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

The 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.

activated in the A.Y. 2020/2021
ModulesCreditsTAFSSD
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
English B2
4
F
-
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activities
2
F
-

Legend | 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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S004554

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

The teaching is organized as follows:

Teoria

Credits

4

Period

II semestre

Academic staff

Alessandro Daducci

Laboratorio

Credits

2

Period

II semestre

Academic staff

Alessandro Daducci

Learning outcomes

The course aims at providing students with the applied and theoretical basis for processing biomedical images and extract useful information from them to support the diagnosis process. At the end of the course, the student shall demonstrate that he/she can apply the material discussed in the lectures to solve effectively the most common issues that may happen throughout a typical analysis pipeline, from the acquisition of the raw images to the correct interpretation of the information extracted from them. In particular, at the end of the course the student shall demonstrate to be able to: -- understand the basic physics principles behind image acquisition and formation with the major imaging modalities (X-rays, CT, MRI, PET, US), as well as advantages, disadvantages and peculiarities of each modality; -- open, manipulate and correctly interpret the multidimensional data acquired with such modalities, which represent specific physical and biological features of the tissue/organ under exam; -- develop an analysis pipeline to extract useful information from such biomedical images and help the diagnostic process, applying at each step the most adequate processing choices for the specific data at hand. At the end of the course, the student shall demonstrate the ability to effectively interact with different collaborators having specific backgrounds typically required in a clinical study based on medical imaging, e.g. engineers, physicists, physicians etc. He/she will also have the required foundations to be able to elaborate further on any scientific, methodological and recent advances in the field beyond the content of the lectures to extend such basic techniques to diverse and more complex analysis scenarios.

Program

(1) Basic concepts
- Image properties: pixel vs voxel, spatial resolution, orientation, data type etc
- File formats: DICOM, NIFTI, MINC etc
- Signal-to-noise (SNR) vs Contrast-to-noise (CNR) ratio
- Noise, blurring and modality-specific artifacts

(2) Overview of major medical imaging modalities
- Radiography: X-rays projection, fluoroscopy and computed tomography (CT)
- Nuclear medicine: SPECT and PET
- Ultrasounds (US)
- Magnetic Resonance Imaging (MRI)

(3) Medical image registration
- Geometric transformations
- Features and similarity measures
- Transformations (linear vs non-linear)

(4) Morphometry analysis
- Region-of-interest analysis
- Voxel-based morphometry
- Surface-based morphometry
- Tract-based morphometry in white matter

(5) Structural connectivity estimation
- Diffusion MRI: principles and main applications
- Local reconstruction: DTI, DSI, CSD etc
- Tissue microstructure estimation: axon diameter mapping, AxCaliber, ActiveAx, CHARMED, NODDI etc
- Tractography: local vs global methods, probabilistic, recent advances

(6) Functional connectivity estimation
- Physiology of neurons and how to record their activity
- Functional MRI: principles and main applications
- Elettroencefalography (EEG) and Magnetoencefalography (MEG): principles and main applications
- Static vs dynamic connectivity

(7) Connectivity analysis (connectomics)
- A network representation of the brain: how and why?
- Studying brain networks with graph theory: concepts and measures
- Comparing brain networks in different groups of subjects

(8) Laboratory
- Hands-on activities on the topics covered throughout the course
- Real neuroimaging data provided to analyze

Examination Methods

The grade will be based on a discussion about the final project assigned during the course. The final project is a very important part of the course, as it allows students to synthesize the concepts learned throughout the course, understand the motivation behind each modality, experiment typical problems that arise in daily-life medical images and apply the appropriate techniques to improve image quality and extract useful information.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE