Marco Stampanoni: Catalogue data in Spring Semester 2021
|Name||Prof. Dr. Marco Stampanoni|
Professur für Röntgenbildgebung
ETH Zürich, GLC F 17.1
Gloriastrasse 37/ 39
|Telephone||+41 44 632 86 50|
|Department||Information Technology and Electrical Engineering|
|227-0390-00L||Elements of Microscopy||4 credits||3G||M. Stampanoni, G. Csúcs, A. Sologubenko|
|Abstract||The lecture reviews the basics of microscopy by discussing wave propagation, diffraction phenomena and aberrations. It gives the basics of light microscopy, introducing fluorescence, wide-field, confocal and multiphoton imaging. It further covers 3D electron microscopy and 3D X-ray tomographic micro and nanoimaging.|
|Objective||Solid introduction to the basics of microscopy, either with visible light, electrons or X-rays.|
|Content||It would be impossible to imagine any scientific activities without the help of microscopy. Nowadays, scientists can count on very powerful instruments that allow investigating sample down to the atomic level.|
The lecture includes a general introduction to the principles of microscopy, from wave physics to image formation. It provides the physical and engineering basics to understand visible light, electron and X-ray microscopy.
During selected exercises in the lab, several sophisticated instrument will be explained and their capabilities demonstrated.
|227-0966-00L||Quantitative Big Imaging: From Images to Statistics||4 credits||2V + 1U||P. A. Kaestner, M. Stampanoni|
|Abstract||The lecture focuses on the challenging task of extracting robust, quantitative metrics from imaging data and is intended to bridge the gap between pure signal processing and the experimental science of imaging. The course will focus on techniques, scalability, and science-driven analysis.|
|Objective||1. Introduction of applied image processing for research science covering basic image processing, quantitative methods, and statistics.|
2. Understanding of imaging as a means to accomplish a scientific goal.
3. Ability to apply quantitative methods to complex 3D data to determine the validity of a hypothesis
|Content||Imaging is a well established field and is rapidly growing as technological improvements push the limits of resolution in space, time, material and functional sensitivity. These improvements have meant bigger, more diverse datasets being acquired at an ever increasing rate. With methods varying from focused ion beams to X-rays to magnetic resonance, the sources for these images are exceptionally heterogeneous; however, the tools and techniques for processing these images and transforming them into quantitative, biologically or materially meaningful information are similar. |
The course consists of equal parts theory and practical analysis of first synthetic and then real imaging datasets. Basic aspects of image processing are covered such as filtering, thresholding, and morphology. From these concepts a series of tools will be developed for analyzing arbitrary images in a very generic manner. Specifically a series of methods will be covered, e.g. characterizing shape, thickness, tortuosity, alignment, and spatial distribution of material features like pores. From these metrics the statistics aspect of the course will be developed where reproducibility, robustness, and sensitivity will be investigated in order to accurately determine the precision and accuracy of these quantitative measurements. A major emphasis of the course will be scalability and the tools of the 'Big Data' trend will be discussed and how cluster, cloud, and new high-performance large dataset techniques can be applied to analyze imaging datasets. In addition, given the importance of multi-scale systems, a data-management and analysis approach based on modern databases will be presented for storing complex hierarchical information in a flexible manner. Finally as a concluding project the students will apply the learned methods on real experimental data from the latest 3D experiments taken from either their own work / research or partnered with an experimental imaging group.
The course provides the necessary background to perform the quantitative evaluation of complicated 3D imaging data in a minimally subjective or arbitrary manner to answer questions coming from the fields of physics, biology, medicine, material science, and paleontology.
|Lecture notes||Available online. https://imaginglectures.github.io/Quantitative-Big-Imaging-2021/weeklyplan.html|
|Literature||Will be indicated during the lecture.|
|Prerequisites / Notice||Ideally, students will have some familiarity with basic manipulation and programming in languages like Python, Matlab, or R. Interested students who are worried about their skill level in this regard are encouraged to contact Anders Kaestner directly (email@example.com).|
More advanced students who are familiar with Python, C++, (or in some cases Java) will have to opportunity to develop more of their own tools.
|227-0968-00L||Monte Carlo in Medical Physics||4 credits||3G||M. Stampanoni, M. K. Fix|
|Abstract||Introduction in basics of Monte Carlo simulations in the field of medical radiation physics. General recipe for Monte Carlo simulations in medical physics from code selection to fine-tuning the implementation. Characterization of radiation by means of Monte Carlo simulations.|
|Objective||Understanding the concept of the Monte Carlo method. Getting familiar with the Monte Carlo technique, knowing different codes and several applications of this method. Learn how to use Monte Carlo in the field of applied medical radiation physics. Understand the usage of Monte Carlo to characterize the physical behaviour of ionizing radiation in medical physics. Share the enthusiasm about the potential of the Monte Carlo technique and its usefulness in an interdisciplinary environment.|
|Content||The lecture provides the basic principles of the Monte Carlo method in medical radiation physics. Some fundamental concepts on applications of ionizing radiation in clinical medical physics will be reviewed. Several techniques in order to increase the simulation efficiency of Monte Carlo will be discussed. A general recipe for performing Monte Carlo simulations will be compiled. This recipe will be demonstrated for typical clinical devices generating ionizing radiation, which will help to understand implementation of a Monte Carlo model. Next, more patient related effects including the estimation of the dose distribution in the patient, patient movements and imaging of the patient's anatomy. A further part of the lecture covers the simulation of radioactive sources as well as heavy ion treatment modalities. The field of verification and quality assurance procedures from the perspective of Monte Carlo simulations will be discussed. To complete the course potential future applications of Monte Carlo methods in the evolving field of treating patients with ionizing radiation.|
|Lecture notes||A script will be provided.|
|227-0970-00L||Research Topics in Biomedical Engineering||0 credits||2K||K. P. Prüssmann, S. Kozerke, M. Stampanoni, K. Stephan, J. Vörös|
|Abstract||Current topics in Biomedical Engineering presented mostly by external speakers from academia and industry.|