Monte carlo 1

Part I: Simulation and Monte Carlo

• This presentation covers simulation and Monte Carlo methods, focusing on general principles and applications.
• The presenter is Samira Abbaspour, PhD, from the Department of Medical Physics, Tehran University of Medical Sciences.

Outline

• Simulation: Basic and Principles: Introduces fundamental concepts of simulation.
• Monte Carlo Simulation: Details the technique of Monte Carlo simulation.
• Application of MC Simulation in Medical Physics: Discusses real-world uses in the field.
• Monte Carlo Codes Used in Medical Physics: Presents common software employed.
• Development of Anthropomorphic Phantoms: Explains the creation of realistic body models for simulation.
• Future Applications of Monte Carlo: Highlights potential future advancements.

Basics

• System: The physical process being studied.
• Model: A mathematical representation of the system.
  • Models are essential tools in science, engineering, and business.
  • Models are abstractions of reality and have inherent limitations.
• Simulation: Using a computer to mimic the system's behavior.
• Monte Carlo Simulation: A simulation that utilizes pseudo-random numbers.

Way to Study Systems

• Experiment actual system: Conducting direct observations.
• Experiment model of system: Testing a model designed to represent the system.
• Physical Model: A representation of the system's physical characteristics.
• Mathematical Model: Describes the system via numerical relationships.
• Analytical Model: Leads to exact results (preferred if possible).
• Simulation Model: Yields approximate results and is useful where analytical methods are impractical or impossible.

Simulation vs. Analytical

• Analytical models provide exact results, whereas simulation models yield approximations.
• Simulation is employed when an analytical or numerical approach is impossible or impractical.
• Analytical models are generally quicker to construct, but often lack the level of detail of a simulation.
• Simulation models are adaptable to differing detail levels but tend to take longer to construct and run compared to analytical approaches.

Advantages of Simulation

• The only viable option for complex systems; analytical methods frequently proving infeasible in these situations.
• The process of designing a simulation enhances the understanding of the real system.
• It facilitates sensitivity analysis and optimization procedures on actual systems without the need for real-world manipulation or direct experimentation.
• Offers greater control over experimental conditions than real-world experimentation.
• Possibility of time compression/expansion for evaluation over various time scales.

Disadvantages of Simulation

• Expensive and time-consuming to create, especially for highly detailed simulations.
• Easy to misuse simulations by exceeding credibility limits, particularly when software is unfamiliar or used inappropriately.
• Impressive visualizations might lead users to ascribe unwarranted confidence to resultant outputs.

Classification of Simulation Models

• Static vs. dynamic: Static simulations work with unchanging systems, while dynamic ones deal with time-evolving scenarios.
• Deterministic vs. stochastic: Deterministic simulations have no randomness, in contrast to stochastic simulations utilizing random numbers (e.g., Monte Carlo).
• Continuous vs. discrete: Continuous simulations employ continuous variables and equations, in contrast to discrete simulations focusing on events occurring at specific points in time.

Verification and Validation

• Both are crucial for accurate implementation
• Verification: Ensuring software accurately implements the specified model
• Validation: Examining whether the simulation model accurately reflects reality.

Parallel and Distributed Simulation

• Simulations involving large numbers of events might require extensive processing times.
• Parallel and distributed computation is helpful in these cases, subdividing tasks across connected processors for quicker results.
• Parallel computation often allows for significantly faster execution over sequential approaches.

Monte Carlo

• A computational method pioneered by Stan Ulam and John von Neumann.
• Historically used extensively in post-WWII developments tied to thermonuclear weapons.
• A fundamental tool in computing, and has widespread application.

Monte Carlo Method

• Relies on statistical probabilities and random numbers.
• Employs repetitive iterations using computers.
• Used to solve problems involving multiple interactions of objects or entities.
• Applications in entertainment, science, engineering, and other fields.

Components of a Monte Carlo Algorithm

• Probability density functions (PDFs)
• Random number generators
• Sampling rules
• Scoring/tallying methods for desired quantities.
• Error estimations (e.g., variance)
• Variance reduction techniques
• Parallelization and vectorization methods

Monte Carlo vs. Analytical Methods

• Useful for complex models where analytical methods are impractical.
• Demonstrates the correlation between problem complexity and simulation time.

MC Applications in Medical Physics

• Radiation protection
• Diagnostic radiology
• Radiation therapy
• Nuclear Medicine
• Other relevant fields

MC Applications in Radiotherapy

• Linac simulation
• Gamma Knife simulation
• Brachytherapy simulation
• Proton computed tomography simulation
• Neutron capture therapy simulation
• MC study of scattered radiation in therapy
• MC treatment planning
• Dosimetry (Gamma, electron, neutron)
• MC simulation of x-ray spectra in Linacs
• Multi Leaf collimator simulation
• IMRT and IGRT simulation
• Portal imaging simulation
• Linac design optimization
• Charge particles therapy

MC Applications in Dosimetry

• Electron dose calculation
• Gamma and X-ray dose calculations
• Neutron dose calculations
• Radiosurgery dose calculations
• Dose estimations in X-ray imaging
• Performance predictions of dosimeters
• Brachytherapy dosimetry
• Dosimeter design

MC Applications in Radiology

• X-ray tube simulation
• Grid performance evaluation
• Detector design
• Image quality evaluation
• Scattered radiation contribution analyses
• Dose calculations in radiology imaging
• X-ray tube and target/filter optimization
• Digital system evaluations
• Detection system optimization
• Detection Quantum Efficiency (DQE) calculations
• Optimal protocol determination

MC Applications in CT

• Performance assessment
• Design geometry optimisation
• Scatter characterization
• Scatter rejection strategies (e.g., septa design)
• BHE simulation and correction
• Dose calculation
• CT design
• Flat panel CT simulation
• Optimization of Flat panel CT design
• Calculation of SPR (Scatter Prediction Rate)
• X-ray Tube design optimisation
• Assessment of correction methods
• Target/filter optimisation
• Calculation of ideal Detector configuration
• Raw data generation for reconstruction algorithm testing

MC Applications in NM/PET

• Gamma camera simulation
• Detector modelling and optimization
• Scatter characterization
• Transmission scan modeling
• Scatter correction techniques
• Attenuation correction techniques
• Collimator design optimization
• Imaging system design
• Dosimetry
• Reconstruction algorithm testing
• Count rate simulation
• Electronic performance modelling
• Detector block design (in PET)
• Motion simulation

MC Simulation Features - PET

• Demonstrates characteristics of simulated images with and without detector blurring corrections.

MC Simulation Features - SPECT

• Spatial distribution and energy spectra from data generated in simulations, compared with measurements.

MC Simulation Features - SPECT

• Illustrates features of whole-body simulated SPECT images with varied activity distributions and simulated tumors.

MC in SPECT Collimator Design

• Provides a comparison between different collimator types (parallel vs. fan-beam).

MC in Image Reconstruction

• Demonstrates different image reconstruction methods.
• Analysis and comparisons between different image reconstruction algorithms are made in simulated data sets.

MC in Image Reconstruction - SPECT

• Shows the effect of scatter and attenuation modeling on SPECT image reconstruction quality.

MC Applications in Nuclear Medicine

• Modeling photon transport in SPECT.
• Energy resolution effect on scattered fraction.
• Quantitative In-111 SPECT evaluation.
• Feasibility assessment of 3D Monte Carlo reconstruction in SPECT.
• Validation of Indium-111 SPECT simulator.
• Fast modeling of collimator/detector response.
• Study of the point spread function (PSF) for 123I SPECT imaging.

MC Applications in PET & PET/CT

• Validate GATE Monte Carlo simulations of the noise equivalent counts, image quality, and other related measurements for the GE Discovery LS PET scanner.
• Validate Monte Carlo simulations for the Philips Allegro/GEMINI PET-systems using software.
• Performance assessment of three-photon PET imaging.
• Monte Carlo simulation and scatter correction.
• Image quality assessment for LaBr3-based whole-body 3D PET scanners.

GP Monte Carlo Codes in Medical Physics

• Discusses several simulation codes (MCNP, EGS4, GEANT, PENELOPE).
• Details the functionality and language options for each code.

Why MCNP4C?

• Presents the reasons behind the widespread popularity of the MCNP4C code in medical physics simulations.
• Highlights various features such as its accuracy, comprehensive geometry options, extensive testing, tallying, and variance reduction options.

Monte Carlo Codes in X-ray Imaging

• Discusses specific MC codes designed primarily for x-ray imaging.
• Gives a detailed description of each code, including language used and general description.

Monte Carlo Codes in NM Imaging

• Presents a list of MC codes commonly employed in nuclear medicine imaging scenarios.
• Explains their general functionality and the types of simulations performed (e.g., SPECT and PET).

Development of Anthropomorphic Phantoms

• Outlines the conceptual design considerations for physical and computer-based models (phantoms) for use in Monte Carlo studies that closely mimic anatomical structures for detailed physical interaction/ absorption simulations.
• Explains the creation of various stylized mathematical phantoms and tomographic voxel-based models.

Anthropomorphic Phantoms

• Shows examples of different shapes-based, and voxel-based anthropomorphic phantoms.

A Wide Panorama of Phantoms

• Categorizes various types of phantoms commonly used in medical physics, distinguishing mathematical, voxel and physical phantoms.

Mathematical Phantoms

• Discusses spherical cow analogy, which helps to highlight importance using simplified models to approximate complex systems quickly.
• Categorization of different classes of mathematical representations for organs in the human body.

Anthropomorphic Phantoms

• Discusses anthropomorphic phantom advancements from simplified spherical representations in 1950s for improved models.
• Focuses on common efforts and main classes of phantoms (analytical/mathematical and digital/voxel).

