Our MSc Biomedical Engineering program is organized into five trimesters each lasting 8 weeks (lectures plus exams). The first trimester offers introductory courses with the purpose of homogenizing (up to certain point) the different educational backgrounds of the attending students.
Each of the second, third and fourth trimester deals with one of the three major sectors of the program. The fifth trimester is devoted to the first level thesis and to business and innovation management seminars and projects.
The minimum total duration of the degree is 15 months (90 ECTS), which may be extended to 24 months for conducting a research grade thesis. In that case the degree credits increase to 120. The structure of our program's curriculum is summarized in the following table and the detailed syllabus is given further below.
1st TRIMESTER 20/9-20/11 Introductory Module
Cell biology and Physiology
SW and HW tools for Rapid Prototyping of Medical Devices
Measurement and Analysis of Bio-signals
Biofluid Mechanics and Cardiovascular Technology
Laser Physics and Medical Lasers
2nd TRIMESTER 26/11-12/2 Biofabrication, Molecular Diagnostics and Therapy Module
Biomaterials and Biofabrication
Tissue Engineering and Regenerative Medicine
Drug Development and Pharmaceutical Technology
Biosensors and Lab on-a-chip
Molecular Diagnostics and Precision Medicine
3rd TRIMESTER 26/2-16/4 Bio-Medical Imaging Module
γ-, X-ray, MRI and Ultrasound Imaging
Physics and Engineering of Medical Imaging Sensors
Bio-spectroscopy and Hyperspectral Imaging
Medical Image Analysis
4th TRIMESTER 1/5-20/6 Medical Information Systems Module
Artificial Intelligence and Medical Decision Support Systems
Medical Informatics and e-health
Medical Robotics and VR
Big Data Analytics in Medicine and Healthcare
5th TRIMESTER 20/9-20/11 Medical Product and Business Development Module
Seminars and Projects on
·Patent Drafting and IPR Management
·Design and Execution of Clinical Trials
·Ethics in Science and Medicine
ECTS of Courses :75
Application Grade Thesis 1/7-20/11 (compulsory)
Research Grade & Specialization Thesis 30/11-30/6 (optional)
1st Trimester Syllabus
ΒΜΕ1 Cell biology and Physiology
Evolution of life on earth for 3.5 billion years in a nutshell, Organization of genomic information, Methods in Molecular Biology, Flow of genomic information: DNA replication, Flow of genomic information: Transcription of DNA into RNA, Flow of genomic information: Translation of RNA into protein, Fundamentals of microscopy and cellular organization, Structure and function of eukaryotic cellular components, Epithelial organization, Methods in Developmental Biology, Principles of Animal Development, Applications of Developmental Biology in Biomedical Engineering, introduction to bone and tissue histology: major tissue classes and body systems, an overview of functional musculoskeletal and topographic anatomy
BME 2 Software and Hardware tools for rapid prototyping of medical devices
Introduction(System Input (sense), Decision (logic), System action (actuators)), Sensors (Accuracy, error, precision, Analogue, digital, Pressure, Temperature, Light, Gyro, Accelerometers, Biosensors, wearables e.t.c), Taking action (ON/OFF, Output analog signals, Control position, rotation) Decision making and logic (no Logic, Microcontrollers, Computers), Writing software (read, compute, decide, programming languages, Prototyping mechanical parts (additive manufacturing (3D printing), molding Subtractive manufacturing (Lathe, CNC), Modeling (COMSOL, Matlab), hands-on projects
BME 3 Measurement and analysis of bio signals
Essential mathematics, continuous and Discrete Signal and Systems: Periodic, aperiodic and impulse signals; Sampling theorem; Laplace, Fourier and z-transforms; transfer function, convolution, correlation. Noise detection and removal, segmentation, feature extraction for clinical diagnosis and classification. Origin, nature, and types of Bio signals, bio signals generated from brain and nervous system (e.g. EEG/MEG, ECG, EMG, MRI/fMRI, fNIRS), Sensors and equipment used to measure bio signals. Principles of sensing physiological parameters, types of transducers and their characteristics, Electrodes for bioelectric signals, Bioelectric signals and their characteristics. Biopotential Amplifiers Clinical Examples
BME4 Biofluid Mechanics and Cardiovascular Technology
Principals of Fluid and Solid Mechanics (Fluid Properties, Hydrostatics, Conservation Laws, Dimensional Analysis and Scaling, Flow in a Straight Tube, Special topics: Deformation and stress strain relationships, Boundary Layers, Mechanics of Materials, Thin and Thick - Walled Cylindrical Vessels, Viscoelasticity), Cardiovascular Structure and Function (Anatomy of the vasculature, the Heart, Systemic Circulation, Microcirculation, Vascular Disease),Blood Rheology (Blood Composition, Viscous Properties of Whole Blood and Plasma, Non-Newtonian Rheology of Blood), Blood Vessel Mechanics (Structural properties of the Vascular Wall and Elasticity, Stress and Strain of Blood Vessels, Residual Stress),Steady Unidirectional Flow (Bernoulli Equation, Rigid Tube Flow, Entrance Length). Time Varying Flow (Womersley solution, Windkessel model, Waves in Arteries, Hemodynamics and Atherosclerosis, Hemodynamics in Stenoses and Aneurysms), Special topics:Applications ofCardioVascular Technology in Arterial Disease and Computational Methods in Biofluid Mechanics
BME 5 Laser Physics and Medical Lasers
Fundamentals (Electromagnetic radiation, Power, Intensity, Polarization, Interference, Diffraction), Optical Systems (Introduction to Geometrical Optics, From lenses and mirrors to simple optical systems, Complex optical systems and ABCD matrix theory, Introduction to optical aberrations) Physics of Light matter interaction (Electronic states, Jablonski diagram, Radiative and non-radiative transitions, Fluorescence, phosphorescence, Absorption, single vs multi-photon), LASERs(Light Amplification by Stimulated emission of Radiation, Laser Cavity, Laser Types - Medical lasers),LASER tissue interaction .
2nd Trimester Syllabus
BME6: Biomaterials and Biofabrication
Kelly Velonia,Anna Mitraki,Emmanuel Stratakis,
Examples of biologically-inspired materials; Collagen-Gelatin-Elastin-Keratin; Silk, spider silks, mussel collagens, amyloid fibrils; Cellulose, starch, cotton; Biological composite materials: nacre, chitin, bones, teeth; Design principles of biomimetic materials; Synthetic Biomaterials; Polymer based biomaterials; Peptide based biomaterials; Ceramic based biomaterials; Metals; Naturally derived synthetic biomaterials; Principles of Biofabrication – Terminology; Subtractive manufacturing; Additive manufacturing; 3D printing; Bioprinting; Cell printing and patterning; Electrospinning; Microfluidic Technology
BME7: Tissue Engineering and Regenerative Medicine
Charalampos Pontikoglou,Maria Chatzinikolaidou, Anthi Ranella
Tissue engineering and regenerative medicine – Fundamentals and Applications - The use of omics for artificial tissue characterization; Introduction to Stem Cells; Bone, cartilage, dental tissue engineering; Cardiovascular tissue engineering; Mesenchymal stem cells (MSCs) & cell therapies with MSCs; Neural tissue engineering; Cellular adhesion and mechanotransduction mechanisms
ΒΜΕ8: Drug Development and Pharmaceutical Technology
Principles of Pharmacology-Pharmacodynamics; Structural-functional characterization of drug receptors and receptor-based drug design; Preclinical and clinical phases of drug development; Principles and prerequisites of drug delivery and disease diagnosis; Molecular, structural and functional characterization of drug delivery vehicles and diagnostic nanoprobes; Nanomedicine based on stimuli-sensitive systems. Introduction to theranostics; Therapeutic opportunities using endogenous stem cells and human iPSCs on neurodegenerative diseases; 3D scaffolds and organ-on-a-chip platforms for drug screening and disease modelling
ΒΜΕ9: Biosensors and Lab on-a-chip
Overview of BioMEMS technology: Fundamentals and Applications; BioMEMS Materials and Microfabrication; Theory and Design; BioMEMS Sensors and Actuators and their in vivo applications; Optical, electrochemical and acoustic biosensors; Applications in immune- and molecular diagnostics
ΒΜΕ10: Molecular Diagnostics and Precision Medicine
Helen Papadaki,Nektarios Tavernarakis, Sofia Agelaki, Michail Klontzas
The basics of -omics technologies, part A (genomics, transcriptomics, proteomics, metabolomics); Gene modulation technologies (for example gene therapy, CRISPR-Cas9) in disease treatment in the context of precision medicine; Precision Medicine and Haematology; Omics applications on cellular therapies; Molecular diagnostics and Precision Oncology; Liquid biopsy as a tool in precision diagnosis and therapy of cancer; Effective implementation of novel technologies and precision medicine approaches in clinical practice.
