1) Module structure (6 Credits, 4 hours per week)
• Lecture (2 h/week)
• Exercise Session (1 h/week)
• Seminar (1 h/week)
2) Language:
English
3) Content
Magnetic resonance imaging (MRI) is a non-ionizing imaging technique. MRI enables multidimensional (2D, 3D, 4D,...) and multicontrast (T1-, T2, T2*, diffusion, perfusion, susceptibility,...) in-vivo (human or animals) or ex-vivo (forensic samples, various substances, cell culture) applications using either exogenous contrast agents or endogenous substances (blood, tissue components). The well-functioning combination of different hardware and software components based on physical and mathematical principles allows the creation of MRI images for both scientific research and clinical diagnostics. MRI images can be evaluated either qualitatively or quantitatively. Quantitative evaluation allows objective, user-independent assessment of MRI datasets and supports the longitudinal analysis of disease progression as well as therapy and drug response monitoring. The module “From Spin to Clinical Decisions: MRI at the Heart of Precision Medicine and Personalized Care” provides students with mathematical, physical, and medical principles of MRI techniques used in (pre)clinical applications. It covers a broad spectrum of imaging methods designed to address anatomical, functional, and metabolic questions from both diagnostic and research perspectives. The focus lies on the theoretical foundations of image formation and processing, the functioning of different imaging techniques, and their (pre)clinical relevance. This three-part course focuses on 1) Understanding the physical mechanisms underlying MRI contrast formation, 2) Analyzing qualitative and quantitative MRI datasets using different programming languages, 3) Preparing and delivering a scientific presentation on an MRI-related topic based on current literature. This module will cover the following key topics:
• Introduction to MR-based imaging techniques, including perfusion, diffusion, susceptibility-weighted imaging, T1- and T2-mapping, and contrast-agent-free techniques: historical development and current state of the art
• Fundamentals of image processing, including image quality assessment, segmentation, and qualitative/quantitative analysis
• Future perspectives of MR-based imaging, including artificial intelligence (AI) and machine learning, novel imaging techniques, and their potential clinical and research applications
4) Literature:
• Mona Salehi Ravesh; Lecture notes, TU Dortmund University, 2026.
• Bernstein M. et al; "Handbook of MRI pulse sequences", Academic Press
• Haacke M. et. Al.; "Magnetic Resonance Imaging: Physical Principle and Sequence Design", Wiley
• https://mriquestions.com/index.html
• https://www.magnetomworld.siemens-healthineers.com/publications/mr-basics
• www.pubmed.org
5) Learning outcome
The lecture covers the MRI techniques (T1, T2, T2*, perfusion-, diffusion-weighting,…) that are essential for understanding qualitative and quantitative MRI applications. As part of the exercise sessions, students will implement mathematical and physical methods using a programming language of their choice to perform qualitative and quantitative analyses of MRI data. In the seminar, students will extract the key findings of selected research articles and present recent advances in the application of various MRI techniques to (pre)clinical imaging. This provides students with practical experience and further develops their presentation skills, which are essential for future research activities and participation in international conferences. Upon successful completion of this module, students will be able to:
• Explain the mathematical, physical, and medical principles underlying MR-based imaging techniques
• Compare advantages and limitations of MRI techniques for addressing anatomical, functional, and metabolic questions in (pre)clinical diagnostics and research
• Apply image processing algorithms to MRI datasets with varying contrast mechanisms and dimensionality
• Understand the role of MRI in personalized medicine and clinical research
• Discuss current trends and future developments in medical imaging, including AI and machine learning approaches
6) Examination Course credit:
Active participation in weekly exercises and seminar presentations. Module exam: Written exam (45 minutes)
7) Participation Requirements
Knowledge from the courses "Advanced medical imaging" is desirable, but not mandatory for understanding the topics in this course.
• Lecture (2 h/week)
• Exercise Session (1 h/week)
• Seminar (1 h/week)
2) Language:
English
3) Content
Magnetic resonance imaging (MRI) is a non-ionizing imaging technique. MRI enables multidimensional (2D, 3D, 4D,...) and multicontrast (T1-, T2, T2*, diffusion, perfusion, susceptibility,...) in-vivo (human or animals) or ex-vivo (forensic samples, various substances, cell culture) applications using either exogenous contrast agents or endogenous substances (blood, tissue components). The well-functioning combination of different hardware and software components based on physical and mathematical principles allows the creation of MRI images for both scientific research and clinical diagnostics. MRI images can be evaluated either qualitatively or quantitatively. Quantitative evaluation allows objective, user-independent assessment of MRI datasets and supports the longitudinal analysis of disease progression as well as therapy and drug response monitoring. The module “From Spin to Clinical Decisions: MRI at the Heart of Precision Medicine and Personalized Care” provides students with mathematical, physical, and medical principles of MRI techniques used in (pre)clinical applications. It covers a broad spectrum of imaging methods designed to address anatomical, functional, and metabolic questions from both diagnostic and research perspectives. The focus lies on the theoretical foundations of image formation and processing, the functioning of different imaging techniques, and their (pre)clinical relevance. This three-part course focuses on 1) Understanding the physical mechanisms underlying MRI contrast formation, 2) Analyzing qualitative and quantitative MRI datasets using different programming languages, 3) Preparing and delivering a scientific presentation on an MRI-related topic based on current literature. This module will cover the following key topics:
• Introduction to MR-based imaging techniques, including perfusion, diffusion, susceptibility-weighted imaging, T1- and T2-mapping, and contrast-agent-free techniques: historical development and current state of the art
• Fundamentals of image processing, including image quality assessment, segmentation, and qualitative/quantitative analysis
• Future perspectives of MR-based imaging, including artificial intelligence (AI) and machine learning, novel imaging techniques, and their potential clinical and research applications
4) Literature:
• Mona Salehi Ravesh; Lecture notes, TU Dortmund University, 2026.
• Bernstein M. et al; "Handbook of MRI pulse sequences", Academic Press
• Haacke M. et. Al.; "Magnetic Resonance Imaging: Physical Principle and Sequence Design", Wiley
• https://mriquestions.com/index.html
• https://www.magnetomworld.siemens-healthineers.com/publications/mr-basics
• www.pubmed.org
5) Learning outcome
The lecture covers the MRI techniques (T1, T2, T2*, perfusion-, diffusion-weighting,…) that are essential for understanding qualitative and quantitative MRI applications. As part of the exercise sessions, students will implement mathematical and physical methods using a programming language of their choice to perform qualitative and quantitative analyses of MRI data. In the seminar, students will extract the key findings of selected research articles and present recent advances in the application of various MRI techniques to (pre)clinical imaging. This provides students with practical experience and further develops their presentation skills, which are essential for future research activities and participation in international conferences. Upon successful completion of this module, students will be able to:
• Explain the mathematical, physical, and medical principles underlying MR-based imaging techniques
• Compare advantages and limitations of MRI techniques for addressing anatomical, functional, and metabolic questions in (pre)clinical diagnostics and research
• Apply image processing algorithms to MRI datasets with varying contrast mechanisms and dimensionality
• Understand the role of MRI in personalized medicine and clinical research
• Discuss current trends and future developments in medical imaging, including AI and machine learning approaches
6) Examination Course credit:
Active participation in weekly exercises and seminar presentations. Module exam: Written exam (45 minutes)
7) Participation Requirements
Knowledge from the courses "Advanced medical imaging" is desirable, but not mandatory for understanding the topics in this course.
- Lehrende:r: Mona Salehi Ravesh