Medical Imaging

lgbio2050  2020-2021  Louvain-la-Neuve

Medical Imaging
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 30.0 h
Kerckhofs Greet; Lee John; Macq Benoît; Peeters Frank;
Students are expected to master the following skills: basic mathematical notions (derivatives, coordinate systems) and the Fourier transform, as they are covered within the course LFSAB1106
Main themes
The course deals with the basics of medical imaging, including digital image processing, as well as the main modalities of medical imaging (transmission imaging, emission imaging, ultrasound echography, and magnetic resonance imaging).

At the end of this learning unit, the student is able to :

1 With respect to the AA referring system defined for the Master in Biomedical Engineering, the course contributes to the development, mastery and assessment of the following skills :
  • AA1.1, AA1.2, AA1.3
  • AA2.1, AA2.2, AA2.3, AA2.4
  • AA4.1, AA4.2, AA4.4
  • AA5.2, AA5.4
a.      Domain-related learning outcomes
Upon completion of the course, the student will be able to :
Part 1 (digital image processing) :
  • Define formally the concept of image and its properties (size, matrix of pixels/voxels, colormap, histogram, color or channel encoding, encoding of the matrix, compression, frequency space representation). (axis 1.1)
  • List the main classes of problems solved by image processing (denoising, deconvolution, filtering, edge detection, segmentation, registration). (axes 1.1 et 1.2)
  • List a few typical methods used to solve these different classes of problems. (axis 1.2)
  • Justify the choice of a method (data representation, criterion to be optimized) with respect to simple problems (segmentation of a slice, registration of two slices, etc.). (axes 1.3 et 2.3)
  • Solve these simple problems by implementing in Matlab the algorithms corresponding to the methods described in the course. (axes 2.1, 2.2, 2.3, 2.4)
Part 2 (MRI and echography) :
  • Describe the physical principle behind magnetic resonance imaging (MRI) : Nuclear Magnetic Resonance (spin, excitation, reception, relaxation, chemical shift,'). (axe 1.1)
  • Describe the principle that allows image reconstruction: gradients, slice/volume selection, frequency/phase encoding, k-space, Fourier transform, resolution, ', and describe the possible artifacts. (axes 1.1 et 1.2)
  • List and describe some sequences used for image acquisition (spin echo and gradient echo, ultrafast sequences and echo planar imaging) and compare their advantages, drawbacks, and application conditions. (axe 1.1)
  • List and describe the possible contrasts : proton density, T1, T2, T2*, flow, diffusion, perfusion, functional and spectroscopic imaging. (axe 1.1)
  • Explain the principles of ultrasound imaging : ultrasounds, transducers, image formation and image quality, Doppler imaging,' (axe 1.1)
Part 3 (imaging modalities using ionizing radiation) :
  • List the different imaging modalities described in the course (radiography, computerized tomography, scintigraphy, SPECT, and PET) and explain their underlying principle (mainly from the physics point of view), mention their applications, advantages and limitations. (axis 1.1)
  • Compare the performances of the different modalities together with the quality of the images (resolution, noise, contrast). (axis 1.2)
  • Discuss the specificity of each technique and the complementarity of the different modalities.  (axis 2.3)
b.      Transversal learning outcomes
Upon completion of the course, the student will be able to :
  • Meet the objectives of a course given in English (understanding of the magistral lecture and of the supporting material). (axes 5.2 et 5.4)
  • Work in groups of two on small projects (challenges), namely : be able to share and distribute the workload, understand and describe the work of the other student, write a joint report. (axes 4.1, 4.2 et 4.4)
The course is divided in three parts :
Part 1 : digital image processing (definition and properties of an image, histogram, spectrum, segmentation, edge detection, filtering, mathematical morphology, registration)
Part 2 : magnetic resonance imaging and ultrasound imaging (linear systems : convolution, point spread function, Fourier transform, sampling ; image reconstruction : Radon transform, filtered backprojection, algebraic reconstruction)
Part 3 : transmission imaging (radiography and computerized tomography) and emission imaging (scintigraphy, SPECT and PET)
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The course consists of 13 lectures in English, three sessions of exercises (mostly reminders or introductory exercises), and three small projects (challenges). For each challenge, groups of two students must submit a report. These reports are evaluated. During a debriefing session, the most commonly encountered issues will be presented and discussed between the teacher and the students. A visit of medical imaging facilities in Saint-Luc University Hospital (Brussels) completes the program. All activities can switch to comodal or distancial depending on sanitary conditions.
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Evaluation of the exercises (challenges only)
Each challenge report will be given a note. The global note for all challenges affects the final evaluation (see below). The introductory exercises that preceed the challenges will be evaluated in a formative way with no impact on the final evaluation. The submission of all challenge reports is mandatory to be admitted to the oral examination for part 1 of the course.
Evaluation of the learning outcomes
The students will be evaluated individually and orally (presential or distancial) on the basis of the contents and learning outcomes mentioned above. The oral examination (in French or English, up to the student) will address :
Part 1 (10 points out of 20, image processing) : the student must answer two questions dealing with the methods and algorithms used in the challenges (see below). Starting from a simple problem (which can be solved with a pen and paper), the student should be able to justify his choices. On 10 points, one third results from the evaluation of the challenge reports, the other two thirds corresponding to the two questions.
Part 2 (5 points out of 20, IRM and ultrasound imaging ) : the student must answer two questions (oral examination with written preparation)
Part 3 (5 pointsout of 20, imaging modalities using ionizing radiation) : the student must answer two questions (oral examination with written preparation).
Other information
The first two sessions of exercise are organized in the computer room
Support de cours :
Partie 1 : transparents.
Parties 2 et 3 : transparents et syllabus.
Les documents du cours sont disponibles sur Moodle.
Teaching materials
  • Transparents et notes relatives aux séances de cours disponibles sur Moodle
Faculty or entity
Force majeure
Teaching methods
Ex-cathedra course broadcasted or recorded.  Practical sessions on computers, and project to be carried out individually or by groups of 2 students.
Evaluation methods
Oral exam with Teams without preparation, if a face-to-face oral exam in LLN is not permitted. See above for otherwise unchanged details.

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Master [120] in Computer Science and Engineering

Master [120] in Electrical Engineering

Master [120] in Mathematical Engineering

Certificat universitaire en physique d'hôpital

Master [120] in Physics

Master [120] in Statistic: Biostatistics

Master [120] in Biomedical Engineering