Computational Methods in Medical Physics

Code:

F4011

Acronym:

F4011

Level:

400

Keywords
Classification	Keyword
OFICIAL	Physics

Instance: 2024/2025 - 2S

Active?	Yes
Web Page:	https://moodle.up.pt/course/view.php?id=522
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	14	Study plan since academic year 2023/2024	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

• Understand the fundamentals of statistical analysis and  characterize a statistical sample.


• Learn how to apply simple Pythin algorithms for the resolution of statistical problems.


• Understand the binomial, Poisson, Gauss and t-Student distributions.


• Understand numerical approaches to problem solution and setting up basic algorithms for numerical modelling.


• Critically evaluate results.


• Understand the concept of random and pseudo-random number series.


• Be familiar with random number generator algorithms.


• Be familiar with  software for statistical and numerical analysis and modelling.


• Understand the Monte Carlo method and applications in radiation transport.


• Have the ability to set up a simulation Monte Carlo for radiation transport.


• Manage to evaluate the quality of a simulation from  data analysis and  check and adjust the geometrical, physical and statistical parameters.


• Manage to choose the most appropriate code for Monte Carlo simulation according to specific examples.


• 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.
• Analysing numerical data with a critical sense.
• Performing simple Monte Carlo simulations for the resolution of Medical Physics problems.
• 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, using Python. 

2) Statistics:

- Reviewing concepts: descriptive and inferencial statistics; population, sample; sampling. Modelling populations with probability distributions. Discrete and continuous distributions: Poisson, Binomial and Normal distributions. t-Student Distribution. Moments of distributions 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 in Python and applications for statistics problems.

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

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.

- Different codes and application topics.

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

- Inverse planning in radiotherapy.

5) Anthropomorphic 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
Alex F Bielajew; Fundamentals of Radiation Dosimetry and Radiological Physics, 2005
000072453. ISBN: 0-521-75033-4
Alireza Haghighat; Monte Carlo Methods for Particle Transport, CRC Press , 2014. ISBN: 9781466592537
Xie George Xu and Keith F. Eckermann; Handbook of Anatomical Models for Radiation Dosimetry, Taylor & Francis, 2009. ISBN: 978-1-4200-5980-9

Teaching methods and learning activities

Practical sessions supported by theoretical sessions about the course.

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

Medical Physics problems will be explored whenever possible.

Software

Jupyter Lab
pacote monte carlo
MCNPx
Anaconda
Python com matplotlib, ipython, numpy e scipy
PRIMO software
Image J

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	33,00
Teste	33,00
Trabalho prático ou de projeto	34,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de projeto	50,00
Estudo autónomo	56,00
Frequência das aulas	56,00
Total:	162,00

Eligibility for exams

- Minimum final grade 9.5

Calculation formula of final grade

Final Mark:

• Solution of a set of problems in statistics, computational methods and radiation transport algorithms, weekly delivered in a report format, using Jupyter Notebook documents, simple preliminary problems on probabilities. 
• Evaluation written test, at the middle of the semester
• Final project, with oral presentation and written report, in one of the subject taught during classes or other considered relevant for the course. 

CF = 0.34 (Final Work/Project) + 0.33 (2 mini-exams) + 0.33 (Continuous evaluation - includes handing weekly/biweekly problems)

Classification improvement

The students can only petition a second attempt on the mini.tests.