# Search result: Catalogue data in Autumn Semester 2020

Biomedical Engineering Master | ||||||

Track Courses | ||||||

Bioelectronics | ||||||

Track Core Courses During the Master programme, a minimum of 12 CP must be obtained from track core courses. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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151-0604-00L | Microrobotics | W | 4 credits | 3G | B. Nelson, N. Shamsudhin | |

Abstract | Microrobotics is an interdisciplinary field that combines aspects of robotics, micro and nanotechnology, biomedical engineering, and materials science. The aim of this course is to expose students to the fundamentals of this emerging field. Throughout the course, the students apply these concepts in assignments. The course concludes with an end-of-semester examination. | |||||

Objective | The objective of this course is to expose students to the fundamental aspects of the emerging field of microrobotics. This includes a focus on physical laws that predominate at the microscale, technologies for fabricating small devices, bio-inspired design, and applications of the field. | |||||

Content | Main topics of the course include: - Scaling laws at micro/nano scales - Electrostatics - Electromagnetism - Low Reynolds number flows - Observation tools - Materials and fabrication methods - Applications of biomedical microrobots | |||||

Lecture notes | The powerpoint slides presented in the lectures will be made available as pdf files. Several readings will also be made available electronically. | |||||

Prerequisites / Notice | The lecture will be taught in English. | |||||

151-0605-00L | Nanosystems | W | 4 credits | 4G | A. Stemmer | |

Abstract | From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. Special emphasis on the emerging field of molecular electronic devices. | |||||

Objective | Familiarize students with basic science and engineering principles governing the nano domain. | |||||

Content | The course addresses basic science and engineering principles ruling the nano domain. We particularly work out the links between topics that are traditionally taught separately. Familiarity with basic concepts of quantum mechanics is expected. Special emphasis is placed on the emerging field of molecular electronic devices, their working principles, applications, and how they may be assembled. Topics are treated in 2 blocks: (I) From Quantum to Continuum From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. (II) Interaction Forces on the Micro and Nano Scale Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. | |||||

Literature | - Kuhn, Hans; Försterling, H.D.: Principles of Physical Chemistry. Understanding Molecules, Molecular Assemblies, Supramolecular Machines. 1999, Wiley, ISBN: 0-471-95902-2 - Chen, Gang: Nanoscale Energy Transport and Conversion. 2005, Oxford University Press, ISBN: 978-0-19-515942-4 - Ouisse, Thierry: Electron Transport in Nanostructures and Mesoscopic Devices. 2008, Wiley, ISBN: 978-1-84821-050-9 - Wolf, Edward L.: Nanophysics and Nanotechnology. 2004, Wiley-VCH, ISBN: 3-527-40407-4 - Israelachvili, Jacob N.: Intermolecular and Surface Forces. 2nd ed., 1992, Academic Press,ISBN: 0-12-375181-0 - Evans, D.F.; Wennerstrom, H.: The Colloidal Domain. Where Physics, Chemistry, Biology, and Technology Meet. Advances in Interfacial Engineering Series. 2nd ed., 1999, Wiley, ISBN: 0-471-24247-0 - Hunter, Robert J.: Foundations of Colloid Science. 2nd ed., 2001, Oxford, ISBN: 0-19-850502-7 | |||||

Prerequisites / Notice | Course format: Lectures and Mini-Review presentations: Thursday 10-13, ML F 36 Homework: Mini-Review (compulsory continuous performance assessment) Each student selects a paper (list distributed in class) and expands the topic into a Mini-Review that illuminates the particular field beyond the immediate results reported in the paper. Each Mini-Review will be presented both orally and as a written paper. | |||||

151-0621-00L | Microsystems I: Process Technology and Integration | W | 6 credits | 3V + 3U | M. Haluska, C. Hierold | |

Abstract | Students are introduced to the fundamentals of semiconductors, the basics of micromachining and silicon process technology and will learn about the fabrication of microsystems and -devices by a sequence of defined processing steps (process flow). | |||||

Objective | Students are introduced to the basics of micromachining and silicon process technology and will understand the fabrication of microsystem devices by the combination of unit process steps ( = process flow). | |||||

Content | - Introduction to microsystems technology (MST) and micro electro mechanical systems (MEMS) - Basic silicon technologies: Thermal oxidation, photolithography and etching, diffusion and ion implantation, thin film deposition. - Specific microsystems technologies: Bulk and surface micromachining, dry and wet etching, isotropic and anisotropic etching, beam and membrane formation, wafer bonding, thin film mechanical properties. Application of selected technologies will be demonstrated on case studies. | |||||

Lecture notes | Handouts (available online) | |||||

Literature | - S.M. Sze: Semiconductor Devices, Physics and Technology - W. Menz, J. Mohr, O.Paul: Microsystem Technology - Hong Xiao: Introduction to Semiconductor Manufacturing Technology - M. J. Madou: Fundamentals of Microfabrication and Nanotechnology, 3rd ed. - T. M. Adams, R. A. Layton: Introductory MEMS, Fabrication and Applications | |||||

Prerequisites / Notice | Prerequisites: Physics I and II | |||||

227-0105-00L | Introduction to Estimation and Machine Learning | W | 6 credits | 4G | H.‑A. Loeliger | |

Abstract | Mathematical basics of estimation and machine learning, with a view towards applications in signal processing. | |||||

Objective | Students master the basic mathematical concepts and algorithms of estimation and machine learning. | |||||

Content | Review of probability theory; basics of statistical estimation; least squares and linear learning; Hilbert spaces; Gaussian random variables; singular-value decomposition; kernel methods, neural networks, and more | |||||

Lecture notes | Lecture notes will be handed out as the course progresses. | |||||

Prerequisites / Notice | solid basics in linear algebra and probability theory | |||||

227-0385-10L | Biomedical Imaging | W | 6 credits | 5G | S. Kozerke, K. P. Prüssmann | |

Abstract | Introduction and analysis of medical imaging technology including X-ray procedures, computed tomography, nuclear imaging techniques using single photon and positron emission tomography, magnetic resonance imaging and ultrasound imaging techniques. | |||||

Objective | To understand the physical and technical principles underlying X-ray imaging, computed tomography, single photon and positron emission tomography, magnetic resonance imaging, ultrasound and Doppler imaging techniques. The mathematical framework is developed to describe image encoding/decoding, point-spread function/modular transfer function, signal-to-noise ratio, contrast behavior for each of the methods. Matlab exercises are used to implement and study basic concepts. | |||||

Content | - X-ray imaging - Computed tomography - Single photon emission tomography - Positron emission tomography - Magnetic resonance imaging - Ultrasound/Doppler imaging | |||||

Lecture notes | Lecture notes and handouts | |||||

Literature | Webb A, Smith N.B. Introduction to Medical Imaging: Physics, Engineering and Clinical Applications; Cambridge University Press 2011 | |||||

Prerequisites / Notice | Analysis, Linear Algebra, Physics, Basics of Signal Theory, Basic skills in Matlab programming | |||||

227-0386-00L | Biomedical Engineering | W | 4 credits | 3G | J. Vörös, S. J. Ferguson, S. Kozerke, M. P. Wolf, M. Zenobi-Wong | |

Abstract | Introduction into selected topics of biomedical engineering as well as their relationship with physics and physiology. The focus is on learning the concepts that govern common medical instruments and the most important organs from an engineering point of view. In addition, the most recent achievements and trends of the field of biomedical engineering are also outlined. | |||||

Objective | Introduction into selected topics of biomedical engineering as well as their relationship with physics and physiology. The course provides an overview of the various topics of the different tracks of the biomedical engineering master course and helps orienting the students in selecting their specialized classes and project locations. | |||||

