Detector Development • EngerLab

Mission

The detector development group works to build novel radiation detectors to solve clinical problems. We use a mix of computer simulations lab-based tests and clinical simulations to develop precise and easy use detectors that can be easily implemented in clinical practice.

Members

Projects

Dosimetry Detectors

Jingyi Bian, Víctor Diaz

Dosimetry is the practice of quantitatively determining the energy deposited in a medium by ionizing radiation. The major challenge in radiotherapy is to deliver and quantify an accurate dose of radiation to the tumor. Several dosimetry techniques are available to measure the radiation dose, but they have limitations. In particular, the non-tissue equivalence of the detector housing/material and the sensitive volume leads to perturbations of the radiation fluence and necessitates correction factors. Another constraint placed on conventional dosimetry techniques is the limited resolution. Currently, there are many ongoing research projects regarding detector development for quality assurance and in-vivo dosimetry in radiotherapy.

Imaging Detector

Liam Carroll

Dynamic positron emission tomography is a nuclear medicine imaging technique providing functional images for organs of interest with applications in oncology, cardiology, and drug discovery. This technique requires the acquisition of the time-course arterial plasma activity concentration, called the arterial input function, which is normally acquired invasively via arterial blood sampling. Currently, we are developing a radiation detector to measure the arterial input function non-invasively.

Detector Group Publications

2022

Carroll, Liam; Enger, Shirin A.

Second prize at the International Conference on Monte Carlo Techniques for Medical Applications award

2022.

Abstract | BibTeX

@award{nokey,

title = {Second prize at the International Conference on Monte Carlo Techniques for Medical Applications },

author = {Liam Carroll and Shirin A. Enger},

year  = {2022},

date = {2022-04-13},

urldate = {2022-04-13},

journal = {International Conference on Monte Carlo Techniques for Medical Applications },

abstract = {Introduction

Geant4[1] is a Monte Carlo toolkit that provides a flexible platform to design radiation transport simulations. This flexibility requires a high level of complexity when writing new user-codes. New users must undergo extensive training to begin writing useful simulations. The aim of this study was to develop a modular radiation simulation software package called MaRSS based on Geant4 user-code to serve as both an educational tool and as a simulation tool for medical radiation detector simulations. 



Materials & Methods

MaRSS builds on Geant4 using Penelope electromagnetic physics models and cross-sections[2]. To give the users possibility to change simulation parameters without changing the source code, MaRSS is equipped with a set of messenger classes which are intercom modules provided by Geant4 to configure applications and provide user interactivity with the code. These messenger classes add additional user commands that can be used to add or remove volumes from the simulation geometry and associated sensitive detectors. The sensitive detectors are objects that Geant4 uses to save simulation results. A number of default volumes and detectors are included in MaRSS that can be used for the design of scintillating fiber-based radiation detectors. MaRSS adds to the existing Geant4 sensitive detector code by creating a new base class called RunSD that is inherited by all sensitive detector objects. RunSD is implemented such that the code needed to initialize a sensitive detector is included in two classes, normally, this code is spread out in several classes. A similar approach is taken with geometrical volumes. To validate MaRSS, range in water of positrons emitted from four radioisotopes commonly used for positron emission tomography was calculated and compared with published work: Fluorine-18 (18F), Carbon-11 (11C), Oxygen-15 (15O) and Gallium-68 (68Ga). A sphere with a radius of 1 m was filed with water. For each radioisotope, 100 million decay events were simulated originating at the center of the simulated water sphere. The resulting positrons were allowed to annihilate. Two energy cuts were simulated, 1 keV and 0.1 keV. Two histograms were created, one using the annihilation locations and another with the energy of the emitted positrons. Results were compared with published positron range values calculated with a PENELOPE[2]-based Monte Carlo software called PeneloPET [3], an analytical expression to estimate the range of positrons described by Cal-Gonzales et.al.[3], a simulation by Lehnert et. al. [4] written using GATE[5] and a separate Monte Carlo software written by Champion and Le Loirec[6] that directly simulates the formation of positronium in water to calculate positron range.



