Suchergebnis: Katalogdaten im Frühjahrssemester 2020
Biomedical Engineering Master | ||||||
Vertiefungsfächer | ||||||
Bioimaging | ||||||
Wahlfächer der Vertiefung Diese Fächer sind für die Vertiefung in Bioimaging besonders empfohlen. Bei abweichender Fächerwahl konsultieren Sie bitte den Track Adviser. | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|
227-0967-00L | Computational Neuroimaging Clinic | W | 3 KP | 2V | K. Stephan | |
Kurzbeschreibung | This seminar teaches problem solving skills for computational neuroimaging (incl. associated computational analyses of 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., concerning mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. | |||||
Lernziel | 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. | |||||
Inhalt | This seminar teaches problem solving skills for computational neuroimaging (incl. associated computational analyses of 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., concerning mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. | |||||
Voraussetzungen / Besonderes | The participants are expected to be familiar with general principles of statistics and have successfully completed at least one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry' | |||||
151-0622-00L | Measuring on the Nanometer Scale | W | 2 KP | 2G | A. Stemmer | |
Kurzbeschreibung | Introduction to theory and practical application of measuring techniques suitable for the nano domain. | |||||
Lernziel | Introduction to theory and practical application of measuring techniques suitable for the nano domain. | |||||
Inhalt | 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. | |||||
Skript | Class notes and special papers will be distributed. | |||||
227-0125-00L | Optics and Photonics | W | 6 KP | 2V + 2U | J. Leuthold | |
Kurzbeschreibung | This lecture covers both - the fundamentals of "Optics" such as e.g. "ray optics", "coherence", the "Planck law" or the "Einstein relations" but also the fundamentals of "Photonics" on the generation, processing, transmission and detection of photons. | |||||
Lernziel | A sound base for work in the field of optics and photonics will be given. | |||||
Inhalt | Chapter 1: Ray Optics Chapter 2: Electromagnetic Optics Chapter 3: Polarization Chapter 4: Coherence and Interference Chapter 5: Fourier Optics and Diffraction Chapter 6: Guided Wave Optics Chapter 7: Optical Fibers Chapter 8: The Laser | |||||
Skript | Lecture notes will be handed out. | |||||
Voraussetzungen / Besonderes | Fundamentals of Electromagnetic Fields (Maxwell Equations) & Bachelor Lectures on Physics. | |||||
227-0384-00L | Ultrasound Fundamentals, Imaging, and Medical Applications Course is offered for the last time in Spring Semester 2020. | W | 4 KP | 3G | O. Göksel | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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). Hands-on exercises: These will help to apply the concepts learned in the course, using simulation environments (such as Matlab k-Wave and FieldII toolboxes). The exercises will involve a mix of design, implementation, and evaluation examples commonly encountered in practical applications. Project: Current and relevant applications in the field of ultrasound are offered as project topics. Projects will be carried out throughout the course, where the project reporting and presentations will be due towards the end of the semester. These will be part of the assessment in grading. | |||||
Voraussetzungen / Besonderes | Prerequisites: Familiarity with basic numerical methods. Basic programming skills in Matlab. | |||||
227-0390-00L | Elements of Microscopy | W | 4 KP | 3G | M. Stampanoni, G. Csúcs, A. Sologubenko | |
Kurzbeschreibung | 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. | |||||
Lernziel | Solid introduction to the basics of microscopy, either with visible light, electrons or X-rays. | |||||
Inhalt | 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. | |||||
Literatur | Available Online. | |||||
227-0391-00L | Medical Image Analysis Basic knowledge of computer vision would be helpful. | W | 3 KP | 2G | E. Konukoglu, M. A. Reyes Aguirre | |
Kurzbeschreibung | 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. | |||||
Lernziel | This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas. | |||||
Voraussetzungen / Besonderes | 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. | |||||
227-0396-00L | EXCITE Interdisciplinary Summer School on Bio-Medical Imaging The school admits 60 MSc or PhD students with backgrounds in biology, chemistry, mathematics, physics, computer science or engineering based on a selection process. Students have to apply for acceptance by April 20, 2020. To apply a curriculum vitae and an application letter need to be submitted. The notification of acceptance will be given by May 22, 2020. Further information can be found at: Link. | W | 4 KP | 6G | S. Kozerke, G. Csúcs, J. Klohs-Füchtemeier, S. F. Noerrelykke, M. P. Wolf | |
Kurzbeschreibung | Two-week summer school organized by EXCITE (Center for EXperimental & Clinical Imaging TEchnologies Zurich) on biological and medical imaging. The course covers X-ray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, infrared and optical microscopy, electron microscopy, image processing and analysis. | |||||
