Visualization in Medical Education

1. Visualization in Medical Education Mai H. El-Shehaly CS 6603: Reinventing CS education through the eTextbook Spring 2012

2. Why should we care ? • Computer-based education relies on CS research: • Image processing • Database systems • Massive Model Processing • Programming GPUs • 3D visualization • Experience from another discipline • Research in medical data visualization serves other massive models

3. Motivation • For Physicians, education is a lifelong process • Medical practice changes rapidly • Many specialties require recertification every few years

4. Goals • The primary purpose of medical education is to teach: • Problem solving (diagnosis) • Problem management (therapy) • The goal of visualization is to make learning more efficient and engaging

5. A bit of History • Bassett Collection of Stereoscopic Images of Human Anatomy (1948 – 1965) • More than 1,550 images of human dissections • Atlas of Human Anatomy, 1962 (24- volumes)

6. A bit of History • The “Visible Human Project”: (1989-1995) • A dataset of cross-sectional photographs of human body • Purpose: anatomy visualization • Male cadaver  1,871 slices ( 1mm - 65 GB) • Female cadaver  0.33 mm – 40 GB

7. Visible Human Project

8. What’s wrong with the Visible Human ? • Ethical concerns University of Vienna demanded that the images be withdrawn: medical profession should have no association with executions, and that the donor's informed consent could be scrutinised • Inaccuracies with the dataset • Freezing, formalin injection, and slicing damaged: brain, blood vessels, missing organs, etc

9. Problem • Healthcare students have difficulties: • Achieving conceptual understanding of 3D anatomy • Hard to address misconceptions about physiological phenomena Cadaver vs. live body

10. Are still images enough ? • Need to study tissue undergoing certain natural processes • Complex physiological processes • Concepts of functional anatomy are poorly conveyed via textbooks and figures

11. E-learning and education in radiology [2011] • Medline search (Medical literature analysis and retrieval system) for publications discussing e-learning in radiology • A review of 38 human studies • Different visualization and interaction techniques [1] Antonio Pinto, Luca Brunese, Fabio Pinto, CiroAcampora, Luigia Romano, E-learning and education in radiology, European Journal of Radiology, Volume 78, Issue 3, June 2011, Pages 368-371, ISSN 0720-048X,

12. E-Learning in Radiology • Impact of the Internet on education: • From personal collections to hospital libraries • Web sites that show series of radiographs or CT images with every anatomic or pathologic finding labeled are NOT of great educational value. • Web site design to emulate “hot seat” session • Wiki sites encourage collaboration

13. E-Learning in Radiology • Computer-based simulator: • Case-based education improves students problem solving ability • In an ideal simulator: • Functionality of a real picture archiving and communication system • Immediate and long-term feedback

14. E-Learning in Radiology • Software technologies to implement e-learning: • Educator-centric LMS acts as a superset for electronic material • Teleconferencing + image databases • Hypermedia documents

15. Small-group learning: PBL vs. CBL • PBL • Open Inquiry Approach • Focus on discovery by learner • Start with Clinical case • Minimal educator role • Stimulate post-session reading and exploration • CBL • Guided Inquiry Approach • Focus on problem solving • Start with Clinical case + advance preparation • Facilitator asks guiding questions • Minimal post-session work

16. Which is better ?

17. Which one do students favor ? Comparing problem-based learning with case-based learning: effects of a major curricular shift at two institutions. [2007]

18. Case Studies

19. Do Computers Teach Better? A Media comparison study for case-based teaching in radiology • 10 radiology cases + a voluntary weekly lecture • 225 students randomly assigned to 4 groups: • Group A:Computer-based cases + Interactive elements • Group B: Computer-based cases + no interaction • Group C: Paper-based cases + interactive elements • Group D:Control group  no cases

20. Do Computers Teach Better ?

21. Do Computers Teach Better ?

22. Do Computers Teach Better ?

23. Advanced 3-D Visualization in Student-centered Medical Education [2008] • A project at Linkoping university, Sweden • Aims: • Develop 3D visualizations & integrate them in different learning situations • Enhance our knowledge about educational value of 3D visualizations in education

24. Student – Centered Education • Based on: cognitive psychology, social constructivism • Students work with scenarios in small groups (6 – 9 students & a tutor) • Identify problems and learning objectives • Resource sessions, seminars, self-studies, practice in professional domain, etc..

25. Problem-Based Learning • Scenarios based on authentic situations serve as a meaningful context for learning • Learner’s processing of information: • Posing questions • Looking for answers • Analyzing and reflecting

26. Learning Situations in the medical program • In Fall 2005: • Students were introduced to images and films in an Internet-based scenario: • Rotating CT image of the heart • MRI movie of the pumping heart • Pedagogical aim: • To challenge the students’ interpretation of the visualizations • Trigger their formulation of learning needs

27. Learning Situations in the medical program • In Spring 2006: • A lecture given by a radiologist and physiologist, using an advanced rendering workstation to show planar and 3D images of the heart: • Clinically important anatomical relations • Variations of normal behavior • A demo in the VR theatre: 4D MRI images of pumping heart • Self-study of volume-rendering images stored in QTVR format

28. Learning Situations in the medical program • Aim of the lecture and demo was to present difficult phenomena using advanced technology not possible for students to handle alone • Interactive QTVR images of CT datasets were run as self-study on students’ PCs

29. Results

30. Results

31. Results

32. Challenges & Limitations

33. 1- Size Access Times Stored data Limited Bandwidth Stored data Displayed subset of size O(N < M) Output Sensitive Techniques Stored data Interactive rates 10-100 Hz Stored model of size O(M  ∞ )

34. 2- Rendering Techniques Rasterization: Ray Tracing: Acceleration Structures Problem: Both are brute-force methods with linear complexity

35. Solutions Memory Management Acceleration Structures View-dependent Rendering • Cache Coherence • Speculative Pre-fetching • Spatial Indexing • Multiresolution structures • Visibility Culling • LOD GPU Parallelism

36. Conclusion • E-learning will become an important source of education in medicine • Case-based learning is favored by students • Future medical eTextbooks should: • Emulate “hot seat” sessions • Play the role of a facilitator • Simulate clinical practice • Provide structured, goal-directed questions • Visualization of massive data requires GPU utilization

37. Research @ VT

38. Thank you