Courses related
to medical imaging
At IIT, our emphasis is on the engineering and physics behind
medical imaging technology. The following courses prepare the student
for an engineering career in medical imaging, or for medical school
with a specialization in radiology.
ECE 437 Digital Signal Processing I
Digital Signal Processing I Prerequisites: ECE 308. Discrete-time system analysis,
discrete convolution and correlation, Z-transforms. Realization and frequency
response of discrete-time systems, properties of analog filters, IIR filter
design, FIR filter design. Discrete Fourier Transforms. Applications of digital
signal processing. Credit will be given for either ECE 436 or ECE 437, but
not for both. (3-0-3) (P)
ECE 475 Random Phenomena in EE
Random Phenomena in Electrical Engineering Prerequisites: ECE 308. Basic axioms
of probability. Signals as random variables. Distribution and density functions.
Functions of random variables. Applications to the binary symmetric communication
channel, square-law and other nonlinear devices. The Gaussian, Poisson, and
other distributions. Application to photon counting. The signal-plus-noise
problem. The DC and AC value of signals: mean and variances. The meaning
of signal-to-noise ratio. Higher moments. Estimation of the mean and the
variance. Confidence intervals. Credit will be given for either ECE 475 or
MATH 475, but not for both. (3-0-3)
ECE 481 Image Processing
Image Processing Prerequisites: ECE 436 or ECE 437. Corequisites: ECE 475 or
MATH 475. Mathematical foundations of image processing, including two-dimensional
discrete Fourier transforms, circulant and block-circulant matrices. Digital
representation of images and basic color theory. Fundamentals and applications
of image enhancement, restoration, reconstruction, compression, and recognition.
(3-0-3) (P)
BME 500 Introduction to Biomedical Engineering
Introduction to the concepts and research in biomedical engineering. Provides
an overview of current biomedical engineering research areas, emphasis on
application of an engineering approach to medicine and physiology.
BME 501 Biomedical Instrumentation
Bioelectric phenomena, transducers, amplifiers. Processing of ECG, EMG and
EEG signals.
ECE 511 Analysis Random Signals
Prerequisites: ECE 308 and ECE 475 or MATH 475 Probability theory, including
discrete and continuous random variables, functions and transformations of
random variables. Random processes, including correlation and spectral analysis,
the Gaussian process and the response of linear systems to random processes.
(3-0-3)
BME 530 Inverse Problems in Biomedical Imaging
This course will introduce graduate students to the mathematical theory of
inverse problems. Concepts from functional analysis will be applied for understanding
and characterizing mathematical properties of inverse problems. This will
permit for the analysis of the stability and resolution of image reconstruction
algorithms for various existing and novel biomedical imaging systems. The
singular value decomposition (SVD) is introduced and applied for understanding
fundamental properties of imaging systems and reconstruction algorithms.
ECE 531 Linear System Theory
Prerequisites: ECE 308 Linear spaces and operators, single and multivariable
continuous dynamical systems, controllability and observability. Canonical
forms, irreducible realizations. Synthesis of compensators and observers. Composite
systems, elements of stability. (3-0-3)
BME 532 Medical Imaging Science
This course is an introduction to basic concepts in medical imaging, such as:
receiver operating characteristics, the rose model, point spread function
and transfer function, covariance and autocovariance, noise, filters, sampling,
aliasing, interpolation, and image registration.
BME 533 Biostatistics
This course is designed to cover the tools and techniques of modern statistics
with specific applications to biomedical and clinical research. Both parametric
and nonparametric analysis will be presented. Descriptive statistics will
be discussed although emphasis is on inferential statistics and experimental
design.
BME 535 Magnetic Resonance Imaging
This course is an introduction to magnetic resonance imaging (MRI). It includes
basic MR physics, the principles of selective excitation, signal detection,
and MR image reconstruction, different pulse sequences, MRI hardware, issues
on image quality and artifacts, and advanced MRI techniques.
BME 538 Neuroimaging
This course describes the use of different imgaging modalities to study brain
function and connectivity. The first part of the course deals with brain
function. It includes an introduction to energy metabolism in the brain,
cerebral blood flow, and brain activation. It continues with an introduction
to magnetic resonance imaging (MRI), perfusion-based fMRI, BOLD fMRI, fMRI
paradigm design and statistical analysis, introduction to positron emission
tomography (PET) and studying brain function with PET, introduction to magnetoencephalography
(MEG) and studying brain function with MEG. The second part of the course
deals with brain connectivity. It includes an introduction to diffusion tensor
MRI, explanation of the relationship between the diffusion properties of
tissue its structural characteristics, and white matter fiber tractography
techniques.
BME 540 Wave Physics and Applied Optics for Imaging Scientists
This course will introduce students to fundamental concepts in wave physics
and the analysis of optical wavefields. These principles will be utilized
for understanding existing and novel imaging methods that employ coherent
radiation. Solutions to inverse scattering and inverse source problems will
be derived and algorithmic realizations of the solutions will be developed.
Phase-contrast imaging techniques and X-ray imaging systems that employ coherent
radiation will be studied.
BME 542 Advanced Concepts in Image Science
This course will introduce students to advanced concepts in theoretical image
science. The topics covered will include deterministic descriptions of imaging
systems, stochastic descriptions of objects and images, statistical decision
theory, object assessment of image quality, and numerical model observers.
ECE 565 Computer Vision & Image Processing
Prerequisites: ECE 437, ECE 475 or MATH 475 Multidimensional sampling and discrete
Fourier transform; Image segmentation; Object boundary (edge) detection and
description; shape representation and extraction; Matching and recognition;
Image registration; Camera geometry and stereo imaging; Morphological processing;
Motion detection and compensation; Image modeling and transforms; Inverse
problems in image processing (restoration and reconstruction). (3-0-3)
ECE 566 Statistical Pattern Recognition
Prerequisites: ECE 511 Introduction to machine-learning approaches, starting
with fundamentals, such as principal component analysis, up through modern
kernel methods, such as support vector machines. Statistical resampling, clustering,
regression and classification. (3-0-3)
ECE 567 Statistical Signal Processing
STATISTICAL SIGNAL PROCESSING Prerequisites: ECE 511 and MATH 333 Detection
theory and hypothesis testing. Introduction to estimation theory. Properites
of estimators, Gauss-Markov theorem. Estimation of random variables: conditional
mean estimates, linear minimum mean-square estimation, orthogonality principle,
Wiener and Kalman filters. Adaptive filtering. LMS algorithm: properties and
applications. (3-0-3)
ECE 569 Digital Signal Processing II
DIGITAL SIGNAL PROCESSING II Prerequisites: ECE 437 and ECE 475 or MATH 475 Review
of basic DSP theory. Design of digital filters: FIR, IIR, frequency-transformation
methods, optimal methods. Discrete Fourier Transofrm (DFT) and Fast Fourier Transform
algorithms. Spectral estimation techniques, clasical and parametric techniques.
AR, MA, ARMA models. Estimation algorithms. Levinson, Durbin-Levinson and Burg's
algorithms. Eigenanalysis algorithms for spectral estimation. (3-0-3)
back to top
|