Results

Figure 1 shows the mean calculated positron ranges and calculated positron emission energies compared to literature values. Simulated positron energy means were within 1.8% of literature values. Simulated ranges were within 2% of GATE simulation[4]. 



References

[1]        S. Agostinelli et al., “Geant4—a simulation toolkit,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 506, no. 3, pp. 250–303, Jul. 2003.

[2]        J. Baró, J. Sempau, J. M. Fernández-Varea, and F. Salvat, “PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter,” Nucl. Inst. Methods Phys. Res. B, vol. 100, no. 1, pp. 31–46, May 1995.

[3]        J. Cal-González et al., “Positron range estimations with PeneloPET,” Phys. Med. Biol., vol. 58, no. 15, pp. 5127–5152, 2013.

[4]        W. Lehnert, M.-C. Gregoire, A. Reilhac, and S. R. Meikle, “Analytical positron range modelling in heterogeneous media for PET Monte Carlo simulation,” Phys. Med. Biol., vol. 56, no. 11, p. 3313, May 2011.

[5]        D. Strul, G. Santin, D. Lazaro, V. Breton, and C. Morel, “GATE (geant4 application for tomographic emission): a PET/SPECT general-purpose simulation platform,” Nucl. Phys. B - Proc. Suppl., vol. 125, pp. 75–79, Sep. 2003.

[6]        C. Champion and C. Le Loirec, “Positron follow-up in liquid water: II. Spatial and energetic study for the most important radioisotopes used in PET,” Phys. Med. Biol., vol. 52, no. 22, pp. 6605–6625, Nov. 2007.



},

keywords = {},

pubstate = {published},

tppubtype = {award}

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Introduction
Geant4[1] is a Monte Carlo toolkit that provides a flexible platform to design radiation transport simulations. This flexibility requires a high level of complexity when writing new user-codes. New users must undergo extensive training to begin writing useful simulations. The aim of this study was to develop a modular radiation simulation software package called MaRSS based on Geant4 user-code to serve as both an educational tool and as a simulation tool for medical radiation detector simulations.

Materials & Methods
MaRSS builds on Geant4 using Penelope electromagnetic physics models and cross-sections[2]. To give the users possibility to change simulation parameters without changing the source code, MaRSS is equipped with a set of messenger classes which are intercom modules provided by Geant4 to configure applications and provide user interactivity with the code. These messenger classes add additional user commands that can be used to add or remove volumes from the simulation geometry and associated sensitive detectors. The sensitive detectors are objects that Geant4 uses to save simulation results. A number of default volumes and detectors are included in MaRSS that can be used for the design of scintillating fiber-based radiation detectors. MaRSS adds to the existing Geant4 sensitive detector code by creating a new base class called RunSD that is inherited by all sensitive detector objects. RunSD is implemented such that the code needed to initialize a sensitive detector is included in two classes, normally, this code is spread out in several classes. A similar approach is taken with geometrical volumes. To validate MaRSS, range in water of positrons emitted from four radioisotopes commonly used for positron emission tomography was calculated and compared with published work: Fluorine-18 (18F), Carbon-11 (11C), Oxygen-15 (15O) and Gallium-68 (68Ga). A sphere with a radius of 1 m was filed with water. For each radioisotope, 100 million decay events were simulated originating at the center of the simulated water sphere. The resulting positrons were allowed to annihilate. Two energy cuts were simulated, 1 keV and 0.1 keV. Two histograms were created, one using the annihilation locations and another with the energy of the emitted positrons. Results were compared with published positron range values calculated with a PENELOPE[2]-based Monte Carlo software called PeneloPET [3], an analytical expression to estimate the range of positrons described by Cal-Gonzales et.al.[3], a simulation by Lehnert et. al. [4] written using GATE[5] and a separate Monte Carlo software written by Champion and Le Loirec[6] that directly simulates the formation of positronium in water to calculate positron range.

