Computational Physiology for Medical Image Computing - IN2319

Organization

Lecture: Prof. Bjoern Menze

External resources: Zoom video link, recorded lectures, other materials available in Moodle

Time & Location

• Lecture: Thursdays, 9:00am - 10:30am. Lecture will be streamed, slides will be made available before. Lectures will be recorded and made made available in Moodle.
• Exercise & discussion: Thursdays, 10:30am - 12:00am. A moderated discussion of the lecture of the previous week will take place ('Zentraluebung'), and a discussion of selected research papers presenting use cases. ]

Overview

Upon completion of the course, participants will be able to use computational models for extracting diagnostic information from different types of clinical image data sets. These data sets may provide information, for example, about blood perfusion or micro-structural tissue properties, about metabolic processes, or patterns of disease progression. The participants will understand the physiological concepts underlying the computational algorithms employed, and will know of advantages and shortcomings of different modeling strategies. This will allow them to analyze clinical imaging protocols with respect to the underlying physiological information, and to propose diagnostic algorithms that combine anatomical and physiological information of different imaging. A focus will be on applications from neuroimaging.

Prerequisites

• Basic knowledge in image computing (e.g., CAMP I lecture or related courses).
• Interest in algorithmic aspects of signal processing and image computing tasks.

Regulations

Due to the electronic teaching requirements of this semester, the format will differ a bit from previous years and from the module description. No coding exercises will take place, but a discussion and presentation of research papers instead.

• Module Specification: IN2319
  
• SWS: 2+2
  
• ECTS: 6 Credits
  
• Course Language: English
  
• Exam: oral or written exam
  

Course Schedule

Literature

Structure Macro:

• 1) https://www.na-mic.org/w/img_auth.php/7/79/Pohl-NeuroImage-06.pdf
• 2) https://arxiv.org/abs/1801.04161
• 3) https://www.nature.com/articles/ncomms5006
• X) Use case: see end of lecture slides

Structure Micro:

• 1) http://www.ajnr.org/content/29/4/632.full
• 2) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3683822/
• X) Use case: http://home.in.tum.de/~menze/teaching/diffusion.zip (PW: CpMic^?)

Blood Perfusion:

• 1) https://www.sciencedirect.com/science/article/pii/S2211568413003306
• 2) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4170157/
• X) Use case: http://home.in.tum.de/~menze/teaching/perfusion.zip (PW: CpMic^?)

Blood Neurokinetics:

• 1) https://www.frontiersin.org/articles/10.3389/fnins.2016.00515/full
• 2) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3960973/

Metabolism MRI:

• 1) https://hal.archives-ouvertes.fr/hal-01904617/document
• 2) http://far.in.tum.de/pub/das2017miccai/das2017miccai.pdf

Metabolism PET:

• 1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5283691/
• 2) https://archive-ouverte.unige.ch/unige:111212/ATTACHMENT01

Disease Models (Tumor):

• 1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2496876/
• 2) https://arxiv.org/abs/1807.00499
• X) Use case: http://home.in.tum.de/~menze/teaching/tumor_model.zip (PW: CpMic^?)

Disease Models (Population):

• 1) http://wwwx.cs.unc.edu/~mn/sites/default/files/hart2010_longitudinal.pdf,
• 2) https://www.cir.meduniwien.ac.at/assets/people/vogl/publication/2014-miccai-WD-Vogl-Longitudinal-Alignment.pdf
• 3) https://www.sciencedirect.com/science/article/abs/pii/S1053811912000791?via%3Dihub
• 4) https://www.nature.com/articles/ncomms5022