Ai Applied to Neuroimaging
A.Y. 2024/2025
Learning objectives
The course in "AI Applied to Neuroimaging" in the Master's program in Human-centered artificial intelligence's primary objective is to provide students with an understanding of the applications of artificial intelligence in the field of neuroimaging.
Expected learning outcomes
The course provides knowledge of the main techniques of imaging the central nervous system, with a special focus on MRI, presenting the basics of both conventional imaging and advanced MRI techniques in different neurological pathologies. The course will cover all aspects of neuroimaging that are and will be topics of an AI application: starting from acquisition techniques (e.g., speeding up acquisitions, optimization of image quality), meaning and post-processing of conventional and advanced images (Diffusion based imaging, perfusion, spectroscopy, functional MRI, quantitative mapping), storage and sharing of datasets, image segmentation, workflow optimization, data reporting and also the diagnostic qualitative interpretation of the images in the clinical medical routine.
The course provides basic knowledge of the main neurological pathologies of the and their clinical and research challenges with potential contribution of AI.
Students will test their knowledge and skills in applying AI neuroimaging approaches using dedicated open-course or company-owned AI imaging tools through hand-on practice laboratories on MRI datasets.
The course provides basic knowledge of the main neurological pathologies of the and their clinical and research challenges with potential contribution of AI.
Students will test their knowledge and skills in applying AI neuroimaging approaches using dedicated open-course or company-owned AI imaging tools through hand-on practice laboratories on MRI datasets.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Lesson period
Second semester
Course syllabus
Both conventional and advanced radiological techniques in the neuroradiological field to provide an overview of the real clinical-radiological needs.
Clinical needs in the application of AI in daily use, defining the necessary workflows and the currently most relevant uses.
Application of clinical and research examples of neuroAI on specific cases
Clinical needs in the application of AI in daily use, defining the necessary workflows and the currently most relevant uses.
Application of clinical and research examples of neuroAI on specific cases
Prerequisites for admission
Knowledge of the physical basis of the radiological equipment, therefore of the mode of operation and use of computerized tomography, magnetic resonance imaging and X-ray equipment
Teaching methods
Frontal lessons , also with didattic cases
Application of clinical and research examples of neuroAI on specific cases
Application of clinical and research examples of neuroAI on specific cases
Teaching Resources
Course slides
Literature provided
Literature provided
Assessment methods and Criteria
Written exam with multiple and open answers. During the exam, the level of achievement of the training objectives of the course will be assessed. The final evaluation is based on the degree of depth and understanding of the topics presented. The evaluation will take into account the ability to communicate in writing and the use of appropriate scientific language. The vote will be unique.
FIS/07 - APPLIED PHYSICS - University credits: 3
MED/37 - NEURORADIOLOGY - University credits: 3
MED/37 - NEURORADIOLOGY - University credits: 3
Lessons: 48 hours
Professors:
Bellazzi Riccardo, Caverzasi Eduardo, Pichiecchio Anna, Postuma Ian