Suchergebnis: Katalogdaten im Frühjahrssemester 2023
Biomedical Engineering Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Bioelectronics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Wahlfächer der Vertiefung Diese Fächer sind für die Vertiefung in Bioelectronics besonders empfohlen. Bei abweichender Fächerwahl konsultieren Sie bitte den Track Adviser. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 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. https://imaginglectures.github.io/Quantitative-Big-Imaging-2023/weeklyplan.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 Anders Kaestner directly (anders.kaestner@psi.ch). 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 227-0973-00L | Translational Neuromodeling  | W | 8 KP | 3V + 2U + 1A | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides an introduction to Translational Neuromodeling (the development of computational assays of neuronal and cognitive processes) 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 and project work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 computational assays of neuronal and cognitive processes) 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 and depression 13. Generative embedding: 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 either develop a novel generative model (and demonstrate its properties in simulations) or devise novel applications of an existing model to empirical data in order to address a clinical question. Group work (up to 3 students) is required. Please note that some of the exercises involve the use of open source software in Matlab. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | See TNU website: https://www.tnu.ethz.ch/en/teaching | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Good knowledge of principles of statistics, good programming skills (the majority of the open source software tools used is in MATLAB; for project work, Julia can also be used) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 227-0976-00L | Computational Psychiatry & Computational Psychosomatics Findet dieses Semester nicht statt. 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 https://www.tnu.ethz.ch/en/teaching | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0220-00L | Introduction to Machine Learning Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch | W | 8 KP | 4V + 2U + 1A | A. Krause, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course introduces the foundations of learning and making predictions based on data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 252-0312-00L | Mobile Health and Activity Monitoring | W | 6 KP | 2V + 3A | C. Holz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Health and activity monitoring has become a key purpose of mobile & wearable devices, e.g., phones, watches, and rings. We will cover the phenomena they capture, i.e., user behavior, actions, and human physiology, as well as the sensors, signals, and methods for processing and analysis. For the exercise, students will receive a wristband to stream and analyze activity and health signals. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course will combine high-level concepts with low-level technical methods needed to sense, detect, and understand them. High-level: – sensing modalities for interactive systems – "activities" and "events" (exercises and other mechanical activities such as movements and resulting vibrations) – health monitoring (basic cardiovascular physiology) – affective computing (emotions, mood, personality) Lower-level: – sampling and filtering, time and frequency domains – cross-modal sensor systems, signal synchronization and correlation – event detection, classification, prediction using basic signal processing as well as learning-based methods – sensor types: optical, mechanical/acoustic, electromagnetic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Health and activity monitoring has become a key purpose of mobile and wearable devices, including phones, (smart) watches, (smart) rings, (smart) belts, and other trackers (e.g., shoe clips, pendants). In this course, we will cover the fundamental aspects that these devices observe, i.e., user behavior, actions, and physiological dynamics of the human body, as well as the sensors, signals, and methods to capture, process, and analyze them. We will then cover methods for pattern extraction and classification on such data. The course will therefore touch on aspects of human activities, cardiovascular and pulmonary physiology, affective computing (recognizing, interpreting, and processing emotions), corresponding lower-level sensing systems (e.g., inertial sensing, optical sensing, photoplethysmography, eletrodermal activity, electrocardiograms) and higher-level computer vision-based sensing (facial expressions, motions, gestures), as well as processing methods for these types of data. The course will be accompanied by a group exercise project, in which students will apply the concepts and methods taught in class. Students will receive a wearable wristband device that streams IMU data to a mobile phone (code will be provided for receiving, storing, visualizing on the phone). Throughout the course and exercises, we will collect data of various human activities from the band, annotate them, analyze, classify, and interpret them. For this, existing and novel processing methods will be developed (plenty of related work exists), based on the collected data as well as existing datasets. We will also combine the band with signals obtained from the mobile phone to holistically capture and analyze health and activity data. Full details: https://teaching.siplab.org/mobile_health_activity_monitoring/2023/ Note: All lectures will be streamed live and recorded for later replay. Hybrid participation will be possible. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Copies of slides will be made available Lectures will be streamed live as well as recorded and made available online. More information on the course site: https://teaching.siplab.org/mobile_health_activity_monitoring/2023/ Note: All lectures will be streamed live and recorded for later replay. Hybrid participation will be possible. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Will be provided in the lecture | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 252-1424-00L | Models of Computation | W | 6 KP | 2V + 2U + 1A | M. Cook | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The goal of this course is to become acquainted with a wide variety of models of computation, to understand how models help us to understand the modeled systems, and to be able to develop and analyze models appropriate for new systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 261-5120-00L | Machine Learning for Health Care | W | 5 KP | 2V + 2A | V. Boeva, J. Vogt, M. Kuznetsova | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine: 1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc. 2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them. 3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them. 4) Genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line Relation to Course 261-5100-00 Computational Biomedicine: This course is a continuation of the previous course with new topics related to medical data and machine learning. The format of Computational Biomedicine II will also be different. It is helpful but not essential to attend Computational Biomedicine before attending Computational Biomedicine II. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1217-00L | Rehabilitation Engineering I: Motor Functions | W | 4 KP | 2V + 1U | R. Riener, C. E. Awai | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | “Rehabilitation” is the (re)integration of an individual with a disability into society. Rehabilitation engineering is “the application of science and technology to ameliorate the handicaps of individuals with disability”. Such handicaps can be classified into motor, sensor, and cognitive disabilities. In general, one can distinguish orthotic and prosthetic methods to overcome these disabilities. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The goal of this course is to present classical and new technical principles as well as specific examples applied to compensate or enhance motor deficits. In the 1 h exercise the students will learn how to solve representative problems with computational methods applied to exoprosthetics, wheelchair dynamics, rehabilitation robotics and neuroprosthetics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Modern methods rely more and more on the application of multi-modal and interactive techniques. Multi-modal means that visual, acoustical, tactile, and kinaesthetic sensor channels are exploited to display information to the patient. Interaction means that the exchange of information and energy occurs bi-directionally between the rehabilitation device and the human being. Thus, the device cooperates with the patient rather than imposing an inflexible strategy (e.g., movement) upon the patient. These principles are recurrent in modern technological tools to support rehabilitation, including prosthesis, orthoses, powered exoskeletons, powered wheelchairs, therapy robots and virtual reality systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Books: Burdet, Etienne, David W. Franklin, and Theodore E. Milner. Human robotics: neuromechanics and motor control. MIT press, 2013. Krakauer, John W., and S. Thomas Carmichael. Broken movement: the neurobiology of motor recovery after stroke. MIT Press, 2017. Teodorescu, Horia-Nicolai L., and Lakhmi C. Jain, eds. Intelligent systems and technologies in rehabilitation engineering. CRC press, 2000. Winters, Jack M., and Patrick E. Crago, eds. Biomechanics and neural control of posture and movement. Springer Science & Business Media, 2012. Selected Journal Articles: Abbas, James J., and Robert Riener. "Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function." Neuromodulation: Technology at the Neural Interface 4.4 (2001): 187-195. Basalp, Ekin, Peter Wolf, and Laura Marchal-Crespo. "Haptic training: which types facilitate (re) learning of which motor task and for whom Answers by a review." IEEE Transactions on Haptics (2021). Calabrò, Rocco Salvatore, et al. "Robotic gait rehabilitation and substitution devices in neurological disorders: where are we now?." Neurological Sciences 37.4 (2016): 503-514. Cooper, R. (1993) Stability of a wheelchair controlled by a human. IEEE Transactions on Rehabilitation Engineering 1, pp. 193-206. Gassert, Roger, and Volker Dietz. "Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective." Journal of neuroengineering and rehabilitation 15.1 (2018): 1-15. Laver, Kate E., et al. "Virtual reality for stroke rehabilitation." Cochrane database of systematic reviews 11 (2017). Marquez-Chin, Cesar, and Milos R. Popovic. "Functional electrical stimulation therapy for restoration of motor function after spinal cord injury and stroke: a review." Biomedical engineering online 19 (2020): 1-25. Miller, Larry E., Angela K. Zimmermann, and William G. Herbert. "Clinical effectiveness and safety of powered exoskeleton-assisted walking in patients with spinal cord injury: systematic review with meta-analysis." Medical devices (Auckland, NZ) 9 (2016): 455. Raspopovic, Stanisa. "Advancing limb neural prostheses." Science 370.6514 (2020): 290-291. Riener, R. (2013) Rehabilitation Robotics. Foundations and Trends in Robotics, Vol. 3, nos. 1-2, pp. 1-137. Riener, R., Lünenburger, L., Maier, I. C., Colombo, G., & Dietz, V. (2010). Locomotor training in subjects with sensori-motor deficits: An overview of the robotic gait orthosis Lokomat. Journal of Healthcare Engineering, 1(2), 197-216. Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43(1), pp. 2-10. Sigrist, Roland, et al. "Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review." Psychonomic bulletin & review 20.1 (2013): 21-53. Xiloyannis, Michele, et al. "Soft Robotic Suits: State of the Art, Core Technologies, and Open Challenges." IEEE Transactions on Robotics (2021). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Target Group: Students of higher semesters and PhD students of - D-MAVT, D-ITET, D-INFK - Biomedical Engineering - Medical Faculty, University of Zurich Students of other departments, faculties, courses are also welcome | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1308-00L | Development Strategies for Medical Implants | W | 3 KP | 2V + 1U | J. Mayer-Spetzler, N. Mathavan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to development strategies for implantable devices considering the interdependencies of biocompatibility, clinical, regulatory, and economical requirements; discussion of state of the art and actual trends in orthopedics, sports medicine, cardiovascular surgery, and regenerative medicine (tissue engineering). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Primary considerations in implant development. Concept of structural and surface biocompatibility and its relevance for implant design and surgical technique. Understanding conflicting factors, e.g., clinical need, economics, and regulatory requirements. Tissue engineering concepts, their strengths, and weaknesses as current and future clinical solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Understanding of clinical and economic needs as guidelines for the development of medical implants; implant and implantation-related tissue reactions, biocompatible materials, and material processing technologies; implant testing and regulatory procedures; discussion of state-of-the-art and actual trends in implant development in sports medicine, spinal and cardio-vascular surgery; introduction to tissue engineering. Commented movies from surgeries will further illustrate selected topics. Seminar: Group seminars on selected controversial topics in implant development. Participation is mandatory. Planned excursions (limited availability, not mandatory, to be confirmed): Participation (as a visitor) in a life surgery (travel at own expense) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Script (electronically available): - presented slides - selected scientific papers for further reading | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Reference to key papers will be provided during the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Only Master's students; achieved Bachelor's degree is a pre-condition Admission to the lecture is based on a letter of motivation to the lecturer J. Mayer. The number of participants in the course is limited to 30 students in total. Students will be exposed to surgical movies which may cause emotional reactions. The viewing of the surgical movies is voluntary and is the student's responsibility. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 376-1397-00L | Orthopaedic Biomechanics | W | 3 KP | 2G | R. Müller, 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1614-00L | Principles in Tissue Engineering | W | 3 KP | 2V | K. Maniura, M. Rottmar, M. Zenobi-Wong | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Fundamentals in blood coagulation; thrombosis, blood rheology, immune system, inflammation, foreign body reaction on the molecular level and the entire body are discussed. Applications of biomaterials for tissue engineering in different tissues are introduced. Fundamentals in medical implantology, in situ drug release, cell transplantation and stem cell biology are discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Understanding of molecular aspects for the application of biodegradable and biocompatible Materials. Fundamentals of tissue reactions (eg. immune responses) against implants and possible clinical consequences will be discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This class continues with applications of biomaterials and devices introduced in Biocompatible Materials I. Fundamentals in blood coagulation; thrombosis, blood rheology; immune system, inflammation, foreign body reaction on the level of the entire body and on the molecular level are introduced. Applications of biomaterials for tissue engineering in the vascular system, skeletal muscle, heart muscle, tendons and ligaments, bone, teeth, nerve and brain, and drug delivery systems are introduced. Fundamentals in medical implantology, in situ drug release, cell transplantation and stem cell biology are discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts provided during the classes and references therin. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | The molecular Biology of the Cell, Alberts et al., 5th Edition, 2009. Principles in Tissue Engineering, Langer et al., 2nd Edition, 2002 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1712-00L | Finite Element Analysis in Biomedical Engineering | W | 3 KP | 2V | S. J. Ferguson, B. Helgason | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides an introduction to finite element analysis, with a specific focus on problems and applications from biomedical engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Finite element analysis is a powerful simulation method for the (approximate) solution of boundary value problems. While its traditional roots are in the realm of structural engineering, the methods have found wide use in the biomedical engineering domain for the simulation of the mechanical response of the human body and medical devices. This course provides an introduction to finite element analysis, with a specific focus on problems and applications from biomedical engineering. This domain offers many unique challenges, including multi-scale problems, multi-physics simulation, complex and non-linear material behaviour, rate-dependent response, dynamic processes and fluid-solid interactions. Theories taught are reinforced through practical applications in self-programmed and commercial simulation software, using e.g. MATLAB, ANSYS, FEBIO. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | (Theory) The Finite Element and Finite Difference methods Gallerkin, weighted residuals, discretization (Theory) Mechanical analysis of structures Trusses, beams, solids and shells, DOFs, hand calculations of simple FE problems, underlying PDEs (Application) Mechanical analysis of structures Truss systems, beam systems, 2D solids, meshing, organ level analysis of bones (Theory and Application) Mechanical analysis of structures Micro- and multi-scale analysis, voxel models, solver limitations, large scale solvers (Theory) Non-linear mechanical analysis of structures Large strain, Newton-Rhapson, plasticity (Application) Non-linear mechanical analysis of structures Plasticity (bone), hyperelasticity, viscoelasticity (Theory and Application) Contact analysis Friction, bonding, rough contact, implants, bone-cement composites, pushout tests (Theory) Flow in Porous Media Potential problems, Terzhagi's consolidation (Application) Flow in Porous Media Confined and unconfined compression of cartilage (Theory) Heat Transfer and Mass Transport Diffusion, conduction and convection, equivalency of equations (Application) Heat Transfer and Mass Transport Sequentially-coupled poroelastic and transport models for solute transport (Theory) Computational Biofluid Dynamics Newtonian vs. Non-Newtonian fluid, potential flow (Application) Computational Biofluid Dynamics Flow between micro-rough parallel plates | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts consisting of (i) lecturers' script, (ii) selected excerpts from relevant textbooks, (iii) selected excerpts from theory manuals of commercial simulation software, (iv) relevant scientific publications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Familiarity with basic numerical methods. Programming experience with MATLAB. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1984-00L | Lasers in Medicine Findet dieses Semester nicht statt. | W | 3 KP | 3G | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Fragen wie "Was ist ein Laser, wie funktioniert er und was macht ihn so interessant für die Medizin?", aber auch "Wie breitet sich Licht im Gewebe aus und welche Wechselwirkungen treten dabei auf?" sollen beantwortet werden. Speziell wird auf therapeutische, diagnostische und bildgebende Anwendungen anhand von ausgewählten Beispielen eingegangen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Sie wissen wie ein Laser funktioniert und wie er aufgebaut ist und verstehen die physikalischen Prinzipien eines Lasers. Sie kennen die Eigenschaften von Laserlicht und wie diese für medizinische Zwecke eingesetzt werden können. Sie können unterschiedlichen Laser-Gewebe-Wechselwirkungen erklären und wissen welche Parameter diese beeinflussen. Sie können erklären, was Auflösung, Kontrast und Vergrösserung bedeutet. Sie sind in der Lage eine Laserschutzbrille für Ihr Lasersystem zu bestellen. Sie sind in der Lage für eine gezielte klinische Anwendung die richtigen Laserparameter zu bestimmen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Die Anwendung des Lasers in der Medizin gewinnt zunehmend dort an Bedeutung, wo seine speziellen Eigenschaften gezielt zur berührungslosen, selektiven und spezifischen Wirkung auf Weich- und Hartgewebe für minimal invasive Therapieformen oder zur Eröffnung neuer therapeutischer und diagnostischer Methoden eingesetzt werden können. Grundlegende Arbeiten zum Verständnis der Lichtausbreitung im Gewebe (Absorptions-, Reflexions- und Transmissionsvermögen) und die unterschiedlichen Formen der Wechselwirkung (photochemische, thermische, ablative und optomechanische Wirkung) werden eingehend behandelt. Speziell wird auf den Einfluss der Wellenlänge und der Bestrahlungszeit auf den Wechselwirkungsmechanismus eingegangen. Die unterschiedlichen medizinisch genutzten Lasertypen und Strahlführungssysteme werden hinsichtlich ihres Einsatzes im Bereich der Medizin anhand ausgesuchter Anwendungsbeispiele diskutiert. Neben den therapeutischen Wirkungen wird auf den Einsatz des Lasers in der medizinischen Diagnostik (z.B. Tumor-Fluoreszenzdiagnostik, Bildgebung) eingegangen. Die beim Einsatz des Lasers in der Medizin erforderlichen Schutzmassnahmen werden diskutiert. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | wird im Internet bereitgestellt (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. - A.E. Siegman, "Lasers", University Science Books - 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 402-0343-00L | Physics Against Cancer: The Physics of Imaging and Treating Cancer Fachstudierende UZH müssen das Modul PHY361 direkt an der UZH buchen. | W | 6 KP | 2V + 1U | A. J. Lomax, U. Schneider | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Radiotherapy is a rapidly developing and technology driven medical discipline that is heavily dependent on physics and engineering. In this lecture series, we will review and describe some of the current developments in radiotherapy, particularly from the physics and technological view point, and will indicate in which direction future research in radiotherapy will lie. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Radiotherapy is a rapidly developing and technology driven medical discipline that is heavily dependent on physics and engineering. In the last few years, a multitude of new techniques, equipment and technology have been introduced, all with the primary aim of more accurately targeting and treating cancerous tissues, leading to a precise, predictable and effective therapy technique. In this lecture series, we will review and describe some of the current developments in radiotherapy, particularly from the physics and technological view point, and will indicate in which direction future research in radiotherapy will lie. Our ultimate aim is to provide the student with a taste for the critical role that physics plays in this rapidly evolving discipline and to show that there is much interesting physics still to be done. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The lecture series will begin with a short introduction to radiotherapy and an overview of the lecture series (lecture 1). Lecture 2 will cover the medical imaging as applied to radiotherapy, without which it would be impossible to identify or accurately calculate the deposition of radiation in the patient. This will be followed by a detailed description of the treatment planning process, whereby the distribution of deposited energy within the tumour and patient can be accurately calculated, and the optimal treatment defined (lecture 3). Lecture 4 will follow on with this theme, but concentrating on the more theoretical and mathematical techniques that can be used to evaluate different treatments, using mathematically based biological models for predicting the outcome of treatments. The role of physics modeling, in order to accurately calculate the dose deposited from radiation in the patient, will be examined in lecture 5, together with a review of mathematical tools that can be used to optimize patient treatments. Lecture 6 will investigate a rather different issue, that is the standardization of data sets for radiotherapy and the importance of medical data bases in modern therapy. In lecture 7 we will look in some detail at one of the most advanced radiotherapy delivery techniques, namely Intensity Modulated Radiotherapy (IMRT). In lecture 8, the two topics of imaging and therapy will be somewhat combined, when we will describe the role of imaging in the daily set-up and assessment of patients. Lecture 9 follows up on this theme, in which a major problem of radiotherapy, namely organ motion and changes in patient and tumour geometry during therapy, will be addressed, together with methods for dealing with such problems. Finally, in lectures 10-11, we will describe in some of the multitude of different delivery techniques that are now available, including particle based therapy, rotational (tomo) therapy approaches and robot assisted radiotherapy. In the final lecture, we will provide an overview of the likely avenues of research in the next 5-10 years in radiotherapy. The course will be rounded-off with an opportunity to visit a modern radiotherapy unit, in order to see some of the techniques and delivery methods described in the course in action. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Although this course is seen as being complimentary to the Medical Physics I and II course of Dr Manser, no previous knowledge of radiotherapy is necessarily expected or required for interested students who have not attended the other two courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 microstructures of human tissues and implants as well as their interface. 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. 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: Physics in Medical Research: From humans to cells - introduction and overview Hard X-ray-based computed tomography in clinics and related research Conventional microtomography for tissue and implant characterization Synchrotron radiation-based tomography of medically relevant objects Comparing microtomography in absorption- and phase-contrast modes Tomographic imaging of cells and subcellular structures Physical approaches in medical imaging Unconventional approaches in hard X-ray imaging: Iterative reconstruction for laminography Quantitative evaluation of medically relevant, three-dimensional data Nondestructive imaging of unique objects: Physicists support museum science From open surgery to non-invasive interventions – role of medical imaging Artificial muscles for treating severe incontinence Applying physics in medicine: Benefitting patients | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | http://www.bmc.unibas.ch/education/ETH_Zurich.phtml 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 465-0952-00L | Biomedical Photonics | W | 3 KP | 2V | M. Frenz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The lecture introduces the principles of light generation, light propagation in tissue and detection of light and its therapeutic and diagnostic application in medicine. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students are expected to aquire a basic understanding of the fundamental physical principles within biomedical photonics. In particular, they will develop a broad skill set for research in fundamentals of light-tissue interaction, technologies such as microscopy, lasers and fiber optics and issues related to light applications in therapeutics and diagnostics in medicine. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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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| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0398-10L | Physiology and Anatomy for Biomedical Engineers II | W | 3 KP | 2G | M. Wyss | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course offers an introduction into the structure and function of the human body, and how these are interlinked. The visualization of anatomy is also supported by 3D-animation. Medical imaging modalities such as Computed Tomography and Magnetic Resonance imaging will be discussed in passing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students will be able to identify and enumerate important anatomical structures to describe basic physiological processes of the human body to use a 3d animation database/software to use 'anatomical language' to retrieve anatomical structures to understand basic medical terminology | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Digestive system, nutrition and digestion Thermal balance and thermoregulation Kidneys and urinary system Endocrine system and hormones Reproductive System Basic anatomy of neck, face and cranium Basics of neurophysiology and neuroanatomy Sensory organs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes and handouts | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Silbernagl S., Despopoulos A. Color Atlas of Physiology; Thieme 2008 Faller A., Schuenke M. The Human Body; Thieme 2004 Netter F. Atlas of human anatomy; Elsevier 2014 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0949-10L | Biological Methods for Engineers (Advanced Lab) Limited number of participants. Students of the MSc in Biomedical Engineering have priority. | W | 4 KP | 9P | C. Frei | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The 2 week-long, full-time block course covers basic laboratory skills and safety, cell culture, protein analysis, RNA/DNA Isolation and RT-PCR. Each topic will be introduced, followed by practical work at the bench. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The goal of this laboratory course is to give students practical exposure to basic techniques of cell and molecular biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The goal of this laboratory course is to give students practical exposure to basic techniques of cell and molecular biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Enrollment is limited and preference given to students in the Masters of Biomedical Engineering program. Due to extensive overlap with "Biological Methods for Engineers" (Basic Lab; 227-0949-00L during the autumn semester), students can only take one of the courses (Basic Lab or Extended Lab). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0945-11L | Cell and Molecular Biology for Engineers Students who have taken the semester course 227-0945-00L Cell and Molecular Biology for Engineers I, 227-0945-10L Cell and Molecular Biology for Engineers II or the year-long course before FS23 cannot earn credit points for this course. | W | 6 KP | 4G | C. Frei | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Lectures will include the following topics: DNA, chromosomes, genome engineering, RNA, proteins, genetics, synthetic biology, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer and stem cells. In addition, 3 journal clubs will be held, where recent publications will be discussed. For each journal club, students (alone or in groups of up to four students) have to write a summary and discussion of the publication. These written documents will be graded and count as 30% for the final grade. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Scripts of all lectures will be available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | "Molecular Biology of the Cell" (7th international student edition) by Alberts, Heald, Johnson, Morgan, Raff, Roberts, and Walter. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| Bioimaging | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kernfächer der Vertiefung Während des Studiums müssen mindestens 12 KP aus Kernfächern einer Vertiefung (Track) erreicht werden. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0946-00L | Molecular Imaging - Basic Principles and Biomedical Applications | W | 3 KP | 2V + 1A | D. Razansky | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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