ELI2600 Medical signals and images Course description

The course describe the analysis and processing of signals in the frequency domain and analysis of systems for signal processing. Images are two-dimensional signals. The course also include the acquisition of image data, reconstruction of images and basic image processing of images for medical diagnostics.

Individual written exam, 3 hours

The exam result can be appealed.

No requirements over and above the admission requirements.

After finishing this course the student should have the following outcome:

Knowledge:

Understand the connection between the impulse response and frequency response

Understand that images are 2D signals and can be analysed accordingly

Understand the connection between resolution, impulse response, point spread function and frequency response.

Know how to describe wave propagation, reflection, refraction and attenuation of waves

Understand how an ultrasound image is formed and how the pulses are transmitted and recorded.

Understand how x-rays are generated and how an X-ray image is formed.

Understand how tomographic data are recorded and how CT-images are reconstructed.

Understand the physical principals of magnetic resonance and how images are formed.

Skills

Can describe and analyse signals with the use of the Fourier transform.

Be able to frequency analyse signals and design optimal processing

Can digitize signals by sampling and AD-conversion.

Can explain the difference between diagnostic and technical image quality.

Understand how images are reconstructed fro measured data.

Can use an ultrasound scanner and chose optimal settings.

General competence:

Be able to process a measured signal to optimize information content.

Be able to evaluate technical quality of an image in order to optimize diagnostic quality

Lectures and exercises including use of MATLAB

Mandatory deliveries:

4 exercises

1 ultrasound lab

1 diagnostic group work

Each task should take about 5 hours of work

Individual written exam 3 hours.

The result of the exam can be appealed.

In the event of a resit or rescheduled exam, an oral examination may be used instead. In case an oral exam is used, the examination result cannot be appealed.

No requirements over and above the admission requirements.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student is capable of:

• explaining what a database system is and what it consists of
• explaining what XML is and how it is used
• explaining what transactions are and how they are carried out
• explaining the use of indices and different ways of storing the files physically
• explaining what a data warehouse is and how it differs from a database system
• explaining how the combination of ER modelling and normal forms provides well-structured relational databases

Skills

The student is capable of:

• designing databases with the help of ER modelling
• creating databases and using them with the help of the SQL language
• drawing ER models and generating database scripts with the help of software

General competence

The student is capable of:

• explaining the documentation and design of databases with ER models

Lectures and work on practical assignments. The weekly assignments will form the basis for the written assignments to be submitted.

The following coursework is compulsory and must be approved before the student can sit the exam:

• 12 individual assignments

Grade scale spring 2021:

Pass-Fail

[Grade scale earlier]:

Grade scale A-F.