MSE Master of Science in Engineering

The Swiss engineering master's degree

Jedes Modul umfasst 3 ECTS. Sie wählen insgesamt 10 Module/30 ECTS in den folgenden Modulkategorien:

  • ​​​​12-15 ECTS in Technisch-wissenschaftlichen Modulen (TSM)
    TSM-Module vermitteln Ihnen profilspezifische Fachkompetenz und ergänzen die dezentralen Vertiefungsmodule.
  • 9-12 ECTS in Erweiterten theoretischen Grundlagen (FTP)
    FTP-Module behandeln theoretische Grundlagen wie die höhere Mathematik, Physik, Informationstheorie, Chemie usw. Sie erweitern Ihre abstrakte, wissenschaftliche Tiefe und tragen dazu bei, den für die Innovation wichtigen Bogen zwischen Abstraktion und Anwendung spannen zu können.
  • 6-9 ECTS in Kontextmodulen (CM)
    CM-Module vermitteln Ihnen Zusatzkompetenzen aus Bereichen wie Technologiemanagement, Betriebswirtschaft, Kommunikation, Projektmanagement, Patentrecht, Vertragsrecht usw.

In der Modulbeschreibung (siehe: Herunterladen der vollständigen Modulbeschreibung) finden Sie die kompletten Sprachangaben je Modul, unterteilt in die folgenden Kategorien:

  • Unterricht
  • Dokumentation
  • Prüfung
Medical Diagnostics & Devices (TSM_MedDD)

This module gives an introduction to the physical and technical principles and applications of common diagnostic modalities. Starting with an overview of clinically used modalities and their applications, technical requirements and limitations based on the fundamental principles will be discussed. Furthermore, efficient methods for biomedical signal processing and analysis are introduced.

Eintrittskompetenzen

Engineering mathematics (algebra, calculus, numerical methods), physics, electricity of BSc engineering programs or similar (including systems and signals, Fourier transform)

Lernziele

Upon completition of the module, the student will be able to

  • gain knowledge in fundamentals of chemical, biological and physical sensors
  • achieve basic knowledge in the design of sensor systems
  • apply sensors and systems in medical diagnostics
  • apply signal processing methods on biosignals for diagnostic purposes
  • achieve basic signal processing skills to perform artifact removal, feature extraction, and classification on biological signals.
  • explain fundamental principle of medical imaging modalities (XR, CT, MRI, Ultrasound)
  • achieve basic knowledge of the most important clinical application of medical imaging modalities
  • describe approaches and methods for image reconstruction and quality assessment

Modulinhalt

Part 1 Devices & Sensors: Overview of diagnostic instrumentation and modalities:

Principles of Ultrasound and MRI

Generation of X-ray, X-ray detectors, technology and application of Fluoroscopy, CT

Image quality, radiation protection and QA for diagnostic devices

Part 2 Signal processing

Chemical, biological, and physical sensors, design requirements for sensors and devices in diagnostics, sensor application in medical diagnostics (ECG, EEG, EMG, optical pulsoxymetrie, (Blood)pressure, flow sensor, otoacoustic emission (OAE), etc.)

Measurement in medical diagnostics, amplifier, signal conversion and quantization

Standard methods for biomedical signal processing and analysis, Imaging processing

  •  Background on time- and frequency-domain characteristics of particular biosignals and common artifacts
  •   Techniques for artefact removal, event detection, feature extraction, pattern recognition, classification

Lehr- und Lernmethoden

Presentations, Excersises and Labs

 

Bibliografie

Dance

DR, Christofides S, Maidment ADA, McLean ID, Ng

KH (Eds): Diagnostic

Radiology

Physics.

Vienna, 2014: IAEA, ISBN 978-92-0-131010-1

Oppelt A (Ed.): Imaging Systems for Medical Diagnostics. Siemens, Publicis Corporate Publishing, Erlangen; ISBN 3-89578-226-2

John D. Enderle, Joseph D. Bronzino, Introduction to Biomedical Engineering, Academic Press

R. A. Wildhaber et al., "Signal detection and discrimination for medical devices using windowed state space filters," 2017 13th IASTED International Conference on Biomedical Engineering (BioMed), Innsbruck, Austria, 2017, pp. 125-133.

R. A. Wildhaber et al., "Windowed State-Space Filters for Signal Detection and Separation," in IEEE Transactions on Signal Processing, vol. 66, no. 14, pp. 3768-3783, 15 July15, 2018.

Vollständige Modulbeschreibung herunterladen

Zurück