Master's degree in Medical Bioinformatics

Master's degree in Medical Bioinformatics

Biomedical image processing (2019/2020)

Course code
4S004554
Credits
6
Coordinator
Alessandro Daducci
Academic sector
INF/01 - INFORMATICS
Language of instruction
English

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Teaching is organised as follows:
Activity Credits Period Academic staff
Teoria 4 I semestre Alessandro Daducci
Laboratorio 2 I semestre Alessandro Daducci

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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.

Syllabus

(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

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

Assessment methods and criteria

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.





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