93946 - Computational Neuroimaging

Course Unit Page

  • Teacher Stefano Diciotti

  • Credits 6

  • SSD ING-INF/06

  • Teaching Mode Traditional lectures

  • Language English

  • Campus of Cesena

  • Degree Programme Second cycle degree programme (LM) in Biomedical Engineering (cod. 9266)

    Also valid for Second cycle degree programme (LM) in Biomedical Engineering (cod. 9266)

  • Course Timetable from Sep 19, 2022 to Dec 19, 2022


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Good health and well-being Quality education

Academic Year 2022/2023

Learning outcomes

At the end of the course, the student has the main theoretical and practical tools for the processing of structural and functional magnetic resonance imaging (MRI) data. He/she has the theoretical knowledge on MR physics and scanner hardware and on structural (T1- and diffusion- weighted) and functional MRI image acquisition and processing. He/she is able to process MRI data performing brain extraction, co-registration, motion and distortion correction and surface-based analysis. He/she is able to deepen further innovative topics by evaluating their pros and cons.

Course contents

Elements of brain anatomy

Brain cells and tissues. Navigating around brain images. Brain structures. Digital atlases

MR physics and scanner hardware

Spin physics. Imaging principles. Basic imaging techniques. Imaging hardware. Image presentation. Image artifacts. Experimental activities on a MRI Tabletop

MRI modalities for Neuroimaging

Introduction to MRI for Neuroimaging. Structural and functional imaging. Clincal images

Structural imaging

T1-weighted imaging: acquisition, brain extraction, co-registration techniques, surface-based analysis, single- and group-based analysis

Diffusion weighted imaging: acquisition, basic principles of diffusion and diffusion tensor imaging (DTI), preprocessing and extraction of the tensor, parametric maps, advanced diffusion techniques

Functional imaging

Basic of fMRI, BOLD effect, acquisition, pre-processing (slice-timing correction, smoothing, high-pass filtering), single-subject, within group and between group analysis, co-registration

Advanced Machine and Deep Learning for Neuroimaging

Basics of machine and deep learning techniques. Machine and deep learning strategies for neuroimaging. Examples in neuroimaging


Notes provided by the Professor.

M. Jenkinson, "Introduction to Neuroimaging Analysis", Oxford University Press, 2017.

Teaching methods

The course comprehends both ex-cathedra lessons and practical exercises on the personal computer. The aim of the lessons is to provide the students with a theoretical knowledge about the magnetic resonance imaging, and to make them aware about the advantages and limitations of each available technique. The practical exercises aim at training the students on the resolution of simple real biomedical problems, and at showing the potential benefits but also the shortcomings and difficulties introduced by processing techniques and software packages. 

Given the type of activity and teaching methods adopted, the attendance of this training activity requires the prior participation of all students in modules 1 and 2 of training on safety in the workplace in e-learning mode.

Assessment methods

The learning assessment will be performed through a final examination consisting of an oral test focused on both the theoretical concepts presented during the lessons and the software tools used in the laboratory. The exam ascertains the theoretical-practical skills and competences of the student, correctness of language, and clearness of concepts and exposition.

Teaching tools

Document camera, videoprojector.

Notes provided by the Professor.

Personal computer laboratory.

Software environment for performing practical exercises in the computer science laboratory.

Office hours

See the website of Stefano Diciotti