Structural Magnetic Resonance Image Analysis for Neuroscientists
Meritxell Bach Cuadra
Magnetic Resonance Imaging (MRI) is today a key component in diagnosis and therapy planning in many medical areas such as neurology, oncology, surgery, etc. Every day MRI scanners generate an impressively increasing amount of data. In this context, image analysis becomes central to process and quantify in a precise and reproducible manner all these images. In this course we will introduce basic imaging processing methods to quantitatively analyze structural MR imaging. Our goal is to provide the students with the basic knowledge and practical experience in MR image registration, segmentation and morphometry analysis.
- Provide the students/participants with solid basis on image analysis methods
- Provide overview of existing tools/software
- Provide the participants with ways of thinking/criticism to choose the best solution for a given problem
We encourage students that are curious to better understand the image processing methods behind widely used structural image analysis tools, like FSL, ITK, or SPM, to participate to this course.
- March 26
- April 2, 9 and 16
- May 7th
- always on Wednesdays, from 10h to 13h.
March 26 - Lecture 1:
• Presentation of the course
• Basics of rigid image volume registration
• Practical session (MITK)
April 2 - Lecture 2:
• Basics of non-rigid image volume registration
• Practical session (MITK and SPM)
April 9 - Lecture 3:
• Anatomical atlases and template construction
• Tissue segmentation of healthy brain (including presentation of some available softwares)
• Practical session (MATLAB)
April 16 - Lecture 4:
• Tissue segmentation of pathological brains
• Voxel-based morphometry
• Practical session
April 30 - Lecture 5:
• Journal Club
• Invited talk: Image classification - Diagnosis
May 7 - Lecture 6:
• Journal Club
• Invited talk: Diffusion Image Analysis
Based on article presentation & discussion. It is mandatory to attend all lessons.
Number of participants limited to 20. Participation to all lectures is mandatory.
Please register (course name as header; supervisor in copy) by writing a mail to email@example.com.