Search result: Catalogue data in Spring Semester 2018

Number	Title	Type	ECTS	Hours	Lecturers
MAS in Medical Physics
Specialization: General Medical Physics and Biomedical Engineering
Major in Bioimaging
Core Courses
227-0390-00L	Elements of Microscopy	W	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.
Learning 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.
Literature	Available Online.
227-0946-00L	Molecular Imaging - Basic Principles and Biomedical Applications	W	2 credits	2V	M. Rudin
Abstract	Concept: What is molecular imaging. Discussion/comparison of the various imaging modalities used in molecular imaging. Design of target specific probes: specificity, delivery, amplification strategies. Biomedical Applications.
Learning objective	Molecular Imaging is a rapidly emerging discipline that translates concepts developed in molecular biology and cellular imaging to in vivo imaging in animals and ultimatly in humans. Molecular imaging techniques allow the study of molecular events in the full biological context of an intact organism and will therefore become an indispensable tool for biomedical research.
Content	Concept: What is molecular imaging. Discussion/comparison of the various imaging modalities used in molecular imaging. Design of target specific probes: specificity, delivery, amplification strategies. Biomedical Applications.
227-0948-00L	Magnetic Resonance Imaging in Medicine	W	4 credits	3G	S. Kozerke, M. Weiger Senften
Abstract	Introduction to magnetic resonance imaging and spectroscopy, encoding and contrast mechanisms and their application in medicine.
Learning objective	Understand the basic principles of signal generation, image encoding and decoding, contrast manipulation and the application thereof to assess anatomical and functional information in-vivo.
Content	Introduction to magnetic resonance imaging including basic phenomena of nuclear magnetic resonance; 2- and 3-dimensional imaging procedures; fast and parallel imaging techniques; image reconstruction; pulse sequences and image contrast manipulation; equipment; advanced techniques for identifying activated brain areas; perfusion and flow; diffusion tensor imaging and fiber tracking; contrast agents; localized magnetic resonance spectroscopy and spectroscopic imaging; diagnostic applications and applications in research.
Lecture notes	D. Meier, P. Boesiger, S. Kozerke Magnetic Resonance Imaging and Spectroscopy
227-0384-00L	Ultrasound Fundamentals, Imaging, and Medical Applications Number of participants limited to 25.	W	4 credits	3G	O. Göksel
Abstract	Ultrasound is the only imaging modality that is nonionizing (safe), real-time, cost-effective, and portable, with many medical uses in diagnosis, intervention guidance, surgical navigation, and as a therapeutic option. In this course, we introduce conventional and prospective applications of ultrasound, starting with the fundamentals of ultrasound physics and imaging.
Learning objective	Students can use the fundamentals of ultrasound, to analyze and evaluate ultrasound imaging techniques and applications, in particular in the field of medicine, as well as to design and implement basic applications.
Content	Ultrasound is used in wide range of products, from car parking sensors, to assessing fault lines in tram wheels. Medical imaging is the eye of the doctor into body; and ultrasound is the only imaging modality that is nonionizing (safe), real-time, cheap, and portable. Some of its medical uses include diagnosing breast and prostate cancer, guiding needle insertions/biopsies, screening for fetal anomalies, and monitoring cardiac arrhythmias. Ultrasound physically interacts with the tissue, and thus can also be used therapeutically, e.g., to deliver heat to treat tumors, break kidney stones, and targeted drug delivery. Recent years have seen several novel ultrasound techniques and applications – with many more waiting in the horizon to be discovered. This course covers ultrasonic equipment, physics of wave propagation, numerical methods for its simulation, image generation, beamforming (basic delay-and-sum and advanced methods), transducers (phased-, linear-, convex-arrays), near- and far-field effect, imaging modes (e.g., A-, M-, B-mode), Doppler and harmonic imaging, ultrasound signal processing techniques (e.g., filtering, time-gain-compensation, displacement tracking), image analysis techniques (deconvolution, real-time processing, tracking, segmentation, computer-assisted interventions), acoustic-radiation force, plane-wave imaging, contrast agents, micro-bubbles, elastography, biomechanical characterization, high-intensity focused ultrasound and therapy, lithotripsy, histotripsy, photo-acoustics phenomenon and opto-acoustic imaging, as well as sample non-medical applications such as the basics of non-destructive testing (NDT).
Prerequisites / Notice	Hands-on exercises will help apply concepts learned in the module, and will involve a mix of designing, implementing, and evaluating in simulation environments, such as Matlab FieldII and k-Wave toolboxes. Prerequisites: Familiarity with basic numerical methods. Basic programming skills and experience in Matlab.

Practical Work
Number	Title	Type	ECTS	Hours	Lecturers
465-0800-00L	Practical Work Only for MAS in Medical Physics	O	4 credits		external organisers
Abstract	The practical work is designed to train the students in the solution of a specific problem and provides insights in the field of the selected MAS specialization. Tutors propose the subject of the project, the project plan, and the roadmap together with the student, as well as monitor the overall execution.
Learning objective	The practical work is aimed at training the student’s capability to apply and connect specific skills acquired during the MAS specialization program towards the solution of a focused problem.

Electives
Number	Title	Type	ECTS	Hours	Lecturers
151-0622-00L	Measuring on the Nanometer Scale	W	2 credits	2G	A. Stemmer, T. Wagner
Abstract	Introduction to theory and practical application of measuring techniques suitable for the nano domain.
Learning objective	Introduction to theory and practical application of measuring techniques suitable for the nano domain.
Content	Conventional techniques to analyze nano structures using photons and electrons: light microscopy with dark field and differential interference contrast; scanning electron microscopy, transmission electron microscopy. Interferometric and other techniques to measure distances. Optical traps. Foundations of scanning probe microscopy: tunneling, atomic force, optical near-field. Interactions between specimen and probe. Current trends, including spectroscopy of material parameters.
Lecture notes	Class notes and special papers will be distributed.
227-0391-00L	Medical Image Analysis Basic knowledge of computer vision would be helpful.	W	3 credits	2G	E. Konukoglu, P. C. Cattin, M. A. Reyes Aguirre
Abstract	It is the objective of this lecture to introduce the basic concepts used in Medical Image Analysis. In particular the lecture focuses on shape representation schemes, segmentation techniques, machine learning based predictive models and various image registration methods commonly used in Medical Image Analysis applications.
Learning objective	This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas.
Prerequisites / Notice	Prerequisites: Basic concepts of mathematical analysis and linear algebra. Preferred: Basic knowledge of computer vision and machine learning would be helpful. The course will be held in English.
227-0966-00L	Quantitative Big Imaging: From Images to Statistics	W	4 credits	2V + 1U	K. S. Mader, 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.
Learning 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.
Literature	Will be indicated during the lecture.
Prerequisites / Notice	Ideally students will have some familiarity with basic manipulation and programming in languages like Matlab and R. Interested students who are worried about their skill level in this regard are encouraged to contact Kevin Mader directly (mader@biomed.ee.ethz.ch). More advanced students who are familiar with Java, C++, and Python will have to opportunity to develop more of their own tools.
227-0967-00L	Computational Neuroimaging Clinic	W	3 credits	2V	K. Stephan
Abstract	This seminar teaches problem solving skills for computational neuroimaging, based on joint analyses of neuroimaging and behavioural data. It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g. mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data.
Learning objective	1. Consolidation of theoretical knowledge (obtained in one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry') in a practical setting. 2. Acquisition of practical problem solving strategies for computational modeling of neuroimaging data.
Content	This seminar teaches problem solving skills for computational neuroimaging, based on joint analyses of neuroimaging and behavioural data. It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g. mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. .
Prerequisites / Notice	The participants are expected to have successfully completed at least one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry'
227-1034-00L	Computational Vision (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI402 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html	W	6 credits	2V + 1U	D. Kiper, K. A. Martin
Abstract	This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.
Learning objective	This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
Content	This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
Literature	Books: (recommended references, not required) 1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997. 2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.

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