Outline

Overview

In this unit, normally undertaken as part of the Masters of Medical Physics degree or the Graduate Diploma in Medical Physics, Monte Carlo modelling of radiation transport is covered, along with the theory of image formation, concepts of computing, numerical methods and image processing, including techniques such as enhancement, registration, fusion and 3D reconstruction, radiomics and an introduction to Machine Learning techniques.

Unit details and rules

Academic unit	Physics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Annette Haworth, annette.haworth@sydney.edu.au
Lecturer(s)	Annette Haworth, annette.haworth@sydney.edu.au
	Chun-Chien (Andy) Shieh, andy.shieh@sydney.edu.au
	John Kipritidis, john.kipritidis@sydney.edu.au
	Jonathan Sykes, jonathan.sykes@sydney.edu.au
	Peter Lazarakis, peter.lazarakis@sydney.edu.au
	Robert Finnegan, robert.finnegan@sydney.edu.au
	Yu Sun, yu.sun@sydney.edu.au
	Sirisha Tadimalla, sirisha.tadimalla@sydney.edu.au

Type	Description	Weight	Due	Length
Oral exam ?	Final Exam (Oral) Final Exam (Oral): Python and radiomics	30%	Formal exam period	15 minutes (oral)
Outcomes assessed:
Assignment	Assignment 1 Assignment 1: Monte Carlo	10%	Week 04 Due date: 24 Aug 2022 at 23:59	1-2 weeks
Outcomes assessed:
Assignment	Assignment 2 Assignment 2: Image processing	10%	Week 06 Due date: 07 Sep 2022 at 23:59	1-2 weeks
Outcomes assessed:
Assignment	Assignment 3 Assignment 3: Physics of MRI	15%	Week 09 Due date: 07 Oct 2022 at 23:59	2-3 weeks
Outcomes assessed:
Assignment	Assignment 4 Assignment 4: Image Registration and Image Segmentation	25%	Week 10 Due date: 12 Oct 2022 at 23:59	3-4 weeks
Outcomes assessed:
Presentation	In-class assessment (Oral) In-class assessment (Oral): e-health. Date/ time TBA	10%	Week 11 Due date: 17 Oct 2022 at 09:00	12-15 mins
Outcomes assessed:
= hurdle task ?

Assessment summary

Lab Quiz/Assignments: These will be based on material presented in practical/virtual labs drawing on material presented in lectures and will be assessed by the respective lab supervisors/lecturers.
In-semester (Oral test): will be based on lecture material, and readings and will be assessed by the lecturers.
Final Exam: will be based on lecture material, and readings and will be assessed by the lecturers.

Final exam: If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator

Detailed information for each assessment can be found on Canvas.

All assessments are compulsory.

Assessment tasks are intended to allow you to demonstrate what you have learned related to the goals of this unit. They also serve
to encourage you to work with the material but should not dominate your approach to learning. See them as another learning
activity, accompanying and complementing the lectures and labs.

In addition, students in physics must be able to express themselves accurately by clear, efficient use of the English language in
their written work. Spelling, grammar, punctuation and correct use of language will be taken into account when written reports and
examination work are assessed. Students should refer to the University’s WriteSite (http://writesite.elearn.usyd.edu.au/) if they are
looking for guidance on grammar and other aspects of academic and professional writing.

Assessment of this unit of study is based on achievement of specific learning objectives as demonstrated in a combination of
assignments, tests, examination and laboratory work. Satisfactory performance in assessments across all learning outcomes is necessary to ensure a pass in this unit. It is expected that a grade of at least 45% across all assessments and the final examination will be achieved
in order to demonstrate satisfactory performance. A combined score of at least 50% is necessary for a pass as indicated in the
section “Assessment Grading” below.

You are responsible for understanding the University policy regarding assessment and examination, which can be found in the
University Policy Register at http://sydney.edu.au/policies/

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Monte Carlo methods in medical physics part 1: Fundamentals	Lecture (3 hr)
Week 02	Monte Carlo methods in medical physics part 2: Simulating radiation and particle transport	Lecture (3 hr)
Week 03	Image processing using MATLAB part 1	Lecture (3 hr)
Week 04	Image processing using MATLAB part 2	Lecture (3 hr)
Week 05	Introduction to python	Lecture (3 hr)
Week 06	Introduction to ITK	Lecture (3 hr)
Week 07	Multimodality image registration	Lecture (3 hr)
Week 08	Image segmentation	Lecture (3 hr)
Week 09	e-Health, hospital data management	Lecture (3 hr)
Week 10	Research methods part 2	Lecture (3 hr)
Week 11	Radiomics part 1: Basic	Lecture (3 hr)
Week 12	Radiomics part 2: Advanced methods	Lecture (3 hr)
Week 13	Professional Development - Meet the Chiefs	Lecture (3 hr)

Attendance and class requirements

Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where online tutorials/workshops/virtual laboratories have been scheduled, students are expected to attend and participate at the scheduled time. Penalties will not be applied if technical issues, etc. prevent attendance at a specific online class. In that case, students should discuss the problem with the coordinator.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Reading List, available on Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. demonstrate an understanding of general properties of Monte Carlo methods and their use in medical physics
LO2. demonstrate an understanding of general algorithm for modeling particle transport & how this applies to photons/electrons
LO3. demonstrate an understanding of basic analysis and processing techniques for medical imaging in research and clinical environments
LO4. demonstrate an understanding of the theory, techniques and applications for image registration in the context of radiotherapy
LO5. demonstrate an understanding of the best practice recommendations regarding the validation and quality assurance of image registration for medical images
LO6. demonstrate an understanding and analysing of image registration software
LO7. demonstrate an understanding of the hierarchy of health information systems from National through state to Local Health District, Hospital and Departmental systems
LO8. demonstrate an understanding of basic appreciation for the importance of data security in healthcare
LO9. demonstrate an understanding of the basics of Python and use it for the development of a radiomics predictive model using both traditional feature-engineering methods as well as deep learning approaches
LO10. demonstrate an understanding of the different methods of medical image segmentation, uncertainties associated with image segmentation in radiotherapy, and practical experience performing image segmentation
LO11. Demonstrate an understanding of the physics of MRI

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Additional information

More information can be found on Canvas site for this unit.

Additional costs

There are no additional costs for this unit.

Site visit guidelines

There are no site visit guidelines for this unit. Please check the Canvas site for this unit.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

PHYS5020: Computation and Image Processing

Semester 2, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect