Module compendium

Transcrição

Module compendium

Fakultät Life Sciences
Module compendium
Master degree programme
Biomedical Engineering / Medizintechnik
www.haw-hamburg.de
-- this page is intentionally left blank-------------------------
Friedrich Ueberle, Jürgen Lorenz, (Hrsg.)
Module compendium
Master degree programme
Biomedical Engineering/Medizintechnik
Fakultät Life Sciences
Department Medizintechnik
January 2011
Department Medizintechnik / Fakultät Life Sciences
Hochschule für Angewandte Wissenschaften Hamburg
Lohbrügger Kirchstraße 65, 21033 Hamburg
Tel.: +49.40.428 75-6162, Fax: +49.40.428 75-6149
www.haw-hamburg.de
-- this page is intentionally left blank -------------------------
4
January 2011
Table of contents
Introduction ............................................................................................................................ 6 Module and course structure ................................................................................................ 7 Module descriptions .............................................................................................................. 8 Applied Mathematics................................................................................................................ 8 Data Acquisition and Processing ........................................................................................... 10 Microsystems and Robotics ................................................................................................... 12 Therapy Technologies ........................................................................................................... 14 Medical Data Systems ........................................................................................................... 16 Advanced Biosignal Processing............................................................................................. 18 Medical Image Processing ..................................................................................................... 20 Management of Medical Technologies .................................................................................. 22 Biomedical Project ................................................................................................................. 24 Master-Thesis ........................................................................................................................ 26 Lecturers............................................................................................................................... 28 5
January 2011
Introduction
This module compendium describes the study Biomedical Engineering / Medizintechnik of the
Hamburg University of Applied Sciences. It contains a tabular overview of the modules and
courses and a detailed description of each module.
The full time study is designed for 3 semesters (1½ years). The graduate-level courses cover
mathematical, scientific and engineering knowledge focused on the field of biomedical engineering,
a broad view on advanced topics of modern therapy and diagnostic technologies, and advanced
knowledge of the management of biomedical technologies. Many courses include practical work
with up-to-date software tools and hardware, e.g. in our research labs or at the hospital sites of our
collaboration partners. In a mandatory scientific project the students are engaged in autonomous
scientific studies in small groups. Enhanced soft skills are acquired in the seminars and during the
lectures by intense discussions and small projects like preparation of presentations, posters and
papers. In the master thesis, the students demonstrate their ability for autonomous scientific work
at graduate level. The courses are held in English language.
The degree “Master of Science” is a second degree qualifying for a profession. It entitles its holder
to exercise professional work in the field of biomedical engineering.
Graduates have the ability to independently acquire new fields of knowledge and to solve complex
problems, even above the current state of knowledge, using scientific methods. Graduates are capable to isolate problems in a new area of expertise or such an area in development and to encircle the most probable solution approach. They have the continuing ability of composing new technical solutions and new process strategies in the field of biomedical engineering and to transfer
these solutions into clinical use or in industrial production. They can work both in the areas of production or development of a company, in technical functions in a hospital or in research institutes.
They are qualified to join a doctorate program subsequently of this program.
Additional regulations are the study and examination regulations (Studien- und Prüfungsordnung)
of the Hamburg University of Applied Sciences (HAW) and the Faculty of Life Sciences. The regulations and further information are accessible at the HAW websites, in particular at the sites of the
Faculty of Life Sciences, Department Medizintechnik.
6
January 2011
Module and course structure
Presence
Module
1
Applied Mathematics
CP
Course
Type
SWS
5
Numerical Mathematics
Data Acquisition and
Processing
sU
3
2
Data Acquisition and Processing
5
3
Microsystems and Robotics
7
5
6
7
8
Therapy Technologies
Medical Data Systems
Advanced Biosignal Processing
Medical Image Processing
Management of Medical
5
1.5
sU
1.5
Robotics Seminar
Therapy Technologies 1
Sem.
2
4
sU
3
sU
2
4
8
7
Technologies
9
Biomedical Project
15
Biosignal Processing
Signal Processing in
imaging modalities
Medical Image
Processing
Image Processing in
Therapy and Diagnostic
Market Perspectives
Assessment
Methodology
30
sU
sU
sU
sU
sU
sU
sU
Module Seminar
Sem.
Scientific Project
Proj.
Sem.
Research Seminar
10 Masterthesis
sU
Masterthesis
Semester
Mastersemester
Grade
fraction
PL: K,M,R,H
SS&WS
1
5,5%
PL: K,M,R,H
SS&WS
1
PL: K,M,R,H
WS
1 or 2
PL: K,M,R,H
WS
1 or 2
SL
WS
1 or 2
PL: K,M,R,H
WS
1 or 2
PL: K,M,R,H
WS
1 or 2
PL: K,M,R,H
WS
1 or 2
PL: K,M,R,H
SS
1 or 2
4,5%
PL: K,M,R,H
SS
1 or 2
4,5%
PL: K,M,R,H
SS
1 or 2
PL: K,M,R,H
SS
1 or 2
PL: K,M,R,H
SS
1 or 2
PL: K,M,R,H
SS&WS
1
PL: K,M,R,H
SS&WS
1
SL
SS&WS
1
PL: R,P
SS&WS
2
SL
SS&WS
2
Thesis
SS&WS
3
3
sU
Microsystems
Robotics
4
sU
Grading:
Exam type
1.5
5,5%
7,5%
5,5%
1
1.5
2
4
8,5%
2
1
7,5%
2
2
2
16,0%
35,0%
Legend:
SWS = Presence hours per week during semester
Course type: sU = seminaristic teaching, Proj. = Project, Sem. = Seminar (>80% presence obligatory)
Prüfungsart: SL = Test (not graded), PL = Exam (graded)
Exam type: K = written exam, M = oral exam / presentation, R = seminar paper , H = homework, P = Project
documentation/poster
7
January 2011
Module descriptions
Biomedical Engineering (Master)
Degree Course
Module code digit:
01
Applied Mathematics
Module coordination/
responsible person
Prof. Dr. Kay Förger
Lecturer
Prof. Dr. Anna Rodenhausen, Prof. Dr. Tomas Schiemann
Period / Semester/ Offer of
this turnus
One Semester / summer and winter semester / twice a year
ECTS Credits/Presence
hours per week
5 CP / 3 SWS
Workload
150 h: 48 h presence, 102 h private studies
Status
Obligatory module
Preconditions / Required
skills
None / Basic skills in programming and mathematics (e.g. acquired in a
bachelor degree programme)
Max. number of participants)
Lab (practical) part of course: 16 per group
Teaching language
English
Lectures: 40
Acquired competences / educational objectives
Expertise and methodological competences
The students are able to use the computer as universal tool to solve practical problems:

On the one hand complex simulations can be performed by LabVIEW with little effort and

on the other hand data can be acquired and processed with a computer easily.
Data and signals are simulated to make the theoretical relations understandable and better applicable.
The students are able to …
 apply a wide range of numerical methods

understand the fundamental principals of the discussed methods

implement and visualize problems from numerical mathematics in the MATLAB environment
Personal and interpersonal skills
The students are able to



keep one’s distance to their results and especially to their own programs,
recognize the must of software tests using simulations with results which are known in advance to
assess the extent of tests for methods and procedures more precisely
develop solutions for a given data acquisition project using the methods presented
Learning matter






Introduction to MATLAB
Numerical solution of linear equation systems
Curve fitting and interpolation
Optimization
Non-linear zero problems
Numerical differentiation
8
January 2011


Numerical integration
Numerical solution of ordinary differential equations
Associated courses / ECTS credits / presence hours per week
Numerical Mathematics:
5 CP / 3 SWS
Teaching methods / methods
generally / types of media
The course is split into a lecture part and a practical part which last
approximately the same amount of time.
Lecture part: Mainly presented in form of a seminaristic lectures, i.e. with
student interaction to discuss and present different solutions, results and
programming approaches by demonstrating the usage of software tools
directly. Additional exercises are to be solved by the students to improve
their comprehension.
Lab (practical) part: Solution of prepared exercises during the attendance.
To difficulties and misunderstood issues is responded by mentoring
individually. Selected solutions were presented to the study group.
Course- and examination
achievements
A graded (written or oral) exam at the end of the semester.
Literature / working materials
S.C. Chapra: Numerical Methods for Engineers, McGraw-Hill, 2005
M. Hanke-Bourgeois: Grundlagen der Numerischen Mathematik und des
wissenschaftlichen Rechnens, Teubner 2002
J. Kahlert: Simulation technischer Systeme, Vieweg, 2004
G. Lindfield, J.Penny: Numerical Methods using MATLAB, Hertfordshire,
1995
J. H. Mathews, K.D. Fink: Numerical Methods using MATLAB, Prentice
Hall, 2004
R. Mohr: Numerische Methoden in der Technik – Ein Lehrbuch mit
MATLAB Routinen, Vieweg, 2000
9
January 2011
Degree Course
Module code digit:
02
Data Acquisition and Processing
responsible person
Lecturer
this turnus
One Semester / summer and winter semester / twice a year
hours per week
5 CP / 3 SWS
Workload
150 h: 48 h presence, 102 h private studies
Status
Obligatory module
skills
None / Basic skills in programming and mathematics (e.g. acquired in a
bachelor degree programme)
Lab (practical) part of course: 16 per group
Teaching language
English
Lectures: 40
The students are able to use the computer as universal tool to solve practical problems:

on the one hand complex simulations can be performed by LabVIEW with little effort and

on the other hand data can be acquired and processed with a computer easily.
Data and signals are simulated to make the theoretical relations understandable and better applicable.
 to apply statistical methods and

to test the developed evaluation methods by simulation to get more reliable programs.

Especially by such an approach subtle programming errors become obvious, which otherwise could be
found hardly but distort the results much. That sensitizes students especially to such errors.

Additionally the students are enabled by computer simulations to analyze measurement and processing
techniques (signal sampling, averaging, statistical tests etc.) if some restrictive mathematical
prerequisites (e.g. sampling theorem, normal distribution of random variables) are not exactly met in
practical problems. Methods which provide reliable results in such cases are highlighted as robust
procedures.
 to look for robust procedures / techniques.
In practical applications the parallel acquisition and processing of measurands and the simultaneous reaction on
user input is an essential requirement, which is difficult to understand and implement in text based programming
languages. On the contrary the graphical programming environment of LabVIEW enables the students to
 design programs with parallel execution and synchronization which are easy to implement and
understand.

acquire and process data from real experiments correctly and scientifically founded.
The students are able to

keep one’s distance to their results and especially to their own programs,
10
January 2011


recognize the must of software tests using simulations with results which are known in advance to
assess the extent of tests for methods and procedures more precisely
develop solutions for a given data acquisition project using the methods presented
Learning matter



Introduction to LabVIEW programming,
statistical evaluation of measured data
- basic statistical quantities (mean, variance and standard error, median etc.)
- hypothesis tests
- parameter estimation
acquisition and processing
- Fourier Transform und series: basics, examples and discretization
- Sampling Theorem: Aliasing, smoothing Windows etc.
- Digital Filters: linear filters (FIR and IIR)
Data Acquisition and Processing:
5 CP / 3 SWS
The course is split into a lecture part and a practical part which last
approximately the same amount of time.
Lecture part: Mainly presented in form of a seminaristic lectures, i.e. with
student interaction to discuss and present different solutions, results and
programming approaches by demonstrating the usage of software tools
directly. Additional exercises are to be solved by the students to improve
their comprehension.
Lab (practical) part: Solution of prepared exercises during the attendance.
To difficulties and misunderstood issues is responded by mentoring
individually. Selected solutions were presented to the study group.
achievements
A graded (written or oral) exam at the end of the semester.
W. H. Press et al.: Numerical recipes in C, Cambridge University Press,
New York, 1998
I.N. Bronstein, K.A. Semendyayev et al.: Handbook of Mathematics, 4th
Ed. Springer, Berlin Heidelberg, 2004
R. Jamal, H. Pichlik: LabVIEW Applications, Prentice Hall , 1998
LabView User Manual, National Instruments, January 1998
R.W. Hamming: Digital Filters, Englewood Cliffs, New Jersey, 1983
P.Profos, T. Pfeifer: Grundlagen der Meßtechnik, Oldenburg Verlag,
München, 1997
11
January 2011
Degree Course
Module code digit:
03
Microsystems and Robotics
responsible person
Prof. Dr. Friedrich Ueberle
Lecturer
Prof.. Dr. Boris Tolg, Prof. Dr. Holger Mühlberger, Prof. Dr. Friedrich
Ueberle, assistant lecturers with background in science, hospital or
industry
this turnus
One Semester / winter semester / once a year
hours per week
7 CP / 5 SWS
Workload
210 h: 80 h presence, 130 h private study
Status
Obligatory module
skills
None / Appropriate knowledge from previous academic studies in a
professionally associated field, bachelor degree: mathematics,
informatics, electronics, physics, mechanics, project management
20
Teaching language
English
The courses of this module enable the student to...
 solve demanding scientific and engineering problems
 know basic and advanced theoretical and practical concepts of Microsystems technology and Robotics.
 assess the potential of robotics and microsystems concepts, patents and publications and to
discuss and express their views and findings in presentations, meetings and in writing.
 gain practical experience with robots and develop simple applications of robots and sensors.
 understand relevant literature and implement the knowledge in innovative designs.
 critically read, understand and review original articles and working documents
 present and discuss their concepts in a peer group and to sponsors
 develop solutions for a given project using robotics and Microsystems sensors write a “peer review”-like
evaluation report of a published paper
 demonstrate their skills in developing / managing / working in a project with other students in order to
reach a given goal in a short time frame and with restricted resources
Learning matter
Robotics course:

Basics of robotics, robot arms, effectors, robot concepts, sensors, drives and gears, work spaces
mathematical methods (e.g. coordinate transforms), algorithms


Applications of robots in industry, medicine, laboratory and personal (health)care and rehabilitation
Autonomous robots and telemanipulators
12
January 2011
Microsystems course:


Introduction to microsystems, sensors, actors, basic materials and technologies
Principles of the construction and development of MEMS-Technologies, production methods and
technologies
Robotics Seminar:

Practical basics of robotics, concept and realization of a robotic project based on available kits and
proprietary developments (E.g.: Distribution and sorting of medication with autonomous mobile robots)

Conception, assembling and testing of adequate sensors (Z.B. camera based sensors, distance sensors
etc.)



Programming of the robots (C language, AVR studio or similar)
Implementation., launching and demonstration oft he project
Project planning, project management, project documentation
Microsystems:
2 CP / 1.5 SWS
Robotics:
2 CP / 1.5 SWS
Robotics Seminar
3 CP / 2 SWS
(Teaching methods / methods
generally / types of media)
Seminaristic lectures, labs, expert puzzle, teamwork, distance learning
elements, web-based cooperation, autonomous studies / Power Point,
blackboard, overhead projection, multimedia, software
achievements
Microsystems, Robotics: Graded examinations: oral presentation, written
reports, colloquium, written exams, oral exams (subject to examiners
choice),
Robotics Seminar: not graded (Studienleistung), Participation in at least
80% of the presence hours required, project presentation required
Microsystems:
 Ulrich Mescheder: Mikrosystemtechnik, Teubner, Stuttgart, 2000
 Marc J. Madou: Fundamentals of microfabrication, 2nd ed., CRC press,
Boca Raton, 2002
 Thomas Elbel, Mikrosensorik, Studium Technik, Vieweg 1996
Robotics:

H.R. Everett: Sensors for mobile robots, AK Peters ltd., Natick, 1995

Tsavetas: Robotic applications in rehabilitation (2001)

Labs, Zippel (Hrg.) Stand und Perspektiven der Computer-assistierten
Chirurgie, Uni-Med, Bremen 2002, Med M12-28.1

Handbook of Robotics, Springer (2008)

Hans-Jürgen Siegert, Siegfried Bocionek, Robotik: Programmierung
intelligenter Roboter, Springer, Berlin, 1996

Robin Gruber, Jan Grewe: Mehr Spaß mit Asuro Band I, Arexx, 2005

Robin Gruber, Jan Grewe: Mehr Spaß mit Asuro Band 2, Arexx, 2007

Alexander Bierbaum, Alexander Piaseczki, Joachim Schröder,Pedram
Azad, Tilo Gockel, Rüdiger Dillmann: Embedded Robotics-Das
Praxisbuch, 2007
Robotics seminar (Materials supplied in the lab):



Commercial robot kits, software (AVR compiler, C)
Sensors, Construction materials, tools, PC’s, software (LabView)
Access to workshop, tools, hardware
13
January 2011
Degree Course
Module code digit:
04
Therapy Technologies
responsible person
Lecturer
Prof. Dr. Friedrich Ueberle, Dr. med. priv. Doz. Fehlauer, Dr. Wolfgang
Wöllmer, assistant lecturers with background in science, hospitals or
industry
this turnus
One Semester / winter semester / once a year
hours per week
5 CP / 4 SWS
Workload
Status
Obligatory module
skills
professionally associated field, bachelor degree: informatics,
electronics, physics, mechanics, human biology
20
Teaching language
English





solve demanding scientific and engineering problems
are familiar with at least two typical therapy technologies (see below) and their clinical applications.
are able to assess the potential and the use of new concepts, patents and publications in the field of
therapy devices and to discuss and express their views and findings in presentations, meetings and in
writing.
are able to describe the physical, technological and medical background of therapy devices.
understand relevant literature and implement the knowledge in innovative designs.
 present and discuss therapy device concepts in a peer group and in public
 autonomously handle technical and biomedical working materials (literature, documents, specifications
 describe theoretical concepts of biomedical therapy technology.
Learning matter
Courses Therapy Technologies – 1 …– 3:


Medical, technical and physical lectures on selected therapy technologies (e.g. lasers, electrotherapy,
radiaton therapy, lithotripsy, magnetic field therapy, technology of anaesthesia). Selection of topics is
given by the lecturers.
Practical demonstrations (Hospitations) if applicable
14
January 2011
Therapy Technologies – 1
2 CP / 1.5 SWS
1 CP / 1.0 SWS
2 CP / 1.5 SWS
(Courses may be pooled or split depending on the complexity of topics)
(Teaching methods / methods
generally / types of media)
achievements
Graded examinations per course: oral presentation, written reports,
colloquium, written exams or combinations (subject to examiners choice)

Kramme, Medizintechnik, Springer 2008, 3rd edition

Bronzino, Biomedical Engineering Handbook, 2002
Scientific papers and books (depending on the course topics)
15
January 2011
Degree Course
Module code digit:
05
responsible person
Prof. Dr. Jürgen Stettin
Lecturer
Prof. Dr. Jürgen Stettin, Prof. Dr. Margaritoff, assistant lecturer of
science and industry
this turnus
One Semester / each in the winter term
hours per week
4 CP / 3 SWS
Workload
120 h: 48 laboratory work, 72 h private study
Status
Obligatory module
Qualification for
participation/ Previous
knowledge
None / knowlegde in medical engineering, computer science
(programming) as well as human biology of the bachelor degree course
(e.g. medical engineering)
Max. number of participation
20
Teaching language
English
The students

can solve complex medical software related task including risk management for software

are familiar with software development methods, techniques and strategies from the medical engineering.

get knowledge on methods and procedures in medical data handling

can judge the input of new concepts, patents and publications and comment in presentations and
discussions.

have knowleges of scientific method-information and are able to evaluate the inputs of the specialist
literature and to turn them into their own designs.
The students
 are able to present specific data structure and principles in public publications.
 are able to deal with technical and medical working materials unaffiliated.
 can describe and impart theoretical relationships in the bio-medical context.
Learning matter
Medical Data Systems:



basic principles of medical documentation
Software abstracts for medical systems (risk analysis and – evaluation, usability)
Interfaces (HL7, DICOM, IHE)
4 CP / 3 SWS
16
January 2011
Semester lectures, practical work /expert puzzle, working groups, power
point, exercises, private study, table, beamer, software, e-learning
elements
achievements
Graded examinations: Academic record: seminar paper, presentation, oral
examination or colloquium (Subject to examiners choice)
Literature and learning aids
Scientific literature as advised by the lecturers during the course
17
January 2011
Biomedical Engineering Master
Degree Course
Module code digit:
06
Advanced Biosignal Processing
responsible person
Lecturer
Prof. Dr. Friedrich Ueberle, Dr. van Stevendaal, Dr. Ulrich Katscher, Dr.
Spies and other assistant lecturers with background in science, hospital
or industry
this turnus
One Semester / summer semester / once a year
hours per week
4 CP / 4 SWS
Workload
120 h: Presence: 64 h, 56 h private study
Status
Obligatory module
skills
professionally associated field, bachelor degree: mathematics,
informatics, systems theory, human biology
20
Teaching language
English
 solve demanding scientific and engineering problems
 know and apply advanced concepts of biomedical signals and systems and the processing of biomedical
signals (e.g. EEG, ECG, phonocardiogram, EMG, EOG)
 know and apply advanced methods of mathematical signals processing, e.g. in cardiac CT (Cone beam),
parallel MRI transmission and reception
 know and apply advanced algorithms for the extraction of functional parameters from biomedical signals,
(e.g independent component analysis - ICA, statistical parameter mapping - SPM)
 understand relevant literature and implement the knowledge in biomedical problems solving.
 critically read, understand and review original articles and working documents
 present and discuss their concepts in a peer group
 develop solutions for biomedical signals processing tasks
Learning matter
Biosignal Processing:
Topics (Choice depending on lecturers):




z-Transformation, FIR and IIR Filter
Signal analysis in phonocardiography
ECG signal processing
EEG signal processing
18
January 2011

Advanced Signal Processing of biomedical signals (e.g. ICA, fourier methods, wavelets)
Signal Processing in imaging modalities:
Topics (Choice depending on lecturers):


