Boston University

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Hidden Worlds - Introduction to Subsurface Sensing and Imaging (ENG EK 130 B0/C0 )
Prof. Michael Ruane (Undergraduate Course)

Engineers often face the problem of detecting and imaging objects that are hidden underground or underwater, or embedded in the human body. A number of probes are possible, including optical beams, x-rays, ultrasonic waves, or electromagnetic waves. Sensors are used to detect the transmitted, reflected, or scattered waves, and the data are used to extract information about the hidden objects. Examples of applications include detecting tumors under human tissue, locating mines under ground, or imaging fish under water. Standard techniques include optical microscopy, x-ray radiography, ultrasonic imaging, magnetic resonance imaging (MRI), computer assisted tomography (CAT), etc. The designers of these systems must understand the physical models that describe the probing and sensing processes, before they can develop the necessary algorithms or software for solving the puzzle -- computing the image distribution and identifying the target.

In this course module (13 meetings over 6 weeks), you will learn the basic ideas behind probing hidden targets using various waves, including the basic principles of the more prevalent imaging techniques. You will develop the concept of modeling and learn about methods of reconstruction from measured data. Simplified test projects will be demonstrated in the 'High Tech Tools and Toys' Lab.

Day(s) and Time(s):	Lectures – Tuesday - Thursday - 2:00pm to 4:00pm
	Labs – Friday 9-10 or 10-11

Distance Learning: NO - Available to BU and NU Students Only

This is a ‘module’ in a two-module, 4 credit course. NU students would need to take a second module, or (possibly) enroll for only 2 credits.

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Biomedical Optics and BioPhotonics (SC765 and BE765)
Prof. Irving J. Bigio (Graduate Course)

Biomedical optics (or Biophotonics) is a newly developing field, dealing with the application of optical science and technology to biomedical problems, including clinical applications. There is no formal text yet available for this topic, although the recommended reference text on optics will prove valuable since we will concentrate on the optical science and engineering, as applied to biomedical problems, covering only those aspects of the biology itself that are necessary to understand the purpose of the application.

The course is modeled in the manner of a modified “journal club.” The instructor will provide lectures introducing the underlying principles of various current research areas in biomedical optics. For each area a publication from the recent literature will be chosen as illustrative of that topical area, and for each publication one student will be assigned to prepare an informal presentation, with overhead slides or PowerPoint, reviewing for the class the underlying principles of that paper and outlining the research results. During the first few class sessions, before the publication reviews begin, the instructor will provide a broad background on the optical properties of tissues and matters of nomenclature.

Day(s) and Time(s):	Lectures - Tues/Thurs, 4:00 - 6:00 PM
	starting September 3, 2003.

Prerequisites: Prior course in optics/photonics is highly recommended, and some cellular biology or physiology is also useful.

Distance Learning: NO - Available to BU and NU Students Only

Northeastern University

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Introduction to Inverse Problems (ECE G398)
Prof. Eric Miller (Graduate Course)

The desire to extract information regarding the structure of a signal or image given a noise corrupted, "blurred'' version of the original is a common goal in many fields of engineering and the applied sciences including geophysical exploration, medical imaging, non-destructive testing, and radar signal processing. For example, a common signal and image processing problem is that of deconvolution where one observes a filtered version of a signal in additive noise and seeks to recover the uncorrupted original. The use of computer aided tomography and magnetic resonance imaging for medical diagnoses has lead to the development of algorithms for the inversion of the Radon transform. Probing the subsurface of the earth for oil deposits, minerals, and even buried landmines requires the processing of scattered acoustic or electromagnetic energy to ascertain the space varying nature of the earth’s density or electrical properties, changes in which are associated with the sought-after quantities.

While common enough in practice, problem such as these are notoriously difficult to solve. Most inverse problems are characterized by an unusually high sensitivity to perturbations in the data so that a small change in the measurements results in wildly nonphysical changes in the recovered signal. Understanding the origins of such sensitivity and designing algorithms for overcoming these difficulties form the backbone of much of the work in this fascinating area of study. This quarter will be devoted to a comprehensive study of these and related issues. Using a rigorous mathematical framework, we shall develop a number of problems associated with “real world” applications including deconvolution, tomography, and linearized inverse scattering. A clear analysis of the sensitivity issue (called ill-posedness) will be presented discussed. Techniques for stabilizing these problems including the use of a pseudo-inverse and appropriate regularization procedures will occupy much of the remainder of the quarter.

The work in this class will center on a collection of about four bi-weekly problem sets emphasizing analytical as well as computational (i.e. Matlab) problems. Additionally, there will be a final project, and perhaps a midterm and/or final exam.

Prerequisites:

1. Strong facility with linear vector space ideas especially for finite dimensional cases, but some familiarity with Hilbert space ideas would not hurt. From linear algebra in particular concepts including as eigen-avalysis, singular value analysis, range, nullspace, and transpose are very important.

2. Fourier analysis including Fourier transform, discrete time Fourier transform, discrete Fourier transform, Fourier series, fast Fourier transform, and convolution. Comfort with doing all of this in multiple dimensions is a plus, but not required.

3. It would be helpful to have some working knowledge of probability and random processes. Notions of statistical independence, Gaussian random vectors, Poisson random variables, Bayes rule, expected values, covariance analysis may arise from time to time.

4. It would also be very helpful to have some interest in, recollection of, or a previous class dealing with some form of wave-based physics. A good undergraduate or introductory graduate class in electricity and magnetism or acoustics would be ideal. Basic familiarity with the partial differential equations (Laplace, Poisson, Helmholtz) encountered in these fields would be of use.

5. Fluency with Matlab or your own favorite programming language as there will be a good deal of computational exercises associated with the class.

Day(s) and Time(s): Monday/Wednesday 9:50AM - 11:30AM

Location: 408 Ell Building, NU Campus

Distance Learning: YES - Available to BU, NU, RPI, and UPRM Students.

Additional Information: Course Website (this will be continuously updated as the term gets closer).

Rensselaer Polytechnic Institute

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Biological Image Analysis
Prof. Badri Roysam

Survey of image analysis applications in biology, biotechnology, and medicine; Introduction to biological microscopy and selected medical imaging systems; Image reconstruction and preprocessing; Grayscale and geometric corrections; adaptive image segmentation; blob analysis, cell/colony counting, and cell morphometry; vessel and neuron tracing algorithms, with applications to neurobiology and medicine; feature extraction, pattern analysis, cluster analysis and classification; image registration algorithms with applications to mosaicing, spatial referencing, motion estimation, and change detection.

Prerequisites: A course in programming. Exposure to basic statistical concepts is desirable.

This class is open to a variety of students ranging from Biology majors to Engineering and Computing majors. Each student will be required to perform some image analysis programming using MATLAB, so some programming background is necessary. Individuals, or teams of two students each, can do the course projects. Cross-disciplinary teaming is encouraged, for example a biologist teaming with a computer scientist. Expectations on these projects will vary based on the student's background.

Day(s) and Time(s): Lectures -To Be Determined

Distance Learning: YES - Available to BU, NU, RPI, and UPRM Students

Additional Information: TBA

 

Boston University

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Image Reconstruction and Restoration (SC717)
Prof. