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Medical Image Processing for Diagnostic Applications (DEMO)

This is a demonstration of the course "Medical Image Processing for Diagnostic Applications". The whole course is accessible via Virtuelle Hochschule Bayern (vhb). If you are interested in medical imaging and want to learn more about its diagnostic applications, go ahead and register there!

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Welcome to the Demo of Medical Image Processing for Diagnostic Applications!
In this course you will learn about the different modalities in medical image processing, learn the necessity of image preprocessing specific to the acquisition type, you will engage yourself in fundamentals and algorithmic details of the 3D-reconstruction, and get to know several options for image registration in all their mathematical beauty.
  1. Course Introduction
  2. Mathematical Tools
    1. Singular Value Decomposition
    2. Optional: Fourier Transform
  3. Preprocessing
    1. Undistortion
    2. Defect Pixel Interpolation
    3. MR Inhomogeneities
  4. Image Reconstruction
    1. Basics
    2. Optional: Projection Models
    3. Parallel Beam
    4. Fan Beam
    5. 3-D Reconstruction
    6. Modalities
    7. Iterative Reconstruction
  5. Rigid Registration
    1. Rigid Transformations
    2. ICP Algorithm
This course is organized in several modules/chapters which are parts of one of the five topics "Course Introduction", "Mathematical Tools", "Preprocessing", "Image Reconstruction", and "Rigid Registration". The first two teach you basic requirements for the big three latter chapters.
The content for the next topic is unlocked as soon as you have completed the former contents. This is evaluated by exercise tasks which you have to solve before you can proceed. Since this is an online course and some information could get lost in transfer via the internet, we highly recommend to take notes of your exercise solutions in order to be able to put them in again, in case it happens that your new content is not unlocked.

There is a lot of material and we present overall 71 units plus some extra content, each of which has its own unit page in one of the course modules where you find:
  • a short introduction,
  • a link to the lecture video which is hosted on Videoportal der FAU,
  • the accompanying course slides,
  • mandatory exercises.
There are two types of exercises, theory and programming exercises. Theory questions are asked directly at the end of each unit. Programming exercises can be found at the end of some modules. You need to have installed Git and Java on your computer. Please select the versions that are suitable for your operating system. If you use Windows we recommend to install TortoiseGit as well. This is a graphical user interface which might make things easier (installation hint: install it before installing Git). Further details can be found in the description of the first programming exercise.

Each new topic is unlocked completely, i.e. you will eventually get access to several new modules at once. It is basically your choice in which order you want to work through them, but as the unit numbers indicate there is a meaningful order which is why we recommend sticking to it.

In order to finish this course you have to pass a written exam which requires your physical attendance. The date and place will be announced in the Announcement blog. In case you are not living in Erlangen, you should plan a one-day trip to the FAU. Other than that the course is completely self-contained and all the learning material is available online.

If you have trouble understanding some content after watching the video and consulting the slides, or if you get stuck in one of the exercises, we provide a forum for all kinds of questions. Please be aware, that this method of communication is asymmetrical, so please give us some time to answer and plan ahead when starting this course.

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Course Material

Course Introduction

Learning Module ILIAS
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Copyright notice: The course contents are copyrighted (c) 2016-2017 by Prof. Andreas Maier, Friedrich-Alexander University of Erlangen-Nuremberg, Germany. Use without prior written permission of the authors is not permitted!
Acknowledgement: The creation of this online course was funded by Virtuelle Hochschule Bayern (vhb) and is part of its course portfolio. It was mainly developed by Prof. Andreas Maier and Frank Schebesch. Ashwini Jadhav supported as a student assistant. Scientific contributions are acknowledged in the respective units. Further, we cited sources for external image material and provide a URL or DOI wherever possible.