MRI: parallel transmission and reception
Fast cardiac CT (Cone beam reconstruction)
Biosignal Processing
2 CP / 2 SWS
Signal Processing in Imaging Modalities:
2 CP / 2 SWS
Courses can be split depending on the complexity of the topics
achievements
Graded examinations: oral presentation, written reports, colloquium, written
exams, oral exams (subject to examiners choice)
Girod/Rabenstein/Stenger: Einführung in die Systemtheorie, Teubner
Stuttgart 1997 / 2. Auflage 2005
Z.Z. Karu: Signals and Systems (made ridiculously simple), ZiziPress,
Huntsville 2001
J. Semmlow: Circuits, Signals and Systems for Bioengineers, Elsevier
Academic 2005
H. Hsu: Signals and Systems, Schaum‘s Outline, 1995
Rangaraj M. Rangayyan: Biomedical Signal Analysis: A Case-study
Approach (IEEE Press Series in Biomedical Engineering) Wiley & Sons
Januar 2002
Gail D. Baura: System Theory and Practical Applications of Biomedical
Signals (IEEE Press Series in Biomedical Engineering) Wiley & Sons 2002
R.W. Hamming: Digital Filters, Prentice Hall, Englewood Cliff. NJ, 1998
19
January 2011
Degree Course
Module code digit:
07
Medical Image Processing
responsible person
Prof. Dr. Thomas Schiemann
Lecturer
Prof. Dr. Thomas Schiemann, Prof. Dr. Friedrich Ueberle, Prof. Dr.
Cornelia Kober, and other assistant lecturers with background in
science, hospital or industry
this turnus
One Semester / summer semester / once a year
hours per week
8 CP / 6 SWS
Workload
Status
Obligatory module
skills
None / Students should have knowledge in electronics, biomedical
engineering, computer science (especially programming) and human
biology.
Max. number of participants
20
Teaching language
English
The students

are able to solve comprehensive problems in engineering and science

know the concepts of medical image processing

are able to describe and apply methods of computer-based image processing and image interpretation

know scientific methods and solutions and are therefore able to evaluate the content of scientific references
and apply these concepts in own software or concepts.

know methods of navigation and their medical application
The students
 are able to present and discuss problems and methods with other scientists

can work with technical and medical equipment in their own responsibility

are able to describe and explain theoretical concepts in the biomedical context
Learning matter
Medical Image Processing:

Basics of digital images and image processing

Histograms and point-based operations

Linear and non-linear filters and their applications (e.g. smoothing, edge-detection, extraction of structures)

Processing of color-images and video-data
20
January 2011

Geometrical manipulations and image registration

Programming of basic methods in ImageJ
Image processing in therapy and diagnostics:
Topics to be chosen by the lecturer:

Basics and technical realization of navigation in medicine

Imaging modalities and navigation

Image Guided Therapy

Reconstruction of 3D image-data form CT or MRI images

Extraction of functional data from CT / MRI / PET by SPSS
Medical Image Processing:
6 CP / 4 SWS
Image Processing in Therapy and Diagnostics: 2 CP / 2 SWS
Seminaristic lectures, practical courses, expert-puzzle, team-work
PowerPoint-presentation, tutorials, private study
blackboard, projector, software-demonstration
e-Learning
achievements
Graded examinations in each course: oral presentation, written reports,
colloquium, written exams, practical exams (subject to examiners choice).
Burger, Burge: Digital Image Processing. An Algorithmic Introduction Using
JAVA, Springer, 2008
Software ImageJ
Gail D. Baura: System Theory and Practical Applications of Biomedical
Signals (IEEE Press Series in Biomedical Engineering) Wiley & Sons 2002
SPSS Manual
21
January 2011
Degree Course
Module code digit:
08
Management of Medical Technologies
responsible person
Prof. Dr. Jürgen Lorenz
Lecturer
this turnus
One semester / 1st or 2nd semester /
each semester
hours per week
7 CP / 5 SWS
Workload
Status
Obligatory module
skills
Appropriate knowledge from previous academic studies in a
professionally associated field, bachelor degree
20
Teaching language
English
This course enables the student to

describe the basic strategy and procedures of Health Technology Assessment (HTA) based on the
general concept of evidence-based medicine

identify quality criteria of scientific publications (ethics, study design, statistical methods, outcome
measures, publication bias, journal impact etc.)

apply HTA both as a prospective and retrospective tool of quality assurance in the development and
evaluation of medical technologies.

retrieve and evaluate relevant information using internet-based data bases (PubMed, Medline, Cochrane
library etc.)

apply economical evaluation methods (cost/benefit-analysis) to healthy technologies.
The students will be able




critically read and review original articles
present and discuss their critique on a paper in a group (“journal club presentation”)
write a “peer review”-like evaluation report of a published paper
write and revise an own text contribution (“workpackage”) to a review paper prepared by the group
Learning matter
Health Technology Assessment:


basis and methodologies of evidence based medicine
National and international health technology assessment organizations
22
January 2011

