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Unit outline_

MRTY5133: Medical Image Optimisation

Semester 2, 2022 [Online] - Camperdown/Darlington, Sydney

This UoS will investigate issues pertaining to the optimisation of medical imaging, aiming to ensure that imaging is best suited to answer the diagnostic questions posed. It will include discussion of the choice of imaging modalities, 2D and 3D radiographic imaging systems, as well as optimisation of display processing technologies and of display systems. In addition, issues pertaining to the relationship between dose and image quality will also be discussed. The aim of this UoS is to provide students with a clear understanding of how optimisation can affect diagnostic outcomes.

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 Tonima Ali, tonima.ali@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
Short answer questions
40% Formal exam period 1 hour
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
In-semester test (Record+) Type B in-semester exam Online quiz
MCQ
40% Week 08
Due date: 19 Sep 2022 at 19:00
50 minutes
Outcomes assessed: LO1 LO2 LO3 LO6 LO8
Small continuous assessment Weekly online discussions
Discussion thread contributions
20% Weekly n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Type B final exam = Type B final exam ?
Type B in-semester exam = Type B in-semester exam ?

Assessment summary

  • Online quiz: The exam will be conducted online. Students are required to have a computer/laptop to undertake this quiz.
  • Weekly online discussions: This task requires students to read the weekly discussion threads, address the questions posed using evidence from the literature, and make commentaries on other students’ contributions.
  • Final exam: The exam will be conducted online. Students are required to have a computer/laptop to undertake this exam.

Detailed information for each assessment can be found on Canvas.

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.

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to evidence-based medical image optimisation Online class (3 hr) LO1 LO2
Week 02 Justification using evidence-based referral guidelines Online class (3 hr) LO1 LO2
Week 03 General principles of optimisation and image quality assessment Online class (3 hr) LO1 LO2
Week 04 Optimisation of dose and image quality in computed tomography Online class (3 hr) LO1 LO2 LO6
Week 05 Principles, applications and image quality in dual energy computed tomography Online class (3 hr) LO1 LO2 LO3
Week 06 Optimisation in digital mammography: 2D versus 3D digital breast tomosynthesis Online class (3 hr) LO1 LO2 LO8
Week 07 Breast density and choice of imaging Online class (3 hr) LO1 LO2 LO8
Week 08 Mid-semester exam Online class (2 hr) LO1 LO2 LO3 LO6 LO8
Week 09 Optimisation of MRI examinations and workflow Online class (3 hr) LO1 LO2 LO7
Week 10 Image post-processing as a tool for image optimisation Online class (3 hr) LO1 LO2 LO4
Week 11 Optimisation in nuclear imaging case study: SPECT evaluation of future remnant liver function Online class (3 hr) LO1 LO2 LO4
Week 12 Medical image display optimisation Online class (3 hr) LO1 LO2 LO5
Week 13 Course review Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8

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.

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. apply the principles of evidence-based practice to the justification and optimisation of imaging investigations
  • LO2. critically evaluate the relevant literature across a range of imaging modalities and techniques
  • LO3. make informed choices about the appropriate use of dual energy computed tomography (CT) versus standard CT
  • LO4. apply the principles of medical image post-processing to the optimisation of imaging procedures and outcomes
  • LO5. make informed choices about the optimisation of medical display technologies based on published studies and understanding of their importance in the imaging workflow
  • LO6. defend decisions about the tradeoff between dose and image quality in computed tomography
  • LO7. support, with appropriate evidence, decisions about magnetic resonance imaging protocols that optimise examinations and workflows
  • LO8. make informed choices based on considerations of breast density and the strengths and limitations of 2D and 3D breast imaging systems (digital mammography and digital breast tomosynthesis)

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Based on student feedback from 2019, we have replaced the Piazza discussion board with a Canvas discussion board which is much easier for students and teachers to navigate.

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.