This course introduces the broad concepts of biomedical imaging across a range of modalities. It provides a foundation towards the understanding of how modern biomedical imaging technologies generate multi-dimensional data for analysis and diagnosis. Key technologies covered include: XRay, CT, MRI, FMRI, Ultrasound, light microscopy, and medical imaging processing. Application of the biomedical images used to interpret biological process and diagnostics disease will also be discussed in small groups. Hands-on practical laboratory visits to cutting edge advanced bioimaging systems will be available to reinforce the lecture material, and quantitative imaging processing in the context of basic research and clinical settings will be covered.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Evaluate the operation and function of different biomedical imaging instruments on molecules, cells and organs.
- Describe the principles of advanced biomedical imaging concepts and their application in health sciences.
- Analyse the limitation of each biomedical imaging modalities and also how they complement each other for molecular, cellular and organ-level systems.
- Apply advanced image processing to quantify biomedical images and critique the factors that contribute to analysis.
- Understand and analyse major biomedical imaging modalities used in health sciences and outline their therapeutic aims.
- Evaluate the research methods and outcomes from selected scientific publications related to the course curriculum.
Research-Led Teaching
This course introduces the broad concepts of biomedical imaging across a range of modalities. It provides a foundation towards the understanding of how modern biomedical imaging technologies generate multi-dimensional data for analysis and diagnosis. Key technologies covered include: XRay, CT, MRI, FMRI, Ultrasound, light microscopy, and medical imaging processing. Application of the biomedical images used to interpret biological process and diagnostics disease will also be discussed in small groups. Hands-on practical laboratory visits to cutting edge advanced bioimaging systems will be available to reinforce the lecture material, and quantitative imaging processing in the context of basic research and clinical settings will be covered.
Recommended Resources
- Samei, E., and Pfeiffer, D.E. (2020). Clinical Imaging Physics: Current and Emerging Practice, 1 edn (Newark: John Wiley & Sons, Incorporated).
- Introduction to biomedical imaging, Webb, Andrew (Andrew G.) Institute of Electrical and Electronics Engineers. Imaging systems in medicine
Staff Feedback
Students will be given feedback in the following forms in this course:- Written comments
- Verbal comments
- Feedback to the whole class, to groups, to individuals, focus groups
Student Feedback
ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.Class Schedule
| Week/Session | Summary of Activities | Assessment |
|---|---|---|
| 1 | Pre-Tutorial/Pre-Computer Class ActivitiesWatch: Imaging through tissue I : High Energy Lecture 1: Grey Pixels - "X-Ray, Computer Tomography (CT)" Lecture 2: Quantum Pixel - "Positron Emission Tomography (PET)" Listen: 5 mins podcastRead: Chapter 4, 13 of Guillermo Avendaño Cervantes Technical Fundamentals of Radiology and CTAttempt online: 5 Multiple Choice QuestionsRun: Online Applet (CT, X RAY, PET), Advanced CT Simulator Tutorial activities (In Person)MEDN3820 Course Overview- " Dissecting the Anatomy of Signals behind Pixels" Tutorial 1 activities: (fill-in-blanks) on X-Ray, CT, PET Concepts using Glossary of Terms for Lect 1,2 Computer Class (Live Online)Online Live: Computer Class 1 (X-RAY Image Processing). |
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| 2 | Pre-Tutorial/Pre-Computer Class ActivitiesWatch: Imaging through tissue II : MagnetsLecture 3: Magnetic Pixel - "Magnetic resonance imaging (MRI) "Lecture 4: Seeing Blood Flow in magnetic pixel - "functional Magnetic resonance imaging (fMRI)"Listen: 5 mins podcastRead: Physics of MRI: A primerAttempt online: 5 Multiple Choice QuestionsRun: Online Applet (MRI-Proton), fMRI Simulator Tutorial activities (In Person)Tutorial 2 activities:Class discussion on Past Year Exam Question on MRI Computer Class (Live Online)Computer Class 2 (MRI Image processing) |
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| 3 | Pre-Tutorial/Pre-Computer Class Activities Watch: Imaging through tissue III: Sound and Light Lecture 5: Sound pixel "Ultrasound"Lecture 6: Light pixel "Optical Coherent Tomography (Artery)"Listen: 5 mins podcastRead: Schmitt, J. M. (1999). Optical coherence tomography (OCT): a review. Attempt online: 5 Multiple Choice QuestionsRun: Online Applet (US and OCT), OCT Simulator Tutorial activities (In Person)Tutorial 3 activities: Form group to evaluate and propose analysis methods on given medical images. Computer Class (Live Online)Computer Class 3 (3D imaging with OCT) |
