Outline

Overview

This unit of study will provide a theoretical understanding of the science of computed tomography (CT). It will cover the basic physics of radiation attenuation, data acquisition, image reconstruction methods, and image display and recording. It will also include an overview of relevant post processing operations. Quality Assurance (QA) of CT scanners will be explored. Finally, CT radiation dose considerations, such as the measurement of Computed Tomography Dose Index (CTDI), the Dose Length Product (DLP), and the Effective Dose (ED), will be reviewed. The factors affecting dose will also be covered, along with approaches to reduce patient doses in the clinical setting.

Unit details and rules

Academic unit	Clinical Imaging
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Roger Fulton, roger.fulton@sydney.edu.au
Lecturer(s)	Roger Fulton, roger.fulton@sydney.edu.au
Tutor(s)	Steven Meikle, steven.meikle@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Small continuous assessment	Online quizzes Multiple choice questions done individually online. This is open book test.	30%	Multiple weeks	2.5 hours
Outcomes assessed:
Small continuous assessment	Tutorial discussions Moderated discussions of questions related to the material covered recently in lectures, initiated in weeks 4, 7, 10 and 13. A marking rubric will be provided.	40%	Multiple weeks	2.5 hours
Outcomes assessed:
Assignment	Laboratory report A practical exercise to increase understanding of fundamental concepts.	30%	Week 11 Due date: 18 May 2025 at 23:59 Closing date: 25 May 2025	Approximately 8 hours to complete task.
Outcomes assessed:
= AI allowed ?

Assessment summary

Online quizzes: Quizzes will be released at approximately 3-weekly intervals and will cover the content of recent lectures. A range of question types may be used, e.g. multiple choice, fill in the blank, matching text, true or false, etc.
Tutorial discussions. Students will participate in moderated discussions at approximately 3-weekly intervals related to material covered recently in lectures. A marking rubric will be provided.
Laboratory report: Students will perform image manipulation and analysis tasks using the ImageJ software package and submit a written report.
Detailed information for each assessment will be available via the Canvas site for the unit.

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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
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.

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

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	CT scanner instrumentation	Online class (2 hr)
Week 02	CT image reconstruction	Online class (2 hr)
Week 03	Digital imaging and image processing in CT	Online class (2 hr)
Week 04	CT image quality	Online class (2 hr)
Week 05	Quality control in CT	Online class (2 hr)
Week 06	Radiation dose in CT	Online class (2 hr)
Week 07	CT dose optimisation	Online class (2 hr)
Week 08	CT image artifacts	Online class (2 hr)
Week 09	Specialised CT Acquisition Modes	Online class (2 hr)
Week 10	3D display techniques in CT	Online class (2 hr)
Week 11	Dual energy CT	Online class (2 hr)
Week 12	Photon counting spectral CT	Online class (2 hr)
Week 13	Hybrid CT imaging: SPECT/CT and PET/CT	Online class (2 hr)

Attendance and class requirements

Required materials: desktop computer or laptop (not tablet), webcam and microphone (built-in or external), and connection to network with sufficient internet speed: at least 3 Mbps download speed and 3 Mbps upload.

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

Recommended texts:

Seeram, E. Computed Tomography: Physical principles, clinical applications and Quality control. Fourth edition (Elsevier, 2016).

Bushberg, J. T., et al. (2021). The Essential Physics of Medical Imaging. (4th ed) Wolters Kluwer. Available as an e-Book via the University Library at https://sydney.primo.exlibrisgroup.com/discovery/fulldisplay?docid=alma991032236266605106&context=U&vid=61USYD_INST:sydney〈=en

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.

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

LO1. demonstrate a thorough theoretical and practical understanding of CT instrumentation, and the fundamental concepts underpinning data acquisition, image reconstruction, image processing, and display
LO2. demonstrate a thorough theoretical and practical understanding of common CT image quality and dose metrics, and the ability to measure them
LO3. apply a thorough theoretical understanding of the effects of CT acquisition parameters on image quality and radiation dose
LO4. identify and differentiate common image artifacts in CT, their causes, and how they may be mitigated
LO5. critically evaluate the potential clinical impact of recent advances in CT technology
LO6. demonstrate a theoretical and practical understanding of CT QA and QC procedures

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

Alignment with Competency standards

Outcomes	Competency standards
	Professional capabilities for medical radiation practice - 1A.3.a. Operate CT systems safely and effectively. 1A.3.f. Process data image sets, including multi-planar reformats and volume imaging.
	Professional capabilities for medical radiation practice - 1A.2.e. Apply knowledge of imaging acquisition modes and radiation dose rates. 1A.2.f. Perform image post-processing techniques. (1A.2) 1A.3.a. Operate CT systems safely and effectively. 1A.3.b. Apply appropriate imaging parameters for the patient/client presentation. 1A.3.c. Adjust relative radiation dose levels based on the range of patient/client presentations. 1A.3.d. Collaborate in the design and evaluation of CT protocols.
	Professional capabilities for medical radiation practice - 1A.2.e. Apply knowledge of imaging acquisition modes and radiation dose rates. 1A.3.a. Operate CT systems safely and effectively. 1A.3.b. Apply appropriate imaging parameters for the patient/client presentation. 1A.3.c. Adjust relative radiation dose levels based on the range of patient/client presentations. 1A.3.d. Collaborate in the design and evaluation of CT protocols.
	Professional capabilities for medical radiation practice - 1.3.a. Understand the different imaging and treatment pathways in medical radiation practice. 1.7.b. Apply quality criteria to assure image quality, evaluate medical images and identify any urgent and/or unexpected findings. 1A.3.a. Operate CT systems safely and effectively. 1A.3.b. Apply appropriate imaging parameters for the patient/client presentation.
	Professional capabilities for medical radiation practice - 1A.2.e. Apply knowledge of imaging acquisition modes and radiation dose rates. 1A.3.a. Operate CT systems safely and effectively. 1A.3.b. Apply appropriate imaging parameters for the patient/client presentation. 1A.3.d. Collaborate in the design and evaluation of CT protocols.
	Professional capabilities for medical radiation practice - 1.7.b. Apply quality criteria to assure image quality, evaluate medical images and identify any urgent and/or unexpected findings. 1A.2.f. Perform image post-processing techniques. (1A.2) 1A.3.a. Operate CT systems safely and effectively. 1A.3.c. Adjust relative radiation dose levels based on the range of patient/client presentations. 1A.3.f. Process data image sets, including multi-planar reformats and volume imaging.

Responding to student feedback

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

In response to student feedback the sequence of lectures has been adjusted for 2025. To allow the unit to be more interactive the video presentation assessment has been dropped and replaced with four assessable tutorial discussions on selected topics from the lectures. The weekly quizzes have been reduced in number from 13 to 4, but they will still test knowledge of the material covered in weeks 1 to 12.

Additional information

One of the recommended textbooks for this unit (Seeram, E. Computed Tomography: Physical principles, clinical applications and Quality control. Fourth edition (Elsevier, 2016)) is unavailable for purchase by the University as an eBook for student use. It is recommended that students consider purchasing their own copy of this text, or the recently released 5th edition, as it will be helpful to them in the course.

Student Administration Manual on the Faculty of Health Sciences

Additional costs

There are no additional costs for this unit.

Site visit guidelines

There are no site visit guidelines for this unit.

Work, health and safety

There are no specific work, health and safety (WHS) requirements for this unit.

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

MRTY5134: Computed Tomography Theory

Semester 1, 2025 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

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