Topic outline

  • Overview:

    The course in 2021 is organized during Periods III and IV. It consists of weekly learning packages which are to be studied and learned by the students independently. The learning objectives for each week are listed in Materials section along with pointers to book chapters, electronic materials, and summary videos containing the information. Quizzes and home work exercises, which by request are discussed during the weekly sessions in Zoom, are used to monitor the learning. A learning report and a project work are compulsory.


    Basic courses in mathematics and physics. Basic Matlab knowledge.

    Kick-off meeting and exercise sessions in Zoom:

    The only compulsory online session is a kick-off meeting on Tuesday, January 12 at 14:15-15:00. There are no other compulsory online sessions during the course, but there are weekly sessions arranged on request, where you can join to discuss the exercises and to get help in solving them. The voluntary sessions are held Tuesdays 14:15-16:00 if requested by the day before the session. You can join via Zoom.


    Exam is replaced by learning report.


    The grading of the course is based on the learning report, the mandatory project work, and exercises.


    The main lecturer/organizers are Prof. Simo Särkkä ( and Lecturer Lauri Palva (

  • The main course book is

    • Robert W. Brown, Y.-C. Norman Cheng, E. Mark Haacke, Michael R. Thompson, Ramesh Venkatesan, "Magnetic Resonance Imaging: Physical Principles and Sequence Design", 2nd Edition, Wiley, 2014.

    which is available as an e-book via Aalto University library. The following books are also useful on the course, but the exercises will be taken from Brown's book:

    • Zhi-Pei Liang, Paul C. Lauterbur, "Principles of Magnetic Resonance Imaging: A Signal Processing Perspective", Wiley-IEEE, 1999
    • Donald W. McRobbie, Elizabeth A. Moore, Martin J. Graves, Martin R. Prince, "MRI from Picture to Proton", 2nd Edition, 2007

    The pointers to other materials will be provided on this page. The tentative list of weekly learning objectives is below (chapter, problem numbers and page references are to Brown's book). Zoom sessions are meant for discussion about the current week's exercises. The exercises are returned on MyCourses in two batches: rounds 1-4 by 23.2. and rounds 5-8 by 20.4.

    Note that the electronic materials shown in the exercise session ("Brief Recap of Chapter X of Brown et al. (2014)") are also linked below – but – the exercise answers that are presented in the exercise sessions will not be uploaded here, because they are home works. If you want to see them, you need to attend the session.

    Overview of MRI (week 3)

    Spins and magnetization (week 4)
      • Chapter 2: Classical Response of a Single Nucleus to a Magnetic Field (pp. 19-36)
      • Brief Recap of Chapter 2 of Brown et al. (2014)
      • Zoom session on Tuesday 26.1. (14:15-16:00)
      • Youtube videos:
      • Exercise 2
      • Quiz 2

    Rotating frame (week 5)
      • Chapter 3: Rotating Reference Frames and Resonance (pp. 37-51)
      • Brief Recap of Chapter 3 of Brown et al. (2014)
      • Zoom session on Tuesday 2.2. (14:15-16:00)
      • Youtube videos:
        • 90 degree pulse and relaxation:
        • Spin resonance and the rotating frame (this contains some quantum mechanics not required on this course but hopefully helps visualizing the rotating frame):
      • Exercise 3
      • Quiz 3

    Bloch equations (week 6; Exercises for weeks 1-4 to be returned by 23.2.)
      • Chapter 4: Magnetization, Relaxation, and the Bloch Equation (pp. 53-66)
      • Brief Recap of Chapter 4 of Brown et al. (2014)
      • Zoom session on Tuesday 9.2. (14:15-16:00)
      • Youtube videos:
      • Exercise 4
      • Quiz 4

    Signal detection (week 7)
      • Chapter 7: Signal Detection Concepts (pp. 95-111)
      • Brief Recap of Chapter 7 of Brown et al. (2014)
      • Zoom session on Tuesday 16.2. (14:15-16:00)
      • Youtube videos:
      • Exercise 5
      • Quiz 5

    Spatially varying fields and echoes (week x)
      • Chapter 8: Introductory Signal Acquisition Methods: Free Induction Decay, Spin Echoes, Inversion Recovery, and Spectroscopy (pp. 113-139)

    Basics of Fourier imaging (week x)
      • Chapter 9: One-Dimensional Fourier Imaging, k-Space, and Gradient Echoes (pp. 141-164)
      • Brief Recap of Chapter 9 of Brown et al. (2014)
      • Zoom session on Tuesday x.x. (14:15-16:00)
      • Youtube videos:
      • Exercise 7
      • Quiz 7

    Multidimensional Fourier imaging (week x; Exercises for weeks 5-8 and the Matlab exercise to be returned by 20.4.)

    Matlab exercise (week x)  Slides of the Matlab exercise session.

  • This is a preliminary timetable of the course. Changes, in particular in Period IV, are possible, but the contact sessions are on Tuesdays at 14:15-16:00 if requested by the day before the session. See Materials for topics that will be covered during each week.

    January 12 Kick-off meeting, 14:15-15:00. Zoom link:

    January 19 Zoom session (exercise round 1; Chapter 1), 14:15-16:00. Only organized by request.

    January 26 Zoom session (exercise round 2; Chapter 2), 14:15-16:00. Only organized by request.

    February 2 Zoom session (exercise round 3; Chapter 3), 14:15-16:00. Only organized by request.

    February 9  Zoom session (exercise round 4; Chapter 4), 14:15-16:00. Only organized by request.

    February 16 Zoom session (exercise round 5; Chapter 7), 14:15-16:00. Only organized by request.

    February 23 Exam week, no teaching, deadline for returning exercise sets 1 to 4 on MyCourses. Submission of exercises 1-4.

    March 2  Zoom session (exercise round 6; Chapter 8), 14:15-16:00. Only organized by request.

    March 9 Zoom session (exercise round 7; Chapter 9), 14:15-16:00. Only organized by request.

    March 16 Zoom session (exercise round 8; Chapter 10), 14:15-16:00. Only organized by request.

    March 23 Zoom session (Matlab exercise), 14:15-16:00. Only organized by request.

    March 30 Recap, 14:15-16:00

    April 20 Deadline for returning exercise sets 5 to 8 and the Matlab exercise on MyCourses. Submission of exercises 5-8Submission of the Matlab exercise.

    April 27 Deadline for returning the project work report.

    April 30 Deadline for the learning report

  • Quizzes do not affect grading.

  • This page will contain the home work exercises as well as the place to return the answers. There are 8 exercise sets, each containing two problems. There is also also Matlab exercise. Exercises 1-4 are to be submitted by Tuesday, February 23 and exercises 5-8 by Tuesday, April 20. You can either write your answers by hand and scan them or use e.g. Word or LaTeX.

    There are voluntary exercise sessions, held in Zoom on most Tuesdays at 14:15-16 if requested, see Timetable. During the sessions, the teachers will assist in solving the exercises. See Materials for more information.

    Exercises 1 to 4: Exercise1, Exercise 2, Exercise 3, Exercise 4

    Exercises 1 to 4 are to be returned on MyCourses by February 23. Submission of exercises 1-4.

    Exercises 5 to 8: Exercise 5, Exercise 6 (in Problem 6.2, it may be easier to use Equation (8.30) instead of (8.31)), Exercise 7, Exercise 8

    Exercises 5 to 8 are to be returned on MyCourses by April 20Submission of exercises 5-8.

    MATLAB exercise

    Matlab exercise is to be returned on MyCourses by April 20Submission of Matlab exercise. Data files for the exercise are available here.

  • A mandatory project work is writing an essay on an imaging method presented in the text book.


    Write an essay (4 to 6 pages) on an advanced MR imaging topic. The possible topics are (chapters and page numbers refer to those in the course book by Brown et al.):

    • Water-fat separation (Chapter 17; pp. 413-446)
    • Fast steady-state imaging (Chapter 18; pp. 447-508)
    • Partial reconstruction and EPI (Chapter 19; pp. 511-566)
    • Diffusion imaging (Chapter 21; pp. 619-636)
    • Sequence design (Chapter 26; pp. 779-821)
    • Parallel imaging (Chapter 28; pp. 859-889)

    The essay must be submitted by April 27.

  • Instructions

    Your task is to write a report which is then used to replace to traditional exam in the course. The report is evaluated based on how well and correctly you can explain and describe the MRI related phenomena, concepts and methods, which have been covered in this course. In the following are the instructions for your learning report.

    Your learning report should cover the textbook chapters 1-4 and 7-10, which were also included in the lectures. The titles of the chapters are in the report template along with a few chapter specific questions. The questions are of essay type such as “Explain the principle of …”. In your learning report, give answers to these questions. Use your own words and write with complete sentences. Make your explanations complete but be concise. Do not copy text from the book or online resources (we know all of them) but write in your own words. The information content is what matters. Please, use equations and diagrams to clarify your answers. There is no minimum or maximum number of pages but keeping it in less than ~10 pages is recommended.

    The maximum amount of points for the learning report is 25 (plus 1 extra point for the feedback). This is the same amount of points as would have been in the exam. The points for each textbook chapter are marked in the report template. The last question about course feedback is worth one extra point. By having completed the course exercises, you can get up to 4 extra points. The overall grading is explained in MyCourses section “Grading”.

    We might also later ask a few of the students to explain their reports to us in teleconference calls in order to double check that the report has been written by the student.

  • The grading of the course is based on a combination of the learning report, mandatory project work, and exercises.

    • The learning report is graded similarly as the exam would have been graded. The maximum number of points is 25. You must receive at least 10 points to pass the course. (Any extra points are not included. That is, you do not pass the course if you get 5 points from the learning report even if you have done all the exercises and submitted course feedback.)
    • By completing the 8 regular exercise sets, you can receive a maximum of 3 extra points for the exam. Furthermore, the Matlab exercise at the end of course and course feedback included in the learning report are both worth 1 extra point. That is, by completing most of the exercises and giving course feedback you can get up to 5 extra points.
    • You must complete the project work (this year an essay) with a passing grade.
    • The grade brackets are 1: 10 to 12 points; 2: 13 to 15 points; 3: 16 to 19 points; 4: 20 to 22 points; 5: 23 to 25 points.