3d Trimester Syllabus
Biomedical Engineering MSc Programme2020-2021
BME 11: Radiology, Molecular Imaging, Radiation Protection, Radiotherapy
History of Radiology; General overview of ionizing and non ionizing radiations and their interactions with matter; Ionizing radiation beam properties; Qualitative measurements on ionizing radiation photon beams; Basic Physics of Nuclear Medicine; Diagnostic clinical applications of conventional γ-camera imaging in various diseases; Production of X-rays; Scatter removal Grids and film Intensifying screens/plates; Radiation protection for workers and patients; Basic Physics and Principles of Computed Tomography (CT); CT appearance of normal tissues; State-of-the-art CT clinical applications; The physical basis of Nuclear Magnetic Resonance (NMR) phenomenon; The introduction of Gradient fields and their effect on spatial localization of the NMR signal; The MRI system as a clinical imaging device; Image post-processing in MRI; MRI safety; MR imaging of normal tissues; MR maging in various diseases; Basic Physics of Positron Emission Tomography (PET); The Evolution from PET to PET/CT and PET/MR Scanners; Clinical and pre-clinical applications of hybrid PET/CT and PET/MRI systems in oncology, cardiology, and in infectious and inflammatory diseases; Definition of Ultrasonography; Properties of Ultrasound; Medical applications; The ICRP System of Radiological Protection; Stochastic and deterministic effects; Risk models; Radiation-induced cancer risk; Radiation therapy introduction; Brachytherapy.
BME 12: Physics and Engineering of Medical Imaging Sensors
Crystalline structures, periodic potentials, energy bands in semiconductors, intrinsic extrinsic semiconductors, current carriers in semiconductors, metals, insulators, semiconductors, elements of semiconductor devices: diodes and transistors, photodetectors (photoelectric and thermal detectors), photoelectric effect, photoconductivity , material design: intrinsic, extrinsic, heterojunction photodetectors, photodiodes and phototransistors, photovoltaic, photoconductive and avalanche mode of operation, band gap and spectral response, responsivity, quantum efficiency, noise types, time response, photodiode circuits, medical uses in spectroscopy, dosimetry etc, CCD silicon imaging sensors, charge storage and transfer, charge to voltage conversion read our circuits, noise, full well capacity and dynamic range, CMOS silicon imaging sensors, correlated sampling, color imaging with silicon detector, infrared photon and thermal cameras, X-ray cameras, performance evaluation-Modulation Transfer Function (MTF) of imaging systems, digital X-ray imaging systems: technology and quality assessment.
