Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie:
- 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)
I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati. - 9-12 crediti ECTS in basi teoriche ampliate (FTP)
I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione - 6-9 crediti ECTS in moduli di contesto (CM)
I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.
La descrizione del modulo (scarica il pdf) riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:
- Insegnamento
- Documentazione
- Esame
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.
Requisiti
Engineering mathematics (algebra, calculus, numerical methods), physics, electricity of BSc engineering programs or similar (including systems and signals, Fourier transform)
Obiettivi di apprendimento
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
Contenuti del modulo
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
Metodologie di insegnamento e apprendimento
Presentations, Excersises and Labs
Bibliografia
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.
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