Results
Figure 1 shows the mean calculated positron ranges and calculated positron emission energies compared to literature values. Simulated positron energy means were within 1.8% of literature values. Simulated ranges were within 2% of GATE simulation[4].

References
[1] S. Agostinelli et al., “Geant4—a simulation toolkit,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 506, no. 3, pp. 250–303, Jul. 2003.
[2] J. Baró, J. Sempau, J. M. Fernández-Varea, and F. Salvat, “PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter,” Nucl. Inst. Methods Phys. Res. B, vol. 100, no. 1, pp. 31–46, May 1995.
[3] J. Cal-González et al., “Positron range estimations with PeneloPET,” Phys. Med. Biol., vol. 58, no. 15, pp. 5127–5152, 2013.
[4] W. Lehnert, M.-C. Gregoire, A. Reilhac, and S. R. Meikle, “Analytical positron range modelling in heterogeneous media for PET Monte Carlo simulation,” Phys. Med. Biol., vol. 56, no. 11, p. 3313, May 2011.
[5] D. Strul, G. Santin, D. Lazaro, V. Breton, and C. Morel, “GATE (geant4 application for tomographic emission): a PET/SPECT general-purpose simulation platform,” Nucl. Phys. B - Proc. Suppl., vol. 125, pp. 75–79, Sep. 2003.
[6] C. Champion and C. Le Loirec, “Positron follow-up in liquid water: II. Spatial and energetic study for the most important radioisotopes used in PET,” Phys. Med. Biol., vol. 52, no. 22, pp. 6605–6625, Nov. 2007.

Daoud, Youstina; Carroll, Liam; Enger, Shirin A.

A Radiation detector simulation toolkit for calculating the Arterial Input Function during Dynamic Positron Emission Tomography Inproceedings

In: International Conference on Monte Carlo Techniques for Medical Applications, 2022.

Abstract | BibTeX

@inproceedings{nokey,

title = {A Radiation detector simulation toolkit for calculating the Arterial Input Function during Dynamic Positron Emission Tomography},

author = {Youstina Daoud and Liam Carroll and Shirin A. Enger},

year = {2022},

date = {2022-04-10},

booktitle = {International Conference on Monte Carlo Techniques for Medical Applications},

abstract = {"Introduction

Dynamic Positron Emission Tomography (dPET) is a functional imaging modality that provides an accurate assessment of patients’ physiological activities and response to treatments such as cancer, cardiac diseases and Alzheimer’s disease. It requires the measurement of the time-course activity concentration of the positron emitting PET radioisotopes in the patient’s arterial plasma, called the Arterial Input Function (AIF). The gold standard measurement of the AIF requires blood samples from the patient during the dPET. In our group, we are developing a non-invasive radiation detector that, placed on a patient’s wrist during the dPET scan, measures the number of positrons and photons escaping the radial artery and calculates the AIF. We have also developed a Modular Radiation Simulation Software for detector simulations called MaRSS that allows the user to run a Geant4-based Monte Carlo simulation, to calculate the AIF. Using the Monte Carlo method, MaRSS simulates a radioactive source decay in the radial artery and scores the amount of radiation escaping the radial artery and reaching the detector placed on the simulated patient’s wrist phantom. The wrist phantom is designed as a cylinder containing 2 holes that simulates the radial artery and vein. The shape and the depth of the radial artery vary between patients and proper knowledge of the distance between the radial artery and the skin, as well as its surface area, is important to accurately design the wrist phantom. Therefore, our aim was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, provide tools to measure the distance between the radial artery and the skin as well as the radial artery’s surface area and to create the necessary input file to MaRSS. The GUI provides MaRSS with a patient specific and more accurate wrist phantom, providing a patient-specific and more accurate calculation of the AIF without knowledge of C++ or Geant4.