Content | Introduction into neuro- and electrophysiology. Functional analysis of peripheral nerves, muscles, sensory organs and the central nervous system. Electrograms, evoked potentials. Audiometry, optometry. Functional electrostimulation: Cardiac pacemakers. Function of the heart and the circulatory system, transport and exchange of substances in the human body, pharmacokinetics. Endoscopy, medical television technology. Lithotripsy. Electrical Safety. Orthopaedic biomechanics. Lung function. Bioinformatics and Bioelectronics. Biomaterials. Biosensors. Microcirculation.Metabolism. Practical and theoretical exercises in small groups in the laboratory. | |||||

Lecture notes | Introduction to Biomedical Engineering by Enderle, Banchard, and Bronzino AND https://lbb.ethz.ch/education/biomedical-engineering.html | |||||

227-0393-10L | Bioelectronics and Biosensors | W | 6 credits | 2V + 2U | J. Vörös, M. F. Yanik, T. Zambelli | |

Abstract | The course introduces the concepts of bioelectricity and biosensing. The sources and use of electrical fields and currents in the context of biological systems and problems are discussed. The fundamental challenges of measuring biological signals are introduced. The most important biosensing techniques and their physical concepts are introduced in a quantitative fashion. | |||||

Objective | During this course the students will: - learn the basic concepts in biosensing and bioelectronics - be able to solve typical problems in biosensing and bioelectronics - learn about the remaining challenges in this field | |||||

Content | L1. Bioelectronics history, its applications and overview of the field - Volta and Galvani dispute - BMI, pacemaker, cochlear implant, retinal implant, limb replacement devices - Fundamentals of biosensing - Glucometer and ELISA L2. Fundamentals of quantum and classical noise in measuring biological signals L3. Biomeasurement techniques with photons L4. Acoustics sensors - Differential equation for quartz crystal resonance - Acoustic sensors and their applications L5. Engineering principles of optical probes for measuring and manipulating molecular and cellular processes L6. Optical biosensors - Differential equation for optical waveguides - Optical sensors and their applications - Plasmonic sensing L7. Basic notions of molecular adsorption and electron transfer - Quantum mechanics: Schrödinger equation energy levels from H atom to crystals, energy bands - Electron transfer: Marcus theory, Gerischer theory L8. Potentiometric sensors - Fundamentals of the electrochemical cell at equilibrium (Nernst equation) - Principles of operation of ion-selective electrodes L9. Amperometric sensors and bioelectric potentials - Fundamentals of the electrochemical cell with an applied overpotential to generate a faraday current - Principles of operation of amperometric sensors - Ion flow through a membrane (Fick equation, Nernst equation, Donnan equilibrium, Goldman equation) L10. Channels, amplification, signal gating, and patch clamp Y4 L11. Action potentials and impulse propagation L12. Functional electric stimulation and recording - MEA and CMOS based recording - Applying potential in liquid - simulation of fields and relevance to electric stimulation L13. Neural networks memory and learning | |||||

Literature | Plonsey and Barr, Bioelectricity: A Quantitative Approach (Third edition) | |||||

Prerequisites / Notice | The course requires an open attitude to the interdisciplinary approach of bioelectronics. In addition, it requires undergraduate entry-level familiarity with electric & magnetic fields/forces, resistors, capacitors, electric circuits, differential equations, calculus, probability calculus, Fourier transformation & frequency domain, lenses / light propagation / refractive index, Michaelis-Menten equation, pressure, diffusion AND basic knowledge of biology and chemistry (e.g. understanding the concepts of concentration, valence, reactants-products, etc.). | |||||

227-0421-00L | Learning in Deep Artificial and Biological Neuronal Networks | W | 4 credits | 3G | B. Grewe | |

Abstract | Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods. | |||||

Objective | The main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers. After this course students will be able to: - read and understand the main ideas and methods that are presented in today’s neuroscience papers - explain the basic ideas and concepts of plasticity in the mammalian brain - implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks. - use a diverse set of ANN regularization methods to improve learning - simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner. | |||||

