Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Master's Degree in Computer Engineering for Intelligent Systems - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
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2° Year activated in the A.Y. 2025/2026
| Modules | Credits | TAF | SSD |
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3 modules among the following
(A.A. 2025/2026 Internet of medical things not activated)| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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3 modules among the following
(A.A. 2025/2026 Internet of medical things not activated)| Modules | Credits | TAF | SSD |
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4 modules among the following:
- 1st year: Advanced visual computing and 3d modeling, Computer vision, Embedded & IoT systems design, Embedded operating systems, Robotics
- 2nd year: Advanced control systemsLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Neurohealth - INTELLINGENT NEURO-DATA MODELING (2025/2026)
Teaching code
4S012367
Academic staff
Ilaria Boscolo Galazzo, Lorenza Brusini, Francesca Benedetta Pizzini
Credits
5
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
Period
2nd semester dal Mar 2, 2026 al Jun 12, 2026.
Courses Single
Authorized
To show the organization of the course that includes this module, follow this link: Course organization
Program
1) MRI: recall of the main signal/contrast sources;
2) Structural sequences: main properties and operations for image enhancement, feature extraction and longitudinal analyses;
3) Diffusion imaging: basic principles; main processing techniques and methods, including diffusion tensor modeling [DTI] for microstructure quantification and tractography;
4) Perfusion imaging: DSC-MRI, DCE-MRI, Arterial Spin Labeling. Image processing and model-based quantification of the most common perfusion parameters;
5) Functional imaging: basic principles; fMRI data design, acquisition and analysis, including data pre-processing/filtering and multivariate analyses;
6) Brain connectomics: from structural connectivity to functional brain networks.
Bibliography
Didactic methods
Theoretical exposition of the main topics, with annual updating of the clinical problems covered. • Interactive discussion: Analysis and interpretation of real clinical cases through guided discussion.
• Practical exercises: Simulation of image acquisition and interpretation protocols.
• Hospital visits: Direct participation in the acquisition of MR images and observation of department activities.
• Group work: Preparation and presentation of clinical cases or in-depth analysis of new technologies.
• Multimedia material: Use of images, videos and visualization software to promote practical learning.
Learning assessment procedures
Single oral exam including the two teaching modules
Evaluation criteria
To pass the exam, the student has to:
- Be able to explain the main neuroimaging methods and related analysis methods
- Demonstrate the ability to critically interpret the results produced with the techniques studied
Exam language
Inglese
