TU Wien:Visualisierung medizinischer Daten 1 VU (Raidou)/Exam 2023-01-22
Part 1: Theoretical Questions
These are some of the questions I remember. Each question was split into multiple smaller parts that awarded a small number of points:
- What are the goals of visualization? Six bullet points were given, and you had to fill them in.
- Difference between two smoothing filters (median & ???)
- What is the transfer function? What does it do?
- What are multi-dimensional transfer functions? When are they needed? Example?
- (Thoroughly) Explain the steps in ray casting (volume rendering pipeline)!
- Explain the difference between the terms post- and pre-classification. When to use which? Why do we need it?
- Question about HU of water, air, and bone. The formula was supplied, and you had to fill in the given values to calculate the attenuation coefficient for an HU of -400 and explain what type of tissue that could be.
- What happens in the compositing step?
- What are the rough steps of region growing (the simple algorithm)? What are the pros/cons? When would you use it?
I think those were all the questions. Overall, the questions were fair, and everything was mentioned on the slides or in the exam at least once.
Part 2: Practicals
2.1) Given three images (CT, MRI, contrast-enhanced), how would you develop a visualization that shows the kidneys, bladder, and large vessels? The context was kidney stone screening. The text also mentioned that the render doesn't have to include the spine/bones or any other soft tissues. You had to explain each step of the visualization pipeline and what methods you'd choose.
2.2) Given a table with intensity values & matching colors and a matrix with colored cells & four lines going through the matrix: Calculate the resulting intensity value using MIP and average compositing. No interpolation was required.
2.3) Five (?) different color maps were given, and you had to explain the pros/cons of each of them and also name an example of where they could be useful or how you would use each of them in a medical visualization.