TTK10:  Signal processing in ultrasound imaging 

Updated  26.08.2019  Hans Torp

Content: The subject presents some selected techniques in digital signal processing used in ultrasound imaging. Examples: Complex demodulation, filter design, sampling and interpolation, detection theory and parameter estimation. The techniques are used on simulated/experimental ultrasound data by programming in Matlab.

Form of instruction: PBL (problem based learning). Weekly meetings under supervision where the students will solve a problem by literature study and Matlab programming. It will be discussed how to approach the problem, and established one or more learning objectives. The students will then solve the problem independently. At the beginning of the next meeting the learning objectives and the solution of the problem is presented by the students, before starting on the next exercise.


Autumn 2019 first meeting 04.09

Place: Meeting room AHM 34, 3rd floor AHL-Center, St. Olav Hospital (except 11.09 and 18.09)

Time:  wednesday 09:15 - 12:00

Please send an email to hans.torp@ntnu.no if you want to attend!

Topics and exercises autumn 2019:

The date when each exercise is introduced might be adjusted gradually.
Suggested solutions will be made available here after each study group meeting.

04.09

Linear time-invariant filter – signal with noise Exercise Suggested solutions
  11.09

Room BG62 (Bevegelsessenteret 6th floor.

Linear time-invariant filter - harmonic imaging Exercise Suggested solutions
Suggested litterature:
Nonlinear acoustics in diagnostic ultrasound, particularly the section "Tissue harmonic imaging".
Medisinsk ultralydavbildning

 18.09

Room BG62 (Bevegelsessenteret 6th floor.

Complex representation of band-pass signals – Lecture Details

Linear filters - Clutter-filter in ultrasound blood flow measurement Exercise Suggested solutions

 

 
 25.09
Clutter filter: Summary.  Time: 9.15 - 12
Sampling and interpolation Exercise
02.10

Sampling and interpolation Summary Suggested solutions 

Classical detection-theory. Neyman Pearson test. Lecture + brainstorming session
Reading material  Exercise


 -

16.10
Classical detection-theory. Neyman Pearson test (summary) Suggested solutions

23.10

Estimation-theory Exercise
Lecture + brainstorming session.
30.10

Estimation theory (summary 1)


 

06.11

 Estimation theory (summary 2) Suggested solutions

20.11

Q&A session

28.11

Oral exam


 



Recommended literature that is not mentioned in the exercises:

Signal processing background

Needed for the course TTK10 are listed below. This can be found in almost any textbook in digital signal processing.

If you are not familiar with this stuff, it helps to go though some examples. E. C. Ifeachor contains a lot of good examples.

o   Fourier transform

o   Discrete Fourier transform

o   Z-transform

o   convolution, correlation

o   Reading: E. C. Ifeachor ch.2.1 - 2.3, 3.1 - 3.4, 4.1 - 4.3

o   Impulse response, frequency response

o   FIR, IIR filters

o   Filter order, coefficients; relation to impulse/frequency response

o   Concept of stopband, transition band, passband

o   Reading: E. C. Ifeachor ch.5.1 - 5.3 + 5.4.1 5.4.2