MR Image Processing
Short Description: This course offers a comprehensive overview of MR image processing techniques, focusing on both key concepts and practical applications. It consists of five modules: an introduction, segmentation and registration, radiomics, fMRI data processing, and hands-on practical exercises. Through a combination of lectures and interactive notebooks, students will develop both theoretical knowledge and practical skills for processing and analyzing MR images in research and clinical settings.
Target Audience: Researchers, clinicians, radiologists, medical physicists, and students in biomedical engineering or related fields interested in learning MR image processing techniques for research and clinical applications.
Prerequisites: Basic understanding of MR imaging principles, fundamental programming skills (preferably in Python or MATLAB), and familiarity with medical imaging concepts.
Course Objectives:
1. Provide a comprehensive understanding of MR image processing techniques, including segmentation, registration, radiomics, and fMRI data processing.
2. Equip students with the ability to utilize theoretical knowledge to solve practical challenges in MR image analysis using interactive tools.
3. Enable students to apply advanced image processing methods in both research and clinical settings.
4. Develop hands-on expertise through interactive notebooks and practical exercises.
5. Foster an understanding of how MR image analysis contributes to clinical decision-making and research outcomes.
Course Materials:
Textbook: McAndrew, A. Introduction to Digital Image Processing with MATLAB®. (ISBN 0-534-40011-6).
Software: MATLAB/Python
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Module |
Topic |
Lecture |
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Module 1A |
Introduction: Why image processing? |
Recording in Progress |
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Module 1B |
Practical module 1A |
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Module 2A |
Pixels & Contrast & Histograms |
Recording in Progress |
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Module 2B |
Practical module 2A |
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Module 3A |
Linear Registration & Motion Correction |
Recording in Progress |
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Module 3B |
Practical module 3A |
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Module 4 |
Edges, Filters, Masking |
Recording in Progress |
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Module 5A |
Fourier Transform (FT) |
Recording in Progress |
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Module 5B |
Practical module 4 and 5A |
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Module 6A |
Segmentation Basics |
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Module 6B |
Practical module 6A |
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Module 7 |
Distance transform; Hough transform for straight lines |
Recording in Progress |
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Module 8 |
Machine learning technical |
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Module 9 |
Advanced Segmentation |
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Module 10A |
Radiomics |
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Module 10B |
Radiomics Practicals |
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