Materials & Methods

The GUI elements were implemented using the multi-platform application and widget toolkit Qt 5 [1]. The C++ library, VTK 8.2.0 [2] was integrated in the GUI, which enables the user to import and manipulate the 2D ultrasound images. The toolkit comprises a measurement tool, a visualization window, a detector tab, a radiation source tab and MaRSS which is its simulation tool. To create an accurate wrist phantom, three 2D – cross secctional ultrasound scans of the patient’s wrist at 2 cm, 4 cm and 6 cm from the wrist crease and 1 longitudinal scan along the radial artery may be acquired and saved in DICOM format. In our case the BK3000 ultrasound system is used. These scans are imported into the GUI by selecting the folder that contains the images. Using the measurement functionalities shown in the top left corner of Figure 1, the surface of the radial artery is measured by drawing an ellipse on the artery’s boundary, then the toolkit measures the surface of the drawn ellipse and displays it in the Measurement window. The artery’s depth is also measured and displayed by drawing a straight line between the artery’s boundary and the skin. Using the left and right arrows, the user can navigate through the selected folder and measure the artery’s surface and depth on the other scans. The top right corner of the GUI shown in Figure 1, illustrates a Detector tab and a Source tab. The Detector tab allows the user to import a detector in STL format and place it on the ultrasound scan to simulate different setups of the detector, this functionality is still under development and

International Conference on Monte Carlo Techniques for Medical Applications, 2022

is optional. The Source tab allows the user to add the radioactive source used during the dPET by entering its mass number and its atomic number. After completing the 3 mandatory steps : import of the scan, measurement of different parameters extracted from the scan and choice of the radioactive source, the user can run the simulation by clicking on Run Simulation in the Simulation menu. The toolkit runs the MaRSS and creates the wrist phantom using the artery’s surface and depth measured by the user, then starts the decay of the chosen source placed randomly inside the artery.

Results

This toolkit allows the user to import 2D ultrasound scans and measure the radial artery’s surface and depth along the wrist, choose the radioactive source from the Source drop-down menu and specify the detector position. An input file to the MaRSS is thus created providing the required information to simulate the wrist phantom, the source and the detector’s position in MaRSS. The Run Simulation tab displays the output of the simulation in the GUI making it the only used tool for setting up the simulation and viewing the results.

Discussion & Conclusions

This toolkit enables the user to run a Geant4 Monte Carlo based simulation for detector development applications in 3 easy steps, not requiring any programming knowledge.

References

[1] Blanchette J, Summerfield M. C++ GUI programming with Qt 4: Prentice Hall Professional; 2006.

[2] Schroeder WJ, Avila LS, Hoffman W. Visualizing with VTK: a tutorial. IEEE Computer graphics and applications. 2000;20(5):20-7.

Acknowledgements This research was undertaken,in part, thanks to funding from the Canada Research Chairs Program (grant # 252135) as well as CHRP (NSERC+CIHR grant 170620).