BME 13: Bio-spectroscopy and Hyperspectral Imaging
Light and matter interaction, Spectroscopy basic principles, Elastic scattering, vibrational molecular spectroscopy, harmonic oscillator and diatomic molecules, X-ray diffraction (XRD), absorption (UV-Vis), Beer - Lambert law, Vibrational Spectroscopic methods, Raman/SERS, FT-IR, Electron Microscopy (SEM-TEM) Nuclear Magnetic Resonance (NMR), Jablonski diagram, Fluorescence (one-photon LIF), Confocal microscopy, Multi-photon spectroscopy Coherent Antistokes Raman Scattering, Stimulated Raman scattering, Super Resolution (STED), Biomedical Applications, Optical Spectroscopy (LIF, Raman, pulse oximeter, FT-IR), Confocal microscopy, Non-linear Nanosurgery CARS-SRS-STED, Merging imaging with spectroscopy, multi-and hyper-spectral imaging, Scanning hyperspectral imaging (HSI) technologies, Snapshot HSI technologies and artificial spectral vision, Calibration of HSI and acquisition of spectral cube data, spectroscopy-based contrast enhancement, Unsupervised and supervised of HSI data-spectral maps and chemical imaging, Training of HSI systems in clinical setting, machine learning/AI and HSI-the optical biopsy concept, Perspectives of HSI in medical device industry a) bright field and fluorescence microscopy/histology b) dermoscopy, endoscopy, colposcopy, c) HSI and contrast-agent pharmacokinetic monitoring.
BME 14: Advanced Microscopy
Introduction to optical biomedical imaging, Optical methods for functional investigation in biology and medicine, Multiscale imaging, from molecules to cells to humans, Image formation; hardware based vs model based; tomography, Light-matter interactions; light propagation in tissue, Imaging regimes; the transport mean free path; depth-to-resolution ratio, Optical microscopy in biomedical engineering, Introduction to optical microscopy Historical review; Biological observations with first microscopes from 17th to 19th century, When did the modern optical microscope emerge? Ray-tracing rules and application in the microscope.Types of optical microscopes, Diffraction and Huygens principle; Abbe’s limit of the spatial resolution in optical microscopy, Basic optical aberrations of a microscope, Objective lens and its characteristics, Introduction to fluorescence Widefield fluorescence microscopy, Optical filters and wavelength/spectral separation, Principles of confocal microscopy, Spatial resolution in fluorescence microscopy, Nonlinear and Coherent Anti-Stokes Raman Scattering (CARS) Basic scatterers and absorbers of biological tissue in the visible spectrum, Light scattering inside biological tissue; biological window, Two-photon excited fluorescence; differences with one-photon excited fluorescence, Advantages of non-linear microscopy vs. confocal, Optical harmonics, generation conditions and information they provide Raman scattering; Differences with Rayleigh scattering; significance for biology. Advantages and disadvantages of Raman microscopy, Coherent Raman Scattering microscopy and its advantages, photoacoustic microscopy, the photoacoustic effect, Types of photoacoustic microscopy and their differences, Spatial resolution and depth of imaging, Advantages over standard optical microscopy techniques, Linear spectral unmixing of absorbers, Applications of photoacoustic microscopy in basic research, Diffraction and spatial resolution of a microscope. Processes of light – matter interaction, how is a doughnut beam formed? Principle of operation of STED microscopy, STED spatial resolution, Current limits of Nanoscopy, Computational and wavefront shaping microscopy, Breaking the depth-to-resolution limit.
BME 15: Medical Image analysis
Introduction to Image Processing; Image formation, digital representation and image file formats, DICOM format; Filtering in the spatial domain: Image enhancement, sharpening, smoothing and point processing, Filtering in in the frequency domain: convolution theorem, filter design and restoration, Medical Image Segmentation, Medical Image Registration and data fusion, Texture and Shape Analysis: Radiomics and Radiogenomics applications, Deep Learning Applications in Segmentation and Classification, Modality specific computing: CT, DCE MRI, Diffusion MRI, Functional MRI; Introduction to medical Image Analysis with Matlab/Octave/Python.
4th Trimester Syllabus
Biomedical Engineering MSc Programme 2020-2021
BME 16: Artificial Intelligence and Medical Decision Support Systems
The course will introduce the students to the basic concepts of Artificial Intelligence (AI) and Machine Learning (ML); will present to the students state-of-the-art applications of AI and ML in medicine and healthcare, focusing on decision-support systems for disease diagnosis and treatment; will develop the students’ interest for such systems, and for AI and ML in general.What is AI; History of AI; Symbolic AI and Expert Systems; Statistical AI. Data-driven AI; The Intelligent Agent paradigm; AI for medicine: an overview; Intelligent Search methods. Informed Search and Uninformed Search; Constraint Satisfaction Problems; Applications in the Medical Domain; Logic: propositional logic and first-order logic; Logical Inference; Decision-making under uncertainty: Bayesian reasoning, Markov Decision Processes; Expert Systems for Medical Decision Support and Assistive Technologies (symbolic AI-based expert systems, Bayesian systems, hybrid approaches); Machine Learning: Neural Networks, Deep Learning, and their applications in medicine and healthcare.
