Projekt Flat-Panel CT Reconstruction
Time and place:
This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again. No registration is required to attend this course. All further information will be provided in the StudOn course (link below).
- Tue 10:00-12:00, Room 0.01-142 CIP
Fields of study
- WPF INF-MA from SEM 1
- WPF MT-MA from SEM 3
The aim of this master project is to build a state-of-the-art flat-panel CT reconstruction software. The project is designed in two parts: The first part is the Academic Laboratory (Hochschulpraktikum). These 5 ECTS can be earned by attending the course, finishing the exercises and giving a short presentation at the end of the semester. The second part is the 5 ECTS Research Laboratory (Forschungspraktikum), where after the semester the students can work on research topics related to the topics taught in the course.
In the Academic Laboratory, the basics of CT reconstruction will be developed in a group. All participants will create a basic CT reconstruction pipeline that is able to reconstruct flat-panel CT images.
The following topics will be taught and implemented in this course:
- Parallel-beam reconstruction
- Fan-beam reconstruction
- Cone-beam reconstruction
- Hardware-acceleration using the graphics card
In the Research Laboratory, the participants will be asked to adopt the designed pipeline individually to specific problems in CT reconstruction. These topics are always related to current research at the Pattern Recognition Lab, including for example:
- Limited field-of-view
- Limited acquisition angle
- Reconstruction with few projections
- Noise reduction
- Motion compensation
You will incorporate your work into a fully-fledged CT reconstruction and analysis tool that makes it easy to evaluate the reconstruction algorithms. At the end of the project, a trip to the Siemens Healthineers in Forchheim is planned in order to experiment with a real scanner.
Keywords: Master Project, Pattern Recognition, CT Reconstruction
Expected participants: 10