Lernziel | Students understand basic concepts and implementations of biological and medical imaging. Based on relative advantages and limitations of each method they can identify preferred procedures and applications. Common foundations and conceptual differences of the methods can be explained. | |||||
Inhalt | Two-week summer school on biological and medical imaging. The course covers concepts and implementations of X-ray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, infrared and optical microscopy and electron microscopy. Multi-modal and multi-scale imaging and supporting technologies such as image analysis and modeling are discussed. Dedicated modules for physical and life scientists taking into account the various backgrounds are offered. | |||||
Skript | Hand-outs, Web links | |||||
Voraussetzungen / Besonderes | The school admits 60 MSc or PhD students with backgrounds in biology, chemistry, mathematics, physics, computer science or engineering based on a selection process. To apply a curriculum vitae, a statement of purpose and applicants references need to be submitted. Further information can be found at: Link | |||||
227-0455-00L | Terahertz: Technology and Applications | W | 5 KP | 3G + 3A | K. Sankaran | |
Kurzbeschreibung | This block course will provide a solid foundation for understanding physical principles of THz applications. We will discuss various building blocks of THz technology - components dealing with generation, manipulation, and detection of THz electromagnetic radiation. We will introduce THz applications in the domain of imaging, sensing, communications, non-destructive testing and evaluations. | |||||
Lernziel | This is an introductory course on Terahertz (THz) technology and applications. Devices operating in THz frequency range (0.1 to 10 THz) have been increasingly studied in the recent years. Progress in nonlinear optical materials, ultrafast optical and electronic techniques has strengthened research in THz application developments. Due to unique interaction of THz waves with materials, applications with new capabilities can be developed. In theory, they can penetrate somewhat like X-rays, but are not considered harmful radiation, because THz energy level is low. They should be able to provide resolution as good as or better than magnetic resonance imaging (MRI), possibly with simpler equipment. Imaging, very-high bandwidth communication, and energy harvesting are the most widely explored THz application areas. We will study the basics of THz generation, manipulation, and detection. Our emphasis will be on the physical principles and applications of THz in the domain of imaging, sensing, communications, non-destructive testing and evaluations. The second part of the block course will be a short project work related to the topics covered in the lecture. The learnings from the project work should be presented in the end. | |||||
Inhalt | PART I: - INTRODUCTION - Chapter 1: Introduction to THz Physics Chapter 2: Components of THz Technology - THz TECHNOLOGY MODULES - Chapter 3: THz Generation Chapter 4: THz Detection Chapter 5: THz Manipulation - APPLICATIONS - Chapter 6: THz Imaging / Sensing / Communication Chapter 7: THz Non-destructive Testing Chapter 8: THz Applications in Plastic & Recycling Industries PART 2: - PROJECT WORK - Short project work related to the topics covered in the lecture. Short presentation of the learnings from the project work. Full guidance and supervision will be given for successful completion of the short project work. | |||||
Skript | Soft-copy of lectures notes will be provided. | |||||
Literatur | - Yun-Shik Lee, Principles of Terahertz Science and Technology, Springer 2009 - Ali Rostami, Hassan Rasooli, and Hamed Baghban, Terahertz Technology: Fundamentals and Applications, Springer 2010 | |||||
Voraussetzungen / Besonderes | Basic foundation in physics, particularly, electromagnetics is required. Students who want to refresh their electromagnetics fundamentals can get additional material required for the course. | |||||
227-0966-00L | Quantitative Big Imaging: From Images to Statistics | W | 4 KP | 2V + 1U | P. A. Kaestner, M. Stampanoni | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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 | |||||
Inhalt | 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. | |||||
Skript | Available online. | |||||
Literatur | Will be indicated during the lecture. | |||||
Voraussetzungen / Besonderes | 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 Per Anders Kaestner directly (Link). 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. | |||||
227-0973-00L | Translational Neuromodeling | W | 8 KP | 3V + 2U + 1A | K. Stephan | |
Kurzbeschreibung | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises. | |||||
Lernziel | To obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data. | |||||
