Part  Medical Images
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 constituted 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.