Computational Methods in Medical Physics

Code:

F4011

Acronym:

F4011

Level:

400

Keywords
Classification	Keyword
OFICIAL	Physics

Instance: 2018/2019 - 2S

Active?	Yes
Web Page:	https://moodle.up.pt/course/view.php?id=1553
Responsible unit:	Department of Physics and Astronomy
Course/CS Responsible:	Master in Medical Physics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:FM	4	Study plan since 2013/2014	1	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

To understand fundamentals of statistic analysis and to be able to characterize a statistical sample. To understand the numerical approach to problem solution and to be able to set up a basic algorithm for numerical modelling. To be able to critically evaluate results. To know the concept of random and pseudo-random number series. To know the random number ganerator algorithms. To acquire familiarity with software for statistical and numerical analysis and modeling. To understand the Monte Carlo method and applications on transport of radiation; To have the ability to set up a simulation Monte Carlo for radiation transport. To be able to evaluate the quality of a simulation from the data analysis and to check and adjust the geometrical, physical and statistical parameters. To be able to choose the most appropriate code for Monte Carlo simulation according to specific examples. To know the numerical solution method of inverse problems and applications in medical physics. Finally, to be able to work in both hospital and research environment where numerical solution to complex problems are requested.

Learning outcomes and competences

The student will develop specific skills on

• Solving numerical problems
• Choosing adequate numerical tools
• Analyzing numerical data with a critical sense
• Comprehension of the application of the topic proposed into the Medical Physics field

Working method

Presencial

Program

1) Introduction:

- Numerical and statistical Methods in Medical Physics and illustrative examples.

 

2) Statistics:

- Reviewing concepts: population, sample; sampling. Modeling populations with probability distributions. Discrete and continuous distributions: Poisson, Binomial and Normal distributions. Moments of vdistributions and sample mean and variance. Central Limit Theorem. Sample Mean and its properties.

- Random numbers and pseudo-random algorithm generation.

 

3) Computational Methods

- Basics of programming C/C++

- Programming a simplified Monte Carlo description of transport by creating a simplified random number generator and a particle transport algorithm.

- Descrbe the inverse planning for Radioterapy used by the Treatment Planning Systems. Numerically solve an inverse problem and set up different level of confidence of the solution.

4) Monte Carlo

- Introduce the Monte Carlo method as an approach to solve numerical problems like complex integration

- Describe different codes commonly used in Medical Physics for particle transport.

- MCNP characteristics; ‘input’ files in MCNP; examples; output file analysis

- EGS description and examples

- GEANT4 characteristics and applications in Medical Physics. Build a GEANT4 based application.

Description of the PRIMO software as a tool for simulations in hospital environment.

 

5) Antropomorphic computational phantoms

- Describe different type of phantoms based on categories: Mathematical, Voxel-type and Boundary representation. Build an example of a patient specific phantom.

Mandatory literature

000076128. ISBN: 0-19-263269-8
Mould Richard F.; Introductory medical statistics. ISBN: 0-7503-0513-4
LearnPyhton.org ; http://www.learnpython.org/ (Interactive Pyhton Tutorial)
Seco João 340; Monte Carlo techniques in radiation therapy. ISBN: 9781466507920
Alex F Bielajew ; Fundamentals of Monte Carlo Transport for neutral and Charged particles, , University of Michigan, 1998-2001
LANL/MCNP team; MCNP User Guide

Complementary Bibliography

000041234
000010744. ISBN: 0-201-18399-4
000072453. ISBN: 0-521-75033-4
Alex F Bielajew; Fundamentals of Radiation Dosimetry and Radiological Physics, 2005

Teaching methods and learning activities

Mainly practical sessions supported by theoretical sessions on the subjects of the course.

The practice sessions will run on the computational lab, where a set of numerical software may be accessed. 

Software

MCNPx
pacote monte carlo
Gnuplot
compilador C - gcc
editor código - bluefish
Python com matplotlib, ipython, numpy e scipy
PRIMO software
GEANT4

keywords

Physical sciences > Physics > Applied physics > Medical physics
Physical sciences > Physics > Computational physics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Trabalho laboratorial	20,00
Teste	30,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de projeto	30,00
Estudo autónomo	90,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

- Minimum final grade 9.5

Calculation formula of final grade

Final Mark:

• Statistics - Solution of a simple preliminar problem on probabilities. Choose of a problem on statistic test, discussing the results at the exam.
• Computational methods - Development of a Project:

1. Invers Problem
2. Gamma Function
3. LINAC model using PRIMO
4. PDD e profiles using GEANT4
5. Phase Space construction using GEANT4

CF = 0,5 (Fina Work/Project) + 0,3 (Test) + 0,2 (Laboratory)

Classification improvement

Same evaluation assignments with new problems and a new simulation project.