096281 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES

Objectives

The Course is organized into two parts: the first one [1] is dedicated to Biomedical Signal Analysis.

The objective is to introduce the most used methods of information processing for biomedical signals and to describe some significant applications in Medicine and Biology, in both physiological studies and in clinical applications for diagnosis, therapy and rehabilitation.

The second part of the Course [2] deals with Medical Images.

The objective is to provide basic concepts for their characterization, illustrating clinical and diagnostic problems and finally to introduce the principles on which some techniques for their processing and reconstruction are based.

The Course is composed by frontal lessons, exercise sessions, seminars and laboratory exercises aiming at deeply analyzing topics related to signal and image processing making use of MatLab Tools. The attendance to the Course is strongly advised.

Course Content

Part [1] Biomedical Signal Processing.

Programme of lessons and exercises

Introduction to biomedical signal processing. General block diagram of biomedical signal processing operations. Recall of analog and digital filters.

Cardiovascular System. ECG signal: main superimposed noises.  Most significant configurations from clinical standpoint. High resolution ECG. Ventricular late potentials: pathophysiological aspects and processing methods. Study of Autonomic Nervous System (ANS) by means of heart rate variability  signal processing in  short- and long-term bases. Pathophysiological aspects: neural control in heart rate and arterial blood pressure. Fetal ECG: processing methods and enhancement of useful clinical parameters. Fetal monitoring. Arterial blood pressure signal: detection systems and main clinical parameters. Interaction models among signals related to autonomic nervous system: ECG, arterial blood pressure and respiration; open- and closed-loop models. Pathophysiological interpretations.

Diagnostic Classification. Classification methods. Principal Component Analysis (PCA):  general introduction and applications. Entropy Analysis: methods and applications.

Neurosensorial System. EEG (electroencephalographic) signal analysis, evoked potentials (EPs)  and event-related potentials (ERPs). Review on traditional processing methods with main applications in clinical and research environments: cerebral activity detected on the scalp, at cortical level and in deep brain stimulation (DBS).

Signal processing and parametric identification. Time series analysis detected from biological signals. Examples in ECG, EEG, EP in pre-processing, filtering, prediction, extraction and estimation of parameters. Diagnostic classification. Deterministic and stochastic identification. Review on stochastic identification approaches. Model families, in particular AR/MA/ARMA (autoregressive and moving average) and models with exogenous input X. Parametric spectral analysis, including backward and forward methods, maximum entropy methods, Pisarenko, Prony, etc. Comparison with the traditional non-parametric techniques: various examples on heart rate variability signals, EEGs and EPs.

Laboratory activities  Exercises with Matlab implementing signal analysis tools. Application to biomedical signals. Home work execises with evaluation. Students will develop knowledge about the most important methods and applications for biomedical signal processing and analysis working on biomedical signal samples.

Expected Learning Outcomes  The formative objective is that the student takes possession of the signal processing methods in biomedical signals. At the end they should be able to work autonomously in different practical cases also different from those addressed in the lessons and/or in the laboratory practical exercises.

Prerequisites: No mandatory prerequisites; it is advisable to be aware of the fundamentals of digital signal processing.

Part [1] Biomedical Signal processing (short summary)

The course is intended to recall general signal processing aspects, to introduce new methods beyond those considered in the bachelor degree and examine various clinical and research application fields. Methods. From deterministic filtering to stochastic parametric analysis: mono and multi-variate AR/MA/ARMA models and non parametric as well as parametric spectral analysis. Principal component analysis. Entropy in signal processing. Applications. Automatic analysis and classification of the electrocardiographic signal (ECG). The autonomic nervous system, cardiovascular variability signals, and cardiorespiratory interactions. Fetal ECG signal. High resolution ECG and late ventricular potentials. The central nervous system: processing of the electroencephalographic signal (EEG) and of evoked (EP) and event-related potentials (ERP).

Part [2] Medical Images

Objective

Provide basic concepts on biomedical images. Introduce the related clinical and diagnostic problems and illustrate principles on which techniques for processing and reconstruction are based on.

The Course is composed by frontal lessons, exercise sessions and laboratory exercises. The attendance of the Course is strongly recommended both at frontal lessons and exercise sessions whose content is also part of the evaluation procedure.

 Programme of lessons and exercises

Introduction to Medical Imaging modalities

 Introduction to image classification. Image formation: Point Spread Function, Modulation Transfer Function, Spatial and amplitude resolution, Contrast, Signal to Noise ratio. Fourier Transform in the 2D spatial domain. Fotonic images. Artifacts and their removal. Human perception and Biomedical Images. Psychophysics Weber's Law. Perception thresholds for human vision.

 Basic Principles for image processing and reconstruction. Sampling and quantization in the spatial domain. Discrete Fourier Transform in the 2D space domain. Convolution 2D. Image quality enhancement: spatial filters, equalization. Geometric operators. Tomographic reconstruction. Numeric approach to tomographic reconstruction: Radon transform, Sinogram, back-projection and blurring. Filtered back- projection. Ramp high pass filter, Central Slice Theorem.

 X ray Imaging: Basic radiography, X-ray generation, radiation interaction with matter, dose. X-ray instrumentation. Digital Radiology. Mammography, Angiographic X-Ray imaging. Computed Tomography (CT): applications, history and evolution of instrumentation, spiral CT. Tomographic reconstruction application to CT images.

 Nuclear medicine Imaging  Radioactivity. Nuclear Emission images. General principles Single Photon Emission Tomography (SPECT). Detectors and data acquisition system. Positron emission Tomography (PET). Spatial resolution. Detectors. Instrumentation. Dual mode PET-CT scanners.

Magnetic Resonance Imaging (MRI): Principles, Instrumentation, Pulse Sequences, T1 and T2 contrast, image generation. FMRI.

Ultrasound Imaging: Principles, echography and doppler, other contrast agents.

Laboratory activities  Exercises with Matlab implementing imagel analysis tools. Application to different medical images. Home work execises with evaluation. Objective of the Lab is teaching how to work on examples of medical image analysys by the most used tools in space and in spatial frequency domain. Students will learn how to preprocess biomedical imagesl, how to extract information which could be of pathophysiological importance. Furthermore practical activity will introduce students to general criteria to work with medical image analysis even different from those seen in the Lab

Expected Learning Outcomes  During the Course the student will acquire methodological knowledge about Medical Images as well as applications and their main processing tools  Students will  be able to work on medical images with analysis and processing Matlab tools. Moreover they should be able to work autonomously in application problems different from those addressed in the lessons and/or in the practical exercises.

Prerequisites: No mandatory prerequisites; it is advisable to be aware of the fundamentals of digital signal processing.

Part [2] Medical Images (short summary)

Introduction to the main imaging diagnostic systems: analysis of the relevant features: spatial resolution, contrast, signal to noise ratio, artifacts. Basic principles for image processing and reconstruction: 2D Fourier transform, sampling and quantization, enhancement techniques (spatial filtering, equalization), geometric operations tomographic reconstruction. Imaging modalities and their charateristics. X ray images: planar radiographs, digital radiology, angiography, transmission tomography (CT). Emission images with radiotracers: scintigraphy and gamma-camera, emission tomography (SPECT and PET). Magnetic resonance imaging (MRI): T1 and T2 contrast, construction of MRI (basic acquisition sequences, frequency and phase encoding), functional MRI (brief remark). Ultrasound (US) imaging: echography, doppler, other contrast means

Notes on Evaluation Modalities

The evaluation will be given on the basis of a written exam, dealing with the topics of Lessons and Exercise hours of the two [1]+[2] (Biomedical Signals and Images) integrant parts of the Course. The Student may choose to take the exam relative to only one Part of the Course, provide that he/she obligates to make the other Part of the Course within the Sessions of the Academic Year of attendance.

The exam is passed when the Student gets a positive evaluation in both Parts. The final grade is the average of single grades obtained in the two parts.

If the Student decides not to accept a positive final (1+2) score, he/she has to repeat the 2 parts.