"},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

"Introduction
Dynamic Positron Emission Tomography (dPET) is a functional imaging modality that provides an accurate assessment of patients’ physiological activities and response to treatments such as cancer, cardiac diseases and Alzheimer’s disease. It requires the measurement of the time-course activity concentration of the positron emitting PET radioisotopes in the patient’s arterial plasma, called the Arterial Input Function (AIF). The gold standard measurement of the AIF requires blood samples from the patient during the dPET. In our group, we are developing a non-invasive radiation detector that, placed on a patient’s wrist during the dPET scan, measures the number of positrons and photons escaping the radial artery and calculates the AIF. We have also developed a Modular Radiation Simulation Software for detector simulations called MaRSS that allows the user to run a Geant4-based Monte Carlo simulation, to calculate the AIF. Using the Monte Carlo method, MaRSS simulates a radioactive source decay in the radial artery and scores the amount of radiation escaping the radial artery and reaching the detector placed on the simulated patient’s wrist phantom. The wrist phantom is designed as a cylinder containing 2 holes that simulates the radial artery and vein. The shape and the depth of the radial artery vary between patients and proper knowledge of the distance between the radial artery and the skin, as well as its surface area, is important to accurately design the wrist phantom. Therefore, our aim was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, provide tools to measure the distance between the radial artery and the skin as well as the radial artery’s surface area and to create the necessary input file to MaRSS. The GUI provides MaRSS with a patient specific and more accurate wrist phantom, providing a patient-specific and more accurate calculation of the AIF without knowledge of C++ or Geant4.
Materials & Methods
The GUI elements were implemented using the multi-platform application and widget toolkit Qt 5 [1]. The C++ library, VTK 8.2.0 [2] was integrated in the GUI, which enables the user to import and manipulate the 2D ultrasound images. The toolkit comprises a measurement tool, a visualization window, a detector tab, a radiation source tab and MaRSS which is its simulation tool. To create an accurate wrist phantom, three 2D – cross secctional ultrasound scans of the patient’s wrist at 2 cm, 4 cm and 6 cm from the wrist crease and 1 longitudinal scan along the radial artery may be acquired and saved in DICOM format. In our case the BK3000 ultrasound system is used. These scans are imported into the GUI by selecting the folder that contains the images. Using the measurement functionalities shown in the top left corner of Figure 1, the surface of the radial artery is measured by drawing an ellipse on the artery’s boundary, then the toolkit measures the surface of the drawn ellipse and displays it in the Measurement window. The artery’s depth is also measured and displayed by drawing a straight line between the artery’s boundary and the skin. Using the left and right arrows, the user can navigate through the selected folder and measure the artery’s surface and depth on the other scans. The top right corner of the GUI shown in Figure 1, illustrates a Detector tab and a Source tab. The Detector tab allows the user to import a detector in STL format and place it on the ultrasound scan to simulate different setups of the detector, this functionality is still under development and
International Conference on Monte Carlo Techniques for Medical Applications, 2022
is optional. The Source tab allows the user to add the radioactive source used during the dPET by entering its mass number and its atomic number. After completing the 3 mandatory steps : import of the scan, measurement of different parameters extracted from the scan and choice of the radioactive source, the user can run the simulation by clicking on Run Simulation in the Simulation menu. The toolkit runs the MaRSS and creates the wrist phantom using the artery’s surface and depth measured by the user, then starts the decay of the chosen source placed randomly inside the artery.
Results
This toolkit allows the user to import 2D ultrasound scans and measure the radial artery’s surface and depth along the wrist, choose the radioactive source from the Source drop-down menu and specify the detector position. An input file to the MaRSS is thus created providing the required information to simulate the wrist phantom, the source and the detector’s position in MaRSS. The Run Simulation tab displays the output of the simulation in the GUI making it the only used tool for setting up the simulation and viewing the results.
Discussion & Conclusions
This toolkit enables the user to run a Geant4 Monte Carlo based simulation for detector development applications in 3 easy steps, not requiring any programming knowledge.
References
[1] Blanchette J, Summerfield M. C++ GUI programming with Qt 4: Prentice Hall Professional; 2006.
[2] Schroeder WJ, Avila LS, Hoffman W. Visualizing with VTK: a tutorial. IEEE Computer graphics and applications. 2000;20(5):20-7.
Acknowledgements This research was undertaken,in part, thanks to funding from the Canada Research Chairs Program (grant # 252135) as well as CHRP (NSERC+CIHR grant 170620).
"

Martinez, Victor Daniel Diaz; Carroll, Liam; Enger, Shirin A.

Monte Carlo Simulation of the 224Ra Decay Chain and the Diffusion of 220Rn for Diffusing Alpha-Emitters Radiotherapy Inproceedings

In: MEDICAL PHYSICS, pp. E828–E828, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.

Mission

Members

Projects

Dosimetry Detectors

Jingyi Bian, Víctor Diaz

Imaging Detector

Liam Carroll

Detector Group Publications

2022

2021

2020

2019

2018