# COMPUTING FOR MEDICAL PHYSICS - 2025/6

Module code: PHYM065

## Module Overview

The module is aimed at giving students an understanding of the use of computers in the broader context of medical physics.
This will range from the use of Monte Carlo modelling using TOPAS for dosimetry and experimental design, to the use of Python to code simple problems related to image processing and data science.
In addition, they will learn the importance of data security and data governance with specific application to a clinical context.

### Module provider

Mathematics & Physics

PANI Silvia (Maths & Phys)

### Module cap (Maximum number of students): N/A

Independent Learning Hours: 98

Lecture Hours: 9

Laboratory Hours: 24

Guided Learning: 10

Captured Content: 9

Semester 2

None

## Module content

Indicative content includes:

Introduction to Python coding.

Image quality, contrast, resolution: Mathematical formulation of the imaging system; impulse response function, stationarity, line spread function, edge spread function, MTF. Usefulness of MTF, modulation input and output, test objects, measure of performance, cascade of MTFs. Perception of detail, visual acuity, resolution criteria. Existence of observer, decision criteria. Construction of the ROC curve and principle of ROC analysis.

Basics of signal processing and CT reconstructions: Fourier series and Fourier transform; Nyquist's theorem. Projections. Radon's theorem. Backprojection and filtered backprojection. Iterative reconstruction.

Image processing, image registration, elements of data science: Images in the Fourier domain. Object segmentation – thresholding, k-means and region growing, Filtering: Edge enhancement and smoothing filters. Edge detection, 2D morphological operators. Image registration: rigid and non-rigid techniques; affine and non-affine methods. Application examples in Multi-modality imaging. Data science applied to medical imaging.

Monte Carlo modelling in TOPAS: Monte Carlo simulation of radiation interactions in matter and an introduction to the use of TOPAS simulation software.

PACS, data governance, data security.

## Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Online quiz 40
Coursework Report 30
Coursework TOPAS modelling exercise 30

None

## Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their understanding of the principles and programming of TOPAS Radiation Monte Carlo code, of the theoretical foundations of image processing, image quality and CT reconstructions and of their understanding of data science and of coding in Python.

Thus, the summative assessment for this module consists of:
- A coursework assignment, in the form of an online quiz, on the theoretical elements of the module and on image analysis.
- A report on a coding exercise in the area of data science.
- A TOPAS Monte Carlo computing assignment.

Formative assessment and feedback
The formative assessment will include model coursework questions.
Continuous verbal feedback will be given during the computing classes in Monte Carlo, Python, image processing and data science.

## Module aims

• Through hands-on computing laboratories, sessions, students will learn the basic use and implementation of the TOPAS Radiation Physics Monte Carlo simulation software.
• Students will learn the foundations of coding in Python and apply them to problem-solving in different areas of data science and image analysis.
• Students will gain an understanding of the importance of data security and data governance in a clinical context.

## Learning outcomes

 Attributes Developed 001 Understand the basis of Monte Carlo simulation, and understand the key operations of the TOPAS simulation programme KCP 002 Understand the foundations of Python coding KCPT 003 Understand elements of signal processing and their application to CT reconstruction and image processing KC 004 Apply the computing skills gained to problem solving in the areas of data science, image analysis and image registration KPT 005 Understand the impact of contrast and spatial resolution on image quality KPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

## Methods of Teaching / Learning

Lectures, hands-on computing sessions.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Upon accessing the reading list, please search for the module using the module code: PHYM065

## Other information

Digital Capabilities - Throughout the module students will engage with large and complex datasets ("big data") and will develop their computational skills in analysing this data using Python and in formulating a correct physical scenario in TOPAS.

Employability - The module introduces students to the use of computers in a range of real-world contexts, including the use of data science which is now ubiquitous in medical and nuclear physics, coding and the understanding of the importance of data management and data security in a clinical context.

Resourcefulness and resilience - Students will enhance their problem-solving skills through the implementation of correct models in TOPAS and the development of Python code to solve data science exercises.

## Programmes this module appears in

Programme Semester Classification Qualifying conditions
Medical Physics MSc 2 Compulsory A weighted aggregate mark of 50% is required to pass the module
Nuclear Science and Radiation Protection MSc 2 Compulsory A weighted aggregate mark of 50% is required to pass the module
Physics MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.