Module Handbook - HAW Hamburg

Fakultät Life Sciences Module compendium Master degree programme Biomedical Engineering / Medizintechnik

www.haw-hamburg.de

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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

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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

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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.

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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 Therapy Technologies 2 Therapy Technologies 3

Sem.

2

4

Medical Data Systems

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

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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

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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

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January 2011

Biomedical Engineering (Master)


02

Data Acquisition and Processing



Lecturer



One Semester / summer and winter semester / twice a year


5 CP / 3 SWS

Workload

150 h: 48 h presence, 102 h private studies

Status

Obligatory module


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

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 …  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.

Personal and interpersonal skills The students are able to 

keep one’s distance to their results and especially to their own programs,

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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)

Associated courses / ECTS credits / presence hours per week Data Acquisition and Processing: 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.


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

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January 2011



03

Microsystems and Robotics


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


One Semester / winter semester / once a year


7 CP / 5 SWS

Workload

210 h: 80 h presence, 130 h private study

Status

Obligatory module


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

Acquired competences / educational objectives Expertise and methodological competences 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. Personal and interpersonal skills The students are able to …  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

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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

Associated courses / ECTS credits / presence hours per week 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


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

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January 2011



04

Therapy Technologies



Lecturer

Prof. Dr. Friedrich Ueberle, Dr. med. priv. Doz. Fehlauer, Dr. Wolfgang Wöllmer, assistant lecturers with background in science, hospitals or industry


One Semester / winter semester / once a year


5 CP / 4 SWS

Workload


Status

Obligatory module


None / Appropriate knowledge from previous academic studies in a professionally associated field, bachelor degree: informatics, electronics, physics, mechanics, human biology


20

Teaching language

English

Acquired competences / educational objectives Expertise and methodological competences The courses of this module enable the student to...     

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.

Personal and interpersonal skills The students are able to …  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

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January 2011

Associated courses / ECTS credits / presence hours per week Therapy Technologies – 1 2 CP / 1.5 SWS Therapy Technologies – 2 1 CP / 1.0 SWS Therapy Technologies – 3 2 CP / 1.5 SWS (Courses may be pooled or split depending on the complexity of topics) (Teaching methods / methods generally / types of media)



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)

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January 2011



05

Medical Data Systems


Prof. Dr. Jürgen Stettin

Lecturer

Prof. Dr. Jürgen Stettin, Prof. Dr. Margaritoff, assistant lecturer of science and industry


One Semester / each in the winter term


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

Acquired competences / educational objectives Expertise and methodological competences 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.

Personal and interpersonal skills 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)

Associated courses / ECTS credits / presence hours per week Medical Data Systems 4 CP / 3 SWS

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January 2011

Teaching methods / methods generally / types of media

Semester lectures, practical work /expert puzzle, working groups, power point, exercises, private study, table, beamer, software, e-learning elements


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

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January 2011

Biomedical Engineering Master


06

Advanced Biosignal Processing



Lecturer

Prof. Dr. Friedrich Ueberle, Dr. van Stevendaal, Dr. Ulrich Katscher, Dr. Spies and other assistant lecturers with background in science, hospital or industry


One Semester / summer semester / once a year


4 CP / 4 SWS

Workload

120 h: Presence: 64 h, 56 h private study

Status

Obligatory module


None / Appropriate knowledge from previous academic studies in a professionally associated field, bachelor degree: mathematics, informatics, systems theory, human biology


20

Teaching language

English

Acquired competences / educational objectives Expertise and methodological competences The courses of this module enable the student to...  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. Personal and interpersonal skills The students are able to …  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

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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)

Associated courses / ECTS credits / presence hours per week 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 Teaching methods / methods generally / types of media



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

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January 2011



07

Medical Image Processing


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


One Semester / summer semester / once a year


8 CP / 6 SWS

Workload


Status

Obligatory module


None / Students should have knowledge in electronics, biomedical engineering, computer science (especially programming) and human biology.

Max. number of participants

20

Teaching language

English


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

Personal and interpersonal skills 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

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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

Associated courses / ECTS credits / presence hours per week Medical Image Processing: 6 CP / 4 SWS Image Processing in Therapy and Diagnostics: 2 CP / 2 SWS Teaching methods / methods generally / types of media

Seminaristic lectures, practical courses, expert-puzzle, team-work PowerPoint-presentation, tutorials, private study blackboard, projector, software-demonstration e-Learning


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

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January 2011



08

Management of Medical Technologies


Prof. Dr. Jürgen Lorenz

Lecturer



One semester / 1st or 2nd semester / each semester


7 CP / 5 SWS

Workload


Status

Obligatory module


Appropriate knowledge from previous academic studies in a professionally associated field, bachelor degree


20

Teaching language

English

Acquired competences / educational objectives Expertise and methodological competences 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.

Personal and interpersonal skills 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

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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

Associated courses / ECTS credits / presence hours per week  



Market Perspectives (of Medical Technologies)) Assessment Methodology Module Seminar (Leadership / Communication & Presentation / Statistics and Advanced Methods)

Teaching methods / methods generally / types of media




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.

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January 2011

Degree Course


Module code digit : 09

Biomedical Project

Module coordination/ Resposible person


Lecturer

All university lecturers of the department MT, Prof. Dr. Ueberle


One semester / 2nd semester / each semester


15 CP / 2 SWS

Workload

450 h, laboratory work, private study, includes 32 h seminar

Status

Obligatory module

Qualification for participation/ Previous 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

Acquired competences / educational objectives Expertise and methodological competences 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

Personal and interpersonal skills 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

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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 generally / types of media

Typically: experimental laboratory work / hardware and software engineering / literature work / seminar / presentations / project meetings / project documentation / web based cooperation


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

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10

Master-Thesis

Module coordination/ 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 participation/ Previous 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


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.

Personal and interpersonal skills 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

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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.) Teaching methods / methods generally / types of media

Active work, discussion, seminar, presentation, elaboration and publication


Graded examinations: Written composition, presentation, poster, colloquium


Scientific literature

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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

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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

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