRM requires healthy tissue near the tumour to use as the reference region, but such a region is not always available. The third aim is to use parts of the tumour itself as a reference region.
The approaches developed in this project will be evaluated through simulations and will also be applied on soft tissue sarcoma data acquired from an on-going study at the RI-MUHC. .
1) Ahmed, Z. and Levesque I. (2016) Increased robustness in reference region model analysis of DCE MRI using two-step constrained approaches, Magn Reson Med. 2017 Oct;78(4):1547-1557 October 31, 2016, 10.1002/mrm.26530.
Bayesian network ensemble as a multivariate strategy to predict and improve lung radiotherapy outcomes Among cancer victims, lung cancer accounts for the most fatalities in men and women worldwide with a 5-year survival rate of only 15% showning no significant improvement over the past three decades. There is a need to design robust predictors of an individual patient’s prognosis prior to treatment with the intent of improving said prognosis. Our group has shown that the use of a systems-based approach which integrates dosimetric variables with relevant biomarkers extracted from blood specimans allows an accurate prediction of treatment response. I propouse using a probabilistic graphical model to intuitively depict and calculate the probability of tumour control or normal tissue complications after treatment. The model will then determine how the treatment plan maybe modified to optimize the prognosis. Ultimately the graphical model will be integrated into software to be used in clinics improving overall treatment success rates .
1) André Diamant, Avishek Chatterjee, Sergio Faria, Issam El Naqa, Houda Bahig, Edith Filion, Cliff Robinson, Hani Al-Halabi, Jan Seuntjens (2018) Can dose outside the PTV influence the risk of distant metastases in stage I lung cancer patients treated with stereotactic body radiotherapy (SBRT)?, Radiotherapy and Oncology, Available online 2018 May 22. doi.org/10.1016/j.radonc.2018.05.012. [Epub ahead of print].
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1) Famulari G†, Pater P, Enger SA, (2017) Microdosimetric evaluation of current and alternative Brachytherapy Sources-A Geant4-DNA Simulation Study, International Journal of Radiation Oncology • Biology • Physics, Vol. 100(1):270-277. DOI: doi: 10.1016/j.ijrobp.2017.09.040.
2) Famulari G†, Pater P, Enger SA, (2017) Microdosimetry calculations for monoenergetic electrons using Geant4-DNA combined with a weighted track sampling algorithm, Phys Med Biol. 2017 Jul 7;62(13):5495-5508 doi: 10.1088/1361-6560/aa71f6.
3) Famulari G†, Urlich T, Armstrong A, Enger SA, (2017) Practical aspects of 153Gd as a radioactive source for use in brachytherapy, Applied Radiation and Isotopes 130: 131-139.
A PET scan is performed by injecting a patient with a tracer which emits radiation that is collected and used to create a 30 image. The most common tracer is FOG, an analog for a sugar molecule. As the body’s cells consume sugar, an FOG-PET scan allows the mapping of sugar metabolism throughout the body, which has led to its widespread adoption in the study of cancer. Most cancers consume more sugar than healthy cells and so appear much brighter on a PET scan. In radiotherapy, imaging’s primary purpose is to define the gross tumour volume (GTV), the anatomical extent of disease. This is traditionally done with CT or MR imaging, which have excellent spatial resolution. Much effort has been spent determining how to redefine the GTV using PET to varied success. We believe that the biological information provided by PET should be used to complement and not compete with other modalities. Thus, our goal is to define and outline potential biological target volumes (BTVs) defined by metabolism. Our previous work investigating the abundance of these volumes in rectal cancer patients has demonstrated significant differences between patients with positive/negative responses to radiotherapy and we hope to compare, verify, and expand this for lung cancer patients. To this end, we are actively recruiting a retrospective cohort of 100 non-small cell lung cancer patients. To compare between patients, we will examine cancerous and healthy cells by taking a ratio of their signals in what we call signal-to-background ratio (SBR) images. We will then extract values found within the disease and plot the number of pixels corresponding to each SBR value. Afterwards, we define sub-volumes by determining the best fitting mathematical functions . By studying relationships between BTVs and patient outcome, we hope to advance radiotherapy treatment planning and evaluation .
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The signal in magnetic resonance imaging (MRI) depends on different intrinsic tissue properties, such as water proton density, relaxation times, and magnetic susceptibility. Recent work has shown that magnetic susceptibility, which reflects the tissue magnetization in response to an applied magnetic field, can be used to reconstruct susceptibility map with a technique called quantitative susceptibility mapping (QSM). With its capacity to quantify the magnetic susceptibility of tissues, QSM can give important information about tissue structure, composition and oxygenation level. Susceptibility mapping is under ongoing development, but it has already shown to be a useful tool in neuroimaging for iron content measurement and estimation of venous oxygen saturation. Most of the work done with QSM has been done in the brain, but we would like to transfer this technique to other parts of the body, in particular to study healthy tissue (liver, breast, muscle) and to explore the value of QSM in cancer tumour characterization. This is gaining interest in the community due to the presence of structures with a magnetic susceptibility different from soft tissue, such as calcium and iron. The main challenges related to QSM outside of the brain include organ motion, the presence of additional phase shift to fat, and the presence of large susceptibility differences, which cause rapid signal decay .
1) V. Fortier, I. Levesque (2017) Phase processing for quantitative susceptibility mapping of regions with large susceptibility and lack of signal, Magn. Res. Med. 2017 Nov 11. doi: 10.1002/mrm.26989. [Epub ahead of print].
Neutrons are produced as an unwanted byproduct when generating high-energy photon radiation therapy beams, and deliver a potentially dangerous whole-body dose to radiotherapy patients. Secondary neutrons are also produced when generating proton beams, which are of greatest potential benefit for pediatric patients but for whom iatrogenic second cancer induction is of greatest concern. However, the carcinogenic risk due to neutron radiation is poorly understood and we currently rely on highly-uncertain radiation weighting factors published by the International Commission on Radiological Protection. These factors suggest that the radiobiological effectiveness (and thus carcinogenic risk) of neutrons varies significantly with neutron energy with a peak effectiveness around 1 MeV, but for largely unknown reasons.
This project aims to elucidate the energy-dependent mechanisms by which neutrons deposit dose in the macroscopic and nanoscopic regimes, and thus better inform carcinogenic risk estimates due to neutron irradiation received during radiation therapy. The method will consist of development of macroscopic Monte Carlo simulations to score energy fluence spectra of neutron radiation and secondary particles generated in water by the primary neutrons. These spectra will then be inserted into nanoscopic neutron track structure simulations using GEANT4-DNA to quantify the amount of DNA damage (i.e. strand breaks, chromosomal aberrations, etc.) caused by primary neutron radiation of various energies.
Primary neutron radiation energies will be selected in accordance with measured neturon spectra around clinical electron and proton accelerators, as well as neutron beam energies available via collaboration with Canadian Nuclear Laboratories (CNL). This will allow cross-verification of simulated results with radiobiological experiments performed in analogous irradiation conditions .
1) Logan Montgomery, Palma Fava, Carolyn R. Freeman, Tarek Hijal, Ciro Maietta, William Parker, John Kildea (2017) Development and implementation of a radiation therapy incident learning system compatible with local workflow and a national taxonomy, Journal of Applied Clinical Medical Physics, 12 pages, First published November 22, 2017, DOI: 10.1002/acm2.12218.
The biological response to radiation, such as cell survival or DNA double-strand breaks (DSB), is quantified in terms of the relative biological effectiveness (RBE). This descriptor is used to measure the effectiveness of damage by various forms of ionizing radiation defined as the ratio of the dose from a reference radiation (250 kVp x-rays) to the dose of a studied source that achieves the same level of biological effect.
Due to the extensive approaches for radiation application in the clinical setting, there is a need to predict accurate RBE values as it plays an important function in the development of radiology-based treatment planning tools. Monte Carlo (MC) methods are accurate and rigorous tools for simulating radiation transport and score energy deposition in heterogeneous systems such as the human body. Since cellular response to radiation is affected by the microscopic distribution of energy deposition, MC track structure (MCTS) codes are implemented for investigations at the cellular and subcellular level.
Experimental studies implementing cell-culture models are needed to fill the biological gaps in our knowledge, and verify the simulation results. Brachytherapy Yb-169 and lr-192 sources and external beam radiation therapy will be investigated for the measurement of double-strand breaks and cell survival fraction. Three-dimensional (3D) cell-culture systems with the potential to more closely mimic in vivo conditions in an in vitro setting will be studied .
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In brachytherapy, the dose gradients are much more significant than in external beam in general and the competition between Compton scattering and the photoelectric effect, in particular for low energy brachytherapy, adds to the complexity of performing experimental measurements. In fact, the accuracy of source strength measurements by standard laboratories is less than for EBRT and the establishment of a primary standard of dose to water, while in the works, has proven a difficult task to accomplish. It is therefore quite a daunting task to ask every clinical physicist, especially in small centers, to perform experimental validation of new algorithms.
The project aims to develop numerical phantoms and build physical phantoms from the most interesting numerical ones to enable advanced quality control and quality assurance of new algorithms and delivery techniques for brachytherapy users.
This project is performed within the framework of Prof. Beaulieu industrial research chair .
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Le projet vise l’utilisation du concept de frontière stochastique (SFA) pour aider la
planification de traitement en radiothérapie. L’hypothèse du travail est qu’il est possible d’utiliser se modèle de nature économique pour identifier des cibles de planification réalistes. Contrairement à des approches de type ‘knowledge-basedplanning’, l’approche SFA ne devrait pas être pénalisée par l’inclusion de mauvais plans dans la banque de plan utilisé pour établir le modèle. Le travail de l’étudiante consistera premièrement à raffiner les outils logiciels mis en place par un étudiant précédent afin d’accroitre la rapidité du calcul et l’utilité générale de l’approche SFA. Dans une deuxième temps, l’étudiante pilotera une implantation clinique de son
approche afin d’en tester les performance dans un contexte réaliste.
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