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| 4 | Pre-Tutorial/Pre-Computer Class ActivitiesWatch: Cellular Imaging with light and ElectronsLecture 7: Colouring pixels with light "Histopathology & Fluorescence"Lecture 8: Using electron-pixel to see proteins "Electron Microscopies"Listen: 5 mins podcastRead: S Lichtman, J., Conchello, JA. Fluorescence microscopy. Nat Methods 2, 910–919 (2005). S. Tcherner Elad, N. Ben-Asher and L. Engel "Cool and collected: Advances in sample preparation for cryo-electron microscopy" Current Opinion in Structural Biology 2025 Vol. 94 Pages 103132Attempt online: 5 Multiple Choice Questions Tutorial activities (In Person) Tutorial 4 activities: Teams will propose specific imaging methods to address a given biomedical research question. Computer Class (Live Online)Computer Class 4 (Optical Microscopy) |
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| 5 | Pre-Tutorial/Computer Class ActivitiesWatch: Machine Learning in Medical Imaging Lecture 9: Processing Pixels for measurements "Fundamentals of signal processing" Lecture 10: Teaching Machine to see pixels "Machine Learning in medical imaging"Listen: 5 mins podcastRead: Varoquaux, G., Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. npj Digit. Med. 5, 48 (2022) Attempt online: 5 Multiple Choice QuestionsRun: Applet on Digital Tissue Slide Scanner Simulator Tutorial activities (In Person)Tutorial 5 activities:Teams to answer short topical question to use Glossary of Terms for Lect 7,8,9,10 Computer Class (Live Online) Computer Class 5 (Machine Learning segmentation) |
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| 6 | Pre-Tutorial/Pre-Computer Class ActivitiesWatch: Imaging Data Science Lecture 11: Converting Pixels to health scores "Compression, archiving, retrieval, and communication" Lecture 12: Large Pixels for Large Data Analysis "Big Data analysis in healthcare" Listen: 5 mins podcastRead: M. D. Mamlouk, P. C. Chang and R. R. Saket ,Contextual Radiology Reporting: A New Approach to Neuroradiology Structured Templates American Journal of Neuroradiology 2018, Attempt online: 5 Multiple Choice QuestionsRun: Online Applet (DICOM Data Explorer), DICOM De-identification Portal Tutorial activities (In Person):Teams will discuss and evaluate different analysis methods to extract health scores from the provided case study. Computer Class (Live Online)Revisit of the concepts of imaging processing protocols from Computer Class 1-5. |
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| 7 | Pre-Tutorial/Pre-Computer Class ActivitiesAttempt online: 20 Multiple Choice Questions (randomised)Read: J. Yee, A. Krishnaraj, S. F. Zaidi and C. Brewington Radiologists' Increasing Role in Population Health Management: AJR Expert Panel Narrative Review American Journal of Roentgenology 2022 Vol. 218Uchida, S. (2013), Image processing and recognition for biological images. Develop. Growth Differ., 55: 523-549. J, Porembka, R. K. Lee, L. B. Spalluto, Tutorial activities (In Person):Tutorial 6 activities: Teams will discuss and evaluate different analysis methods to extract health scores from the provided case study. Computer Class (Live Online)Computer Class 6 (3D Image Puzzle Instruction) |
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| 8 | Pre-Tutorial/Pre-Computer Class ActivitiesRead: Past Exam Solution sets Tutorial activities (Live Online) - to be scheduled with students Solving Full sets of Examination Question |
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| 9 | Guest Lecture: Radiology and Medical Entrepreneurship | nil |
| 10 | Pre-Tutorial/Pre-Computer Class ActivitiesPre-Reading Material: Summary notes on e.g. “Compare and contrast the fundamental principles and applications of brightfield and fluorescence microscopy. Discuss the advantages and disadvantages of each approach in different biological imaging scenarios. “Describe a typical microscope, referencing key components such as the excitation light, dichroic mirror, objective lens, emission filters, and detectors. How does this design framework enable the capture of specific molecular events? “ “Analyze the importance of refractive index in high resolution fluorescence imaging of biological subjects (cells, organelles etc).” Lab activities (In Person) In-class activities: Guided Laboratory Instructional Tour (1 hr) |
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| 11 | Read: Past Exam Solution sets Tutorial activities (Live Online) - to be scheduled with students Solving Full sets of Examination Question |
