HST.582J/6.555J/16.456J
Lecture Topics
(grouped by topic; see calendar for order of presentation)

A.  Biomedical Signals and Images

ECG: Cardiac electrophysiology, relation of electrocardiogram (ECG) components to cardiac events, clinical applications.

Speech Signals: The source-filter model of speech production, spectrographic analysis of speech.

Speech Coding: Analysis-synthesis systems, channel vocoders, linear prediction of speech, linear prediction vocoders

Imaging Modalities: Survey of major modalities for medical imaging: ultrasound, X-ray, CT, MRI, PET, and SPECT.

MR Phyiscs: Physics and signal processing for magnetic resonance imaging.

Efficient Data Acquisition in MRI: Current research topics including parallel reception and parallel transmission.

B.  Deterministic Signal and Image Processing

Data Acquisition: Sampling in time, aliasing, interpolation, and quantization.

Digital Filtering: Difference equations, FIR and IIR filters, basic properties of discrete-time systems, convolution.

DTFT: The discrete-time Fourier transform and its properties. FIR filter design using windows.

DFT: The discrete Fourier transform and its properties, the fast Fourier transform (FFT), the overlap-save algorithm, digital filtering of continuous-time signals.

Sampling Revisited: Sampling and aliasing in time and frequency, spectral analysis.

Image processing I: Extension of filtering and Fourier methods to 2-D signals and systems.

Image processing II: Interpolation, noise reduction methods, edge detection, homomorphic filtering.

Image Registration I: Rigid and non-rigid transformations, objective functions.

Image Registration II: Joint entropy, optimization methods.

C.  Probability and Random Signals

Random signals I: Time averages, ensemble averages, autocorrelation functions, crosscorrelation functions.

Random signals II: Random signals and linear systems, power spectra, cross spectra, Wiener filters.

Blind source separation: Use of principal component analysis (PCA) and independent component analysis (ICA) for filtering.

Hypothesis Testing I, II, and III: Review of probability theory, Bayes' rule, Bayesian hypothesis testing, risk adjusted classifiers, Receiver Operating Characteristic curves, Neyman-Pearson binary hypothesis testing, application to image segmentation and MRI image reconstruction, the E-M algorithm.

Adaptive Filtering I: Non-stationarity in biomedical signals, typical applications of adaptive filtering, review of the Wiener filter, least mean squares (LMS) algorithm.

Adaptive Filtering II: Recursive least squares (RLS) algorithm, Kalman filter, strengths and weaknesses of each adaptive filter.