Checking date: 04/05/2023

Course: 2023/2024

Medical Image Reconstruction
Master in Machine Learning for Health (Plan: 480 - Estudio: 359)

Coordinating teacher: ABELLA GARCIA, MONICA

Department assigned to the subject: Bioengineering Department

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Image processing, programming, statistics. Advanced programing skills in Matlab are essential to follow the sessions, which will have a high practical content based on algorithm programing in Matlab.
The objective of this course is to allow the students to understand the main image reconstruction techniques used in the CT, PET, SPECT and MRI medical imaging systems, not only from a theoretical point of view, but also in a practical way through the implementation of the algorithms in Matlab. At the same time, the student will become familiar with the data acquired in each type of system, which is essential to be able to correctly approach the reconstruction problem.
Skills and learning outcomes
Description of contents: programme
This course covers the main image reconstruction techniques used in the tomographic imaging systems TAC, PET, SPECT and MRI. It will allow the student to get familiar with the acquired data in each system that enable the generation of the tomographic image, basic to be able to approach the reconstruction problem. The contents can be sumarized in (see more details in the weekly planning): 1. Introduction to tomographic image reconstruction. 2. Imaging basics: spatial resolution, noise/artefifact, Fourier transform, Radon transform. 3. Acquisition geometries: parallel beam, fan beam beam and cone beam. 4. Analytical algorithms. 5. Iterative algorithms. 6. Advanced methods. 5. Practical applications in different image modalities.
Learning activities and methodology
The course will be mostly in computer room to put in practice all the concepts.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Aninash C. Kak, Malcolm Slaney. Principles of Computerized Tomographic Imaging (Classics in Applied Mathematics). Society for Industrial and Applied Mathematics. 1987
Additional Bibliography
  • Frank Natterer. The Mathematics of Computerized Tomography. SIAM. 2001
Recursos electrónicosElectronic Resources *
Detailed subject contents or complementary information about assessment system of B.T.
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The course syllabus may change due academic events or other reasons.