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Course: 2023/2024

Medical Image Reconstruction

(19282)

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

EPI

Coordinating teacher: ABELLA GARCIA, MONICA

Department assigned to the subject: Bioengineering Department

Type: Electives

ECTS Credits: 6.0 ECTS

Course: 1º

Semester: 2º

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.

Objectives

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

Link to document

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

Both for the ordinary and the extraordinary call, the course evaluation will be based on:
- Participation during the classes: 5% of the final grade.
- Tests and works done individually or in groups during the course: 95% of the final grade.

% 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

Electronic Resources *

Avinash C. Kak and Malcolm Slaney · Principles of Computerized Tomographic Imaging : http://www.slaney.org/pct/

Detailed subject contents or complementary information about assessment system of B.T.

Go to the detailed contents

(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.