Analytical Model

• Discusses the initial history of attempting to create realistic anatomical models of the human body.
• Discusses the development of commonly used analytical models based on the MIRD phantom and the ORNL phantom series.

Modelling with Geometries

• Provides a more visual method for modeling complex geometries within models (e.g., brain).

"Final" Result

• Presents characteristics (e.g., sizes) of commonly used phantoms in the ORNL Mathematical Phantom Series.

Female ORNL Anthropomorphic Phantom

• Presents a visual representation, including specific organ placement details.

Female ORNL Anthropomorphic Phantom

• Shows additional anatomical features, aiding understanding of spatial arrangement and specificity of phantom model.

Female ORNL Anthropomorphic Phantom

• Presents further outputs (e.g., data points) generated from calculations using the simulated phantom.

Organ Specifications

• Presents details on tissue compositions/ densities for different tissues.

Mathematical Phantoms

• Discusses the different classes of mathematical phantoms; in particular comparing and describing differences between the MIRD5, ORNL, and new MIRD head and brain models.

Mathematical Phantoms

• Outlines the various MIRD5 phantoms commonly used, emphasizing variations in height and weight as well as organ masses, aligning with the reference man standards (ICRP 1975).
• Highlights the ADAM and EVA phantom variants for anatomical variations (e.g., genders).

Mathematical Phantoms

• Provides a visual depiction of human organ locations in a diagrammatic illustration
• Describes different representations of the human body in various states.

Mathematical Phantoms

• Outlines dynamic non-uniform rational B-spline phantoms for torso modeling.

Complex Shape Based Phantoms

• Displays examples of imaging data from distinct body parts (e.g., abdomen, head, jaw, etc.).

A Wide Panorama of Phantoms

• Summarizes different phantom types highlighting mathematical (e.g. MIRD), voxel-based and physical (e.g. human-like shaped objects) models of the human body and its parts which may be used in Monte Carlo calculations.

Interest on Anthropomorphic Phantoms

• Notes the focus on tomographic models during a specific conference, including discussions on various voxel and other models.

Image Coding

• Explains fundamentals of pixel-based image representation (e.g., the matrix, pixel concept, and data storage).

Matrix Size

• Emphasizes the importance of pixel size and matrix dimensions relative to the field-of-view and the desired resolution.
• Presents examples of common matrix dimensions and sizes.

Voxel

• Illustrates various voxel sizes to allow students to visualize the differences, allowing for recognition of relative importance in simulating the human body.

Anthropomorphic Phantoms

• Displays illustrations of voxel-based Zubal or additional phantoms, highlighting modifications or variations of the model, such as the adding of arms.

VIP-man phantoms

• A visual representation of a specific type of voxel-based human body model, with annotations for head, torso, and body segment anatomical locations.

GSF Phantom Series

• The slide showcases images of a male and young girl phantom model.

MEETMAN Voxel Phantom

• Presents an image of an anatomical model of the human body that can be utilized for calculations and simulations, including head, torso, and limbs.

FAX: a Female Adult Voxel Phantom

• Details data used in the creation of the FAX phantom (a voxel-based female anatomical model).

MAX: a Male Adult Voxel Phantom

• Illustrates a male anatomical model used in Monte Carlo calculations.

Scan2MCNP (CT Scan to MCNP Conversion)

• Shows screenshots of software used for converting CT scan data to a usable format for MCNP simulations.

4D NCAT and XCAT Phantom

• Visualizes the concepts of 4D (four-dimensional) anatomical representations useful for incorporating time-dependent (dynamic) aspects that may exist or be of interest, in a variety of medical applications, such as cardiac.

Related Community

• Provides information about the Consortium of Computational Human Phantoms (CCHP).
• Presents the initiative goals, history, and the role of human phantoms in medical applications.

Analytical Versus Voxelized Phantom

• Compares computational time and memory requirements for analytically and 3D voxelized representation in simulations.

Voxelized vs. Mathematical Phantoms

• Compares the qualities of mathematical and voxel-based representations of the human body.

Physical Phantoms

• Lists several types of physical phantoms (e.g., elliptical ECT, 3D head, Striatal, Heart/Thorax)
• Highlights geometrical and brain phantoms and heart/thorax phantoms as example types useful in Monte Carlo calculations.

Future Applications of Monte Carlo

• Outlines the future of Monte Carlo calculations, highlighting improvement potential in image quality, temporal resolution and patient throughput.
• Notes expected use cases in clinical and scientific areas.

Recommended Readings

• Includes suggestions for key publications in the field of Monte Carlo methods for medical radiation physics and related research.

Test your knowledge on simulation models, energy deposition measurements, and key concepts in scientific modeling. This quiz explores Monte Carlo simulation, mathematical phantoms, and the establishment of the MIRD 5 phantom. Challenge yourself with questions that cover both theoretical and practical aspects of modeling in engineering.