Inhalt | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models. Lecture topics include: 1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics 2. Psychiatric nosology 3. Pathophysiology of psychiatric disease mechanisms 4. Principles of Bayesian inference and generative modeling 5. Variational Bayes (VB) 6. Bayesian model selection 7. Markov Chain Monte Carlo techniques (MCMC) 8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases 9. Generative models of fMRI data 10. Generative models of electrophysiological data 11. Generative models of behavioural data 12. Computational concepts of schizophrenia, depression and autism 13. Model-based predictions about individual patients Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is permitted. | |||||
Literatur | See TNU website: Link | |||||
Voraussetzungen / Besonderes | Good knowledge of principles of statistics, good programming skills (MATLAB or Python) | |||||
227-0976-00L | Computational Psychiatry & Computational Psychosomatics Number of participants limited to 24. Information for UZH students: Enrolment to this course unit only possible at ETH Zurich. No enrolment to module BMT20002. Please mind the ETH enrolment deadlines for UZH students: Link | W | 2 KP | 4S | K. Stephan | |
Kurzbeschreibung | This seminar deals with the development of clinically relevant computational tools and/or their application to psychiatry and psychosomatics. Complementary to the annual Computational Psychiatry Course, it serves to build bridges between computational scientists and clinicians and is designed to foster in-depth exchange, with ample time for discussion | |||||
Lernziel | Understanding strengths and weaknesses of current trends in the development of clinically relevant computational tools and their application to problems in psychiatry and psychosomatics. | |||||
Inhalt | This seminar deals with the development of computational tools (e.g. generative models, machine learning) and/or their application to psychiatry and psychosomatics. The seminar includes (i) presentations by computational scientists and clinicians, (ii) group discussion with focus on methodology and clinical utility, (iii) self-study based on literature provided by presenters. | |||||
Literatur | Literature for additional self-study of the topics presented in this seminar will be provided by the presenters and will be available online at Link | |||||
Voraussetzungen / Besonderes | Participants are expected to be familiar with general principles of statistics (including Bayesian statistics) and have successfully completed the course “Computational Psychiatry” (Course number 227-0971-00L). | |||||
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: Link | W | 6 KP | 2V + 1U | D. Kiper | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Literatur | 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. | |||||
376-1397-00L | Orthopaedic Biomechanics Number of participants limited to 48. | W | 3 KP | 2G | R. Müller, P. Atkins, J. Schwiedrzik | |
Kurzbeschreibung | This course is aimed at studying the mechanical and structural engineering of the musculoskeletal system alongside the analysis and design of orthopaedic solutions to musculoskeletal failure. | |||||
Lernziel | To apply engineering and design principles to orthopaedic biomechanics, to quantitatively assess the musculoskeletal system and model it, and to review rigid-body dynamics in an interesting context. | |||||
Inhalt | Engineering principles are very important in the development and application of quantitative approaches in biology and medicine. This course includes a general introduction to structure and function of the musculoskeletal system: anatomy and physiology of musculoskeletal tissues and joints; biomechanical methods to assess and quantify tissues and large joint systems. These methods will also be applied to musculoskeletal failure, joint replacement and reconstruction; implants; biomaterials and tissue engineering. | |||||
Skript | Stored on Moodle. | |||||
Literatur | Orthopaedic Biomechanics: Mechanics and Design in Musculoskeletal Systems Authors: Donald L. Bartel, Dwight T. Davy, Tony M. Keaveny Publisher: Prentice Hall; Copyright: 2007 ISBN-10: 0130089095; ISBN-13: 9780130089090 | |||||
Voraussetzungen / Besonderes | Lectures will be given in English. | |||||
402-0673-00L | Physics in Medical Research: From Humans to Cells | W | 6 KP | 2V + 1U | B. K. R. Müller | |
Kurzbeschreibung | The aim of this lecture series is to introduce the role of physics in state-of-the-art medical research and clinical practice. Topics to be covered range from applications of physics in medical implant technology and tissue engineering, through imaging technology, to its role in interventional and non-interventional therapies. | |||||
Lernziel | The lecture series is focused on applying knowledge from physics in diagnosis, planning, and therapy close to clinical practice and fundamental medical research. Beside a general overview, the lectures give a deep insight into a very few selected techniques, which will help the students to apply the knowledge to a broad range of related techniques. In particular, the lectures will elucidate the physics behind the X-ray imaging currently used in clinical environment and contemporary high-resolution developments. It is the goal to visualize and quantify (sub-)microstructures of human tissues and implants as well as their interface. Ultrasound is not only used for diagnostic purposes but includes therapeutic approaches such as the control of the blood-brain barrier under MR-guidance. Physicists in medicine are working on modeling and simulation. Based on the vascular structure in cancerous and healthy tissues, the characteristic approaches in computational physics to develop strategies against cancer are presented. In order to deliberately destroy cancerous tissue, heat can be supplied or extracted in different manner: cryotherapy (heat conductivity in anisotropic, viscoelastic environment), radiofrequency treatment (single and multi-probe), laser application, and proton therapy. Medical implants play an important role to take over well-defined tasks within the human body. Although biocompatibility is here of crucial importance, the term is insufficiently understood. The aim of the lectures is the understanding of biocompatibility performing well-defined experiments in vitro and in vivo. Dealing with different classes of materials (metals, ceramics, polymers) the influence of surface modifications (morphology and surface coatings) are key issues for implant developments, which might be bio-inspired. Mechanical stimuli can drastically influence soft and hard tissue behavior. The students should realize that a physiological window exists, where a positive tissue response is expected and how the related parameter including strain, frequency, and resting periods can be selected and optimized for selected tissues such as bone. For the treatment of severe incontinence, we are developing artificial smart muscles. The students should have a critical look at promising solutions and the selection procedure as well as realize the time-consuming and complex way to clinical practice. The course will be completed by relating the numerous examples and a common round of questions. | |||||
Inhalt | This lecture series will cover the following topics: Introduction: Imaging the human body down to individual cells and beyond Development of artificial muscles for incontinence treatment X-ray-based computed tomography in clinics and related medical research High-resolution micro computed tomography Phase tomography using hard X-rays in biomedical research Metal-based implants and scaffolds Natural and synthetic ceramics for implants and regenerative medicine Biomedical simulations Polymers for medical implants From open surgery to non-invasive interventions - Physical approaches in medical imaging Dental research Focused Ultrasound and its clinical use Applying physics in medicine: Benefitting patients | |||||
Skript | Link login and password to be provided during the lecture | |||||
Voraussetzungen / Besonderes | Students from other departments are very welcome to join and gain insight into a variety of sophisticated techniques for the benefit of patients. No special knowledge is required. Nevertheless, gaps in basic physical knowledge will require additional efforts. | |||||
465-0952-00L | Biomedical Photonics Findet dieses Semester nicht statt. | W | 3 KP | 2V | ||
Kurzbeschreibung | The lecture introduces the principles of generation, propagation and detection of light and its therapeutic and diagnostic application in medicine. | |||||
Lernziel | The lecture provides knowledge about light sources and light delivery systems, optical biomedical imaging techniques, optical measurement technologies and their specific applications in medicine. Fundamental principles will be accompanied by practical and contemporary examples. Different selected optical systems used in diagnostics and therapy will be discussed. | |||||
Inhalt | Optics always was strongly connected to the observation and interpretation of physiological phenomenon. The basic knowledge of optics for example was initially gained by studying the function of the human eye. Nowadays, biomedical optics is an independent research field that is no longer restricted to the observation of physiological processes but studies diagnostic and therapeutic problems in medicine. A basic prerequisite for applying optical techniques in medicine is the understanding of the physical properties of light, the light propagation in and its interaction with tissue. The lecture gives inside into the generation, propagation and detection of light, its propagation in tissue and into selected optical applications in medicine. Various optical imaging techniques (optical coherence tomography or optoacoustics) as well as therapeutic laser applications (refractive surgery, photodynamic therapy or nanosurgery) will be discussed. | |||||
Skript | will be provided via Internet (Ilias) | |||||
Literatur | - M. Born, E. Wolf, "Principles of Optics", Pergamon Press - B.E.A. Saleh, M.C. Teich, "Fundamentals of Photonics", John Wiley and Sons, Inc. - O. Svelto, "Principles of Lasers", Plenum Press - J. Eichler, T. Seiler, "Lasertechnik in der Medizin", Springer Verlag - M.H. Niemz, "Laser-Tissue Interaction", Springer Verlag - A.J. Welch, M.J.C. van Gemert, "Optical-thermal response of laser-irradiated tissue", Plenum Press | |||||
Voraussetzungen / Besonderes | Language of instruction: English This is the same course unit (465-0952-00L) with former course title "Medical Optics". |
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