Content | Deep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks. | |||||

Lecture notes | The lecture slides will be provided as a PDF after each lecture. | |||||

Prerequisites / Notice | This advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 120 students (MSc and PhDs). 2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge. | |||||

227-0427-00L | Signal Analysis, Models, and Machine LearningDoes not take place this semester. This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester). | W | 6 credits | 4G | H.‑A. Loeliger | |

Abstract | Mathematical methods in signal processing and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: neural networks, kernel methods. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events. | |||||

Objective | The course is an introduction to some basic topics in signal processing and machine learning. | |||||

Content | Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events. | |||||

Lecture notes | Lecture notes. | |||||

Prerequisites / Notice | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | |||||

227-0939-00L | Cell Biophysics | W | 6 credits | 4G | T. Zambelli | |

Abstract | A mathematical description is derived for a variety of biological phenomena at the molecular and cellular level applying the two fundamental principles of thermodynamics (entropy maximization and Gibbs energy minimization). | |||||

Objective | Engineering uses the laws of physics to predict the behavior of a system. Biological systems are so diverse and complex prompting the question whether we can apply unifying concepts of theoretical physics coping with the multiplicity of life’s mechanisms. Objective of this course is to show that biological phenomena despite their variety can be analytically described using only two concepts from statistical mechanics: maximization of the entropy and minimization of the Gibbs free energy. Starting point of the course is the probability theory, which enables to derive step-by-step the two pillars of statistical mechanics: the maximization of entropy according to the Boltzmann’s law as well as the minimization of the Gibbs free energy. Then, an assortment of biological phenomena at the molecular and cellular level (e.g. cytoskeletal polymerization, action potential, photosynthesis, gene regulation, morphogen patterning) will be examined at the light of these two principles with the aim to derive a quantitative expression describing their behavior according to experimental data. By the end of the course, students will also learn to critically evaluate the concepts of making an assumption and making an approximation. | |||||

Content | 1. Basics of theory of probability 2. Boltzmann's law 3. Entropy maximization and Gibbs free energy minimization 4. Two-state systems and the MWC model 5. Random walks and macromolecular structures 6. Electrostatics for salty solutions 7. Elasticity: fibers and membranes 8. Diffusion and crowding: cell signaling 9. Molecular motors 10. Action potential: Hodgkin-Huxley model 11. Photosynthesis 12. Gene regulation 13. Development: Turing patterns 14. Sequences and evolution | |||||

Literature | - Statistical Mechanics: K. Dill, S. Bromberg, Molecular Driving Forces, 2nd Edition, Garland Science, 2010. - Biophysics: R. Phillips, J. Kondev, J. Theriot, H. Garcia, Physical Biology of the Cell, 2nd Edition, Garland Science, 2012. | |||||

Prerequisites / Notice | Participants need a good command of differentiation and integration of a function with one or more variables (calculus) as well as of Newton's and Coulomb's laws (basics of mechanics and electrostatics). Notions of vectors in 2D and 3D are beneficial. Theory and corresponding exercises are merged together during the classes. | |||||

227-1037-00L | Introduction to Neuroinformatics | W | 6 credits | 2V + 1U | V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens | |

Abstract | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||

Objective | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||

Content | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks. | |||||

376-1714-00L | Biocompatible Materials | W | 4 credits | 3V | K. Maniura, M. Rottmar, M. Zenobi-Wong | |

Abstract | Introduction to molecules used for biomaterials, molecular interactions between different materials and biological systems (molecules, cells, tissues). The concept of biocompatibility is discussed and important techniques from biomaterials research and development are introduced. | |||||

Objective | The course covers the follwing topics: 1. Introdcution into molecular characteristics of molecules involved in the materials-to-biology interface. Molecular design of biomaterials. 2. The concept of biocompatibility. 3. Introduction into methodology used in biomaterials research and application. 4. Introduction to different material classes in use for medical applications. | |||||

Content | Introduction into natural and polymeric biomaterials used for medical applications. The concepts of biocompatibility, biodegradation and the consequences of degradation products are discussed on the molecular level. Different classes of materials with respect to potential applications in tissue engineering, drug delivery and for medical devices are introduced. Strong focus lies on the molecular interactions between materials having very different bulk and/or surface chemistry with living cells, tissues and organs. In particular the interface between the materials surfaces and the eukaryotic cell surface and possible reactions of the cells with an implant material are elucidated. Techniques to design, produce and characterize materials in vitro as well as in vivo analysis of implanted and explanted materials are discussed. A link between academic research and industrial entrepreneurship is demonstrated by external guest speakers, who present their current research topics. | |||||

Lecture notes | Handouts are deposited online (moodle). | |||||

Literature | Literature: - Biomaterials Science: An Introduction to Materials in Medicine, Ratner B.D. et al, 3rd Edition, 2013 - Comprehensive Biomaterials, Ducheyne P. et al., 1st Edition, 2011 (available online via ETH library) Handouts and references therin. | |||||

402-0674-00L | Physics in Medical Research: From Atoms to Cells | W | 6 credits | 2V + 1U | B. K. R. Müller | |

Abstract | Scanning probe and diffraction techniques allow studying activated atomic processes during early stages of epitaxial growth. For quantitative description, rate equation analysis, mean-field nucleation and scaling theories are applied on systems ranging from simple metallic to complex organic materials. The knowledge is expanded to optical and electronic properties as well as to proteins and cells. | |||||

Objective | The lecture series is motivated by an overview covering the skin of the crystals, roughness analysis, contact angle measurements, protein absorption/activity and monocyte behaviour. As the first step, real structures on clean surfaces including surface reconstructions and surface relaxations, defects in crystals are presented, before the preparation of clean metallic, semiconducting, oxidic and organic surfaces are introduced. The atomic processes on surfaces are activated by the increase of the substrate temperature. They can be studied using scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The combination with molecular beam epitaxy (MBE) allows determining the sizes of the critical nuclei and the other activated processes in a hierarchical fashion. The evolution of the surface morphology is characterized by the density and size distribution of the nanostructures that could be quantified by means of the rate equation analysis, the mean-field nucleation theory, as well as the scaling theory. The surface morphology is further characterized by defects and nanostructure's shapes, which are based on the strain relieving mechanisms and kinetic growth processes. High-resolution electron diffraction is complementary to scanning probe techniques and provides exact mean values. Some phenomena are quantitatively described by the kinematic theory and perfectly understood by means of the Ewald construction. Other phenomena need to be described by the more complex dynamical theory. Electron diffraction is not only associated with elastic scattering but also inelastic excitation mechanisms that reflect the electronic structure of the surfaces studied. Low-energy electrons lead to phonon and high-energy electrons to plasmon excitations. Both effects are perfectly described by dipole and impact scattering. Thin-films of rather complex organic materials are often quantitatively characterized by photons with a broad range of wavelengths from ultra-violet to infra-red light. Asymmetries and preferential orientations of the (anisotropic) molecules are verified using the optical dichroism and second harmonic generation measurements. Recently, ellipsometry has been introduced to on-line monitor film thickness, and roughness with sub-nanometer precision. These characterisation techniques are vital for optimising the preparation of medical implants. Cell-surface interactions are related to the cell adhesion and the contractile cellular forces. Physical means have been developed to quantify these interactions. Other physical techniques are introduced in cell biology, namely to count and sort cells, to study cell proliferation and metabolism and to determine the relation between cell morphology and function. X rays are more and more often used to characterise the human tissues down to the nanometer level. The combination of highly intense beams only some micrometers in diameter with scanning enables spatially resolved measurements and the determination of tissue's anisotropies of biopsies. |

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