BME 17: Bio-Informatics
Modern biology, both molecular and evolutionary, is virtually impossible without computational methods. The amount of biological data, obtained from re-sequencing projects, genomics, gene expression, or phylogenetics require specialized software for data handling and analysis. Students will learn basic programming using the statistical language R; be able to handle modern datasets; perform common statistical analyses such as hypothesis testing, detection of differential expressed genes etc.; be able to use databases such as Gene Expression Omnibus (GEO) to download publicly available datasets; basic usage of command line and terminal in Linux; acquire knowledge of specific dataset such as Next Generation Sequencing Datasets and get familiar with basic tools used to analyze them.Introduction to biology for bioinformatics; General presentation of R programming language and R-studio; Microarray datasets; Data analysis for microarray datasets; NGS datasets; Linux in Bioinformatics
BME 18: Medical Informatics and eHealth
The application of information and communications technology (ICT) in health care has grown exponentially over the last 30 years and there's strong evidence about its potential to improve effectiveness and efficiency in the medical domain. In this course we therefore overview the ICT landscape in health care and provide examples of these technologies for medical, mobile, and remote health applications. The objective of the course is to give students an introduction to the health care informatics, the systems and the interoperability standards for implementing biomedical applications.Introduction to Health care Information: Clinical, Administrative, Patient specific, Aggregate; Patient Records: Electronic Medical Records (EMRs), Electronic Health Records (EHRs), Personal Health Records (PHRs); ICT in Healthcare and standardization: PACS-Based Multimedia Imaging Informatics, Medical Imaging: the DICOM standard; Vocabularies, Thesauri, and Healthcare Terminology Standards: SNOMED-CT, ATC, LOINC, ICD; Messaging and Document Exchange:HL7v2, CDA, and HL7 FHIR; Healthcare interoperability: IHE integration profiles; Advanced and emerging use of clinical information systems: Mobile Health (mHealth), Internet of Medical Things (IOMT), Information exchange across borders, Security, privacy, and confidentiality.
BME 19: Medical Robotics and VR
The course will familiarize students with the AI and robotic technologies that revolutionize the field of healthcare. Students will learn the latest in emerging technologies for minimal invasive surgery, telesurgery, telementoring and telemedicine; will introduce the students in the basic principles of Robotic Assistive Surgery, teleoperation, and VR medical training systems; will develop students’ interest in the latest emerging technologies of AI and automation for medical provision.Introduction to Medical Robotics; VR, telementoring and remote training; Medical Modeling, Simulation & Visualization (MMSV); Structure and function of medical robots; Robotic Assistive Minimally Invasive Surgery (MIS); Haptic Feedback and Visual Servoing; Perceptual Docking in MIS; A.I. for Telemedicine
BME 20: Big Data Analytics in Medicine and Health Care
The course will introduce the students to the basic concepts of big data analytics in medicine and health care. Students will learn how to collect, store and transform heterogeneous healthcare data, exploiting medical ontologies and terminologies for semantically uplifting them. Then they will learn how to efficiently and effectively process them through big data frameworks, enabling predictive modeling and computational phenotyping. In addition they will understand how graph analytics can be used to visualize and interpret data and effectively communicate results and findings. The course will familiarize students with the current procedures and requirements for processing big healthcare data and for the available technologies; will develop the students’ interest in personalized medicine through big data analytics and to help them understand the challenges and the opportunities in the area.Intro of Big Data Analytics in Medicine and Health Care/Course overview; Collecting, Storing and Transforming Healthcare Data; Medical Ontologies & Terminologies; Big Data Frameworks; Predictive modeling; Computational Phenotyping; Graph Analytics and Visualization; Opportunities and Challenges in Health Data analytics.