Due Online Quiz based on Lab Tour (Assignment) |
| 12 | Suggested Online Live: Consultation Period - to be scheduled with students | Due Individual Assignment
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Assessment Summary
| Assessment task | Value | Learning Outcomes |
|---|---|---|
| Tutorial (10%) | 10 % | 1,2,3,4,5 |
| Computer Lab (10%) | 10 % | 2,4,5 |
| Individual Presentation and Individual Report (25%) | 25 % | 1,2,3,6 |
| Lab Visit Quiz (15%); | 15 % | 1,3,5 |
| Written Exam (40%) | 40 % | 1,2,3,5,6 |
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
Policies
ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:- Student Assessment (Coursework) Policy and Procedure
- Special Assessment Consideration Policy and General Information
- Student Surveys and Evaluations
- Deferred Examinations
- Student Complaint Resolution Policy and Procedure
Assessment Requirements
The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.Moderation of Assessment
Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.Participation
The expected workload will consist of approximately 130 hours throughout the semester including:
- Face-to face component which may consist of 2 x 1 hour of lectures per week, 7 x 1 hour tutorial, 7x 1hr computer class and 1 x 1 hour Lab demonstration session, 3 x1 revisions lecture during throughout the semester .
- Approximately 88 hours of work/data analysis and self directed study which will include preparation for lectures, presentations, group work reports and other assessment tasks.
Students are expected to actively participate and contribute towards discussions during lecture, tutorial, computer class and revisions.
Examination(s)
Written Examination in person
Assessment Task 1
Learning Outcomes: 1,2,3,4,5
Tutorial (10%)
Responses to Weekly session
Week conducted: Weeks 1-7
Value:10%
Due date: 7 days after the tutorial is posted.
Assessment to be returned: 5 days after tutorial submission ended.
Assessment
- Tutorial 1- Critique Answers based on Past Year Exam Question on High Energy Imaging.
- Tutorial 2- Past Year Exam Question on MRI, PET.
- Tutorial 3 - Write a short technical report analysing a case study using Ultrasound and Optical Coherence Tomography (OCT) imaging.
- Tutorial 4 - Practice Past Year Exam Question on Electron or Optical Microscopy
- Tutorial 5 -Discuss and outline imaging protocols that will aid the application of Machine Learning in Biomedical Images.
- Tutorial 6 - Outline protocols for designing a study where mobile imaging technologies are used to assess different Indigenous health outcomes.
Rubric
| KNOWLEDGE | UNDERSTANDING | INSIGHT | |||
|---|---|---|---|---|---|
Assessment Task 2
Learning Outcomes: 2,4,5
Computer Lab (10%)
Assessment (Image Processing and Analysis) Students will be given image analysis projects related to "de-noising", "image averaging", "image segmentation", "thresholding", "object counting" using the ImageJ (NIH) software suite. Student will be expected to provide their tutor either Written Reflection or Image processing tasks or Image Puzzle for each computer class.
- Demonstrate and replicate the desired steps to achieve digital image processing.
- Explain to your tutor the use of the digital processing steps in research or clinical setting.
- Discussing the pros and cons of the digital processing steps.
Week conducted: Weeks 1-7
Value: 10%
Due date: 7 days after the computer assessment is posted.
Assessment to be returned: 7 days after submission
Assessment
- Computer Class 1 - Write a reflection on how pixels in X-Ray Images are processed.
- Computer Class 2 - Practice Image Processing Task on Whole body Medical Images (MRI, PET)
- Computer Class 3 - Write a reflection on quantitative image analysis of Ultrasound Images.
- Computer Class 4 - Practice quantitative image analysis of Microscopy and Electron Microscopy Image
- Computer Class 5 - Write a reflection on the approaches in Machine Learning used in Biomedical Images.
- Computer Class 6 - Solving a 3D Image Puzzle
Rubric
| KNOWLEDGE | UNDERSTANDING | INSIGHTS | |||
|---|---|---|---|---|---|
Assessment Task 3
Learning Outcomes: 1,2,3,6
Individual Presentation and Individual Report (25%)
Individual Presentation and Individual Report (25%)
Individual Report 10% (3 pages)/Presentation-15% (10 mins recording)
Due date: Week 12
Assessment to be returned: 14 days after submission
Individual Project- Essay (3 pages)
- Individual Report: A structured imaging report analysing an image dataset from a simulated patient ID or biomedical dataset. The report focuses on "dissecting the anatomy of signals behind pixels"—translating technical observations.