Process of peer-reviewed scientific publication
New Medical Technologies (Market Perspectives):



Assessment of the market potential of biomedical technologies and methods
Health economy
Study design and evaluation



Market Perspectives (of Medical Technologies))
Assessment Methodology
Module Seminar (Leadership / Communication & Presentation / Statistics and
Advanced Methods)
achievements

Powerpoint presentations

Group work (internet retrieval, discussions)

Excursions (“expert interviews”)
2 CP / 1 SWS
3 CP / 2 SWS
2 CP / 2 SWS
New Medical Technologies / Health Technology Assessment:
Graded exams (Prüfungsleistungen): Oral presentation, written study
report, written exams (subject to examiners choice)
Module seminar: not graded (Studienleistung)





Introduction to health technology assessment. CS Goodmann. HTA
101, 2004.
Sterne JA, Egger M, Smith GD. Systematic reviews in health care:
investigating and dealing with publication and other biases in metaanalysis. BMJ. 2001;323:101-5.
Steinberg EP. Cost-effectiveness analyses. N Engl J Med.
1995;332:123.
Oxman AD, Sackett DL, Guyatt GH. Users' guides to the medical
literature. I. How to get started. JAMA. 1993;270(17):2093-5.
Guyatt GH, Haynes RB, Jaeschke RZ, et al. Users’ guide to the
medical literature, XXV: Evidence-based medicine: principles for
applying the users’ guides to patient care. Evidence-Based Medicine
Working Group. JAMA. 2000;284:1290-6.
23
January 2011
Degree Course
Module code digit : 09
Biomedical Project
Resposible person
Lecturer
All university lecturers of the department MT, Prof. Dr. Ueberle
this turnus
One semester / 2nd semester / each semester
hours per week
15 CP / 2 SWS
Workload
450 h, laboratory work, private study, includes 32 h seminar
Status
Obligatory module
Qualification for
knowledge (Preconditions /
Required skills)
Appropriate knowledge from previous academic studies in a
professionally associated field, bachelor degree
Projects: small groups or single students / Seminar: 20 students
Teaching language
English
The projects must be individually supervised by a professor of the
biomedical department (Department Medizintechnik / Fakultät LS). The
project regulations of the Department Medizintechnik apply
The courses of this module enable the students to...

approach and handle complex problems, tasks and projects in the biomedical field

understand and apply complex laboratory and biomedical equipment to solve the project tasks

find and understand appropriate literature, assess and understand complex informations and apply them
to the project (E.g. literature data bases, specialized publications)

autonomously design, develop and implement laboratory experiments / software / hardware

autonomously design, keep records and interpret measurements using appropriate mathematical and
scientific methods

provide and track a project plan

understand and define project goals and negotiate them with the project sponsors

present the results to peers and sponsors
The students are able to…



handle projects responsible, with awareness to cost, risk and safety
autonomously organize project groups, organize meetings and communication among the project
participants and identify and solve all problems typical to scientific projects
get in contact to experts, where necessary, discuss project and test plans with co-workers and project
sponsors and defend their plans and results against critical objections
24
January 2011
Learning matter



project skills in practice
the scientific matters depend on the projects, which must be supervised / approved by a professor of the
biomedical department
the projects should address scientific level problems from any aspect of biomedical engineering and
biomedical sciences
Related courses / ECTS credits / Presence hours per week:

Biomedical Project
12 CP equivalent to 360 work hours

Research Seminar
(Obligatory) 3 CP / 2 SWS
Teaching methods/ methods
Typically: experimental laboratory work / hardware and software
engineering / literature work / seminar / presentations / project meetings /
project documentation / web based cooperation
achievements
Graded examination: Each student has to deliver:
Written project report, poster (A1), at least one oral presentation. The
results will be graded.
The participation in at least 80% of the project seminar meetings is
obligatory, presentations (1..2, not graded) required.
Scientific literature, depending on the project
25
January 2011
Degree Course
Module code digit:
10
Master-Thesis
Responsible person
Lecturer
All university lecturers
Period/ Semester/ Offer of
this turnus
One Semester
ECTS Credits
30 CP
Workload
900 h (Autonomous private study)
Status
Obligatory module
Qualification for
knowledge (Preconditions /
Required skills)
At least 210 CP from the previous academic studies in relevant
scientific fields/ relevant knowledge in electronics, biomedical
engineering, informatics, human biology
Before the official start of the assignment the subject-matter and the
supervisors must be approved by the board of examiners of the
Department Medizintechnik / Fakultät Life Sciences.
The first examiner must be a professor of the Department
Medizintechnik / Fakultät Life Sciences.
One student per thesis project
Teaching language
English, German language if agreed by the examiners
The students

can solve challenging engineering specific and natural scientific problems.

