• 096280 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES [2] - BIOE 421

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.

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.

Part [2] Medical Images

Introduction to Medical Imaging modalities

Introduction to image classification. Image generation: Point Spread Function, Modulation Transfer Function, Spatial and amplitude resolution, Contrast, Signal to Noise ratio. Fourier Transform in the 2D spatial domain. Fotonic images. 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. Artifacts and their removal. 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 image analysis tools. Application to different medical images. Homework exercises with evaluation. Objective of the Lab is teaching how to work on examples of medical image analysis by the most used tools in space and in spatial frequency domain. Students will learn how to preprocess biomedical images, 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.

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 characteristics. 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

It is advisable to be aware of the fundamentals of digital signal processing.

Assessment

The final score of the exam will be based on a written exam consisting of both open-ended questions and exercises covering the image processing part of the Course and on a project developed in the Laboratory part of the Course. The project will be presented to the instructor at the end of the course. Practical assignments to be implemented in Matlab (based on topics covered during the Labs) are not mandatory but constitutes an additional opportunity to increment final score.

The score of the written exam, the project part and the practical assessments will be summed to compute the total score. 30 cum laude will be assigned when the total score is higher than 16.

The written exam will assign up to 12 points and will be considered sufficient when the score will be equal or higher than 7. The project part will assign up to 3 points. The optional Lab assignments (2) will assign up to 2 points. Students can take the written part at any exam session during the year.  No mid-term assessments will be scheduled.

Lesson texts can be dowloaded from Beep Platform. Other material is available and some other will be added

(terza Edizione)