Presentation Format:•10 minutes maximum.-Unlisted Video on Youtube (Link to be Include in assignment)
- Recorded Presentation: A recorded case study presentation on the image dataset. Students must present a structured breakdown of the imaging findings, justify their choice of modality, and explain how the underlying imaging signals confirm the simulated disease state.
Rubric
| KNOWLEDGE | UNDERSTANDING | INSIGHTS | |||
|---|---|---|---|---|---|
Assessment Task 4
Learning Outcomes: 1,3,5
Lab Visit Quiz (15%);
Lab Visit Quiz (15%);
Due date: Week 11
Assessment to be returned: 7 days after submission
There will be 10 individual questions.
Rubric
| KNOWLEDGE | UNDERSTANDING | ||||
|---|---|---|---|---|---|
Assessment Task 5
Learning Outcomes: 1,2,3,5,6
Written Exam (40%)
Written Exam (40%)
Written exam (3 hrs) in the university examination period.
Assessment will take the form of short answer written answers on how to practically apply the imaging technologies.
Example question:
QUESTION 3 Write at least 20 words for each of the following questions.
Use equations and diagrams where necessary (20 points):
(i) X-Rays are produced when high velocity electrons collide and “eject” from a metal target. This phenomenon of “braking radiation” relates to a sudden deceleration of electrons. Increased temperature was applied to the cathode along with decreased voltage in the X-ray tube. Describe the changes to the resulting energy of X-rays produced. (2 points)
(ii) X-Rays are attenuated by either absorption or scattering events in tissue. Angiography is a method using of a liquid dye (gadolinium) injected through a fine tip flexible needle (called a catheter). The dye fills the blood vessel which changes the attenuation factor and in turn alters the final X-ray image. Coronary angioplasty uses a special balloon and a metal mesh tube (stent) to open up a narrowed or blocked coronary artery.
a. Assume that the attenuation coefficient and thickness of the dyed blood vessel is µBlood, tBlood and soft tissue is µSoft, tSoft,.respectively. When the initial number of X-Rays projected on the body is Nin, provide an analytical expression of the remaining photons Nout at positions A, B, C. (Hint: final amount of photon received at the X-ray detector) (4 points)
b. Describe the main physiological reasons and working principle behind the differences in contrast i.e. appearance of the different grayscale levels in A, B, C in the X-ray image. (4 points)
(iii) Magnetic resonance imaging (MRI) and Positron emission tomography (PET) provides a volumetric reconstruction of a full body. a. Describe the differences between how the signals in an MRI (proton spin) and PET (Gamma radiation) are collected and subsequently converted into a 2D cross section for 2 major organs: muscle and brain. (4 points)
(iv)Functional magnetic resonance imaging (fMRI), which measures the oxygenation of red blood cells, is a complementary imaging technique that extract functional information in a living subject.
a. Describe the difference between signals acquired in fMRI and traditional MRI. (3 points)
b. A patient was particularly uncomfortable about the noise produced by the MRI machine and fidgeting nervously. Explain how this in turn, affected the signal to noise ratio in the fMRI reading. (3 points)
Rubric
| KNOWLEDGE | UNDERSTANDING | INSIGHT | |||
|---|---|---|---|---|---|
Academic Integrity
Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.Online Submission
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.Hardcopy Submission
For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.Late Submission
Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.
Referencing Requirements
Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.Returning Assignments
Assignments are returned on wattle to individual students.
Extensions and Penalties
Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.Resubmission of Assignments
There are no resubmission of Assignments
Privacy Notice
The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information. In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service — including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy. If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.Distribution of grades policy
Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes. Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.Support for students
The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Convener
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Research InterestsBiomedical Imaging, Imaging Science, Biological Physics |
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Dr Steve Lee
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Instructor
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Research Interests |
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Dr Steve Lee
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Tutor
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Dr Daniel Lim
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Tutor
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Research InterestsBiomedical Imaging, Imaging Science, Biological Physics |
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Jasper Li
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Demonstrator
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Research Interests |
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Jack Sarran
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