are familiar with the concepts of scientific work in the medical engineering and use them conducive

use mathematical / physical and technical methods on problems in the bioengineering

have a scientific method-knowledge and are able to evaluate critical results from the literature and to express
and transact them in their own words.

have knowledges and abilities in project- and time management that allow them to work out large scientific
results in the given period.
The students
 are able to talk in trade public about correlative job definitions and methods
 are able to deal unaffiliated with technical and medical working materials.
 can describe and overbring theoretical contexts in the bio medicine.
 are specially invoked to present and protect their results in form of scientific publications and / or public
presentations.

See attachment: catalog of criteria for master thesis
26
January 2011
Requirements
- Master Thesis: in written form
- poster or pdf file for a poster
- the results are to be presented and protected in form of a presentation with following discussion in a
specific forum which is named by the adviser (e.g. seminar in the module BME 02, 03, 05, 09,
Hamburger Studententagung, expert conference, etc.)
Active work, discussion, seminar, presentation, elaboration and publication
achievements
Graded examinations: Written composition, presentation, poster,
colloquium
Scientific literature
27
January 2011
Lecturers
Professors
Name
Expertise
Prof. Dr. Peter Berger
Betriebssoziologie/Human Ressource Management
Prof. Dr. Constantin Canavas
Automatisierungstechnik
Prof. Dr. Friedrich Dildey
Physik
Prof. Dr. Susanne Heise
Biogefahrstoffe/Toxikologie
Prof. Dr. Carolin Floeter
Biologie
Datenverarbeitung
Prof. Dr. Heinrich Heitmann
Physik
Prof. Dr. Frank Hörmann
Präklinisches Rettungswesen/Gefahrenabwehr (1/2W2)
Prof. Dr. Timo Kampschulte
Elektrotechnik
Prof. Dr. Bernd Kellner
Elektrotechnik/Medizintechnik
Prof. Dr. Bettina Knappe
Grundlagen der Chemie
Prof. Dr. Cornelia Kober
Biomechanik/Technische Mechanik
Prof. Dr. Holger Kohlhoff
Mathematik und Informatik
Prof. Dr. Heiner Kühle
Elektrotechnik
Prof. Dr. Christoph Maas
Mathematik
Prof. Dr. Petra Margarithoff
Medizinische Datensysteme
Prof. Dr. Detlev Lohse
Betriebwirtschaftslehre
Humanbiologie
Prof. Dr. Stefan Oppermann
Präklinisches Rettungswesen/Gefahrenabwehr (1/2W2)
Prof. Dr. Anna Rodenhausen
Mathematik
Prof. Dr. Rainer Sawatzki
Prof. Dr. Thomas Schiemann
Datenverarbeitung
Prof. Dr. Marc Schütte
Psychologie (1/2 W2)
Prof. Dr. Marion Siegers
Mathematik
Prof. Dr, Rainer Stank
Technische Mechanik
Medizintechnik
Prof. Dr. Boris Tolg
Medizinische Mess- und Gerätetechnik
Prof. Dr. Gesine Witt
Umweltchemie
28
January 2011
Academic personal
Dipl. Ing. Sakher Abdo
Dipl. Ing. Jan-Claas Böhmke
Dipl. Ing. Horst Enghusen
Dipl. Ing. Peter Krüß
Dipl. Ing. Jens Martens
Dr. Dagmar Rokita
Dipl. Ing. Stefan Schmücker
External lecturers
Prof. Dr. Andreas Brensing
PD Dr. Fehlauer
Michal Huflejt (M.Sc.)
Dr. Ulrich Katscher
Dr. Udo van Stevendaal
Dr. Wolfgang Wöllmer
Dr. Lothar Spies
29
January 2011
------ This page is intentionally left blank---------
30
January 2011
31
January 2011

Module compendium

Transcrição

Documentos relacionados

Notice reg.Practical Examination of Bioscience Department

Generic semantic relatedness measure for biomedical

Faculty of Life Sciences Module compendium

Palestra Head-Marking Tendencies in Indo

5 Closing Session CHINESE WHISPERS VIII Jornadas de Inglês

representing `the` american people 2016 - Amerikanistik

A BPM conference series that over the past decade has built its

Invitation Be part of the conference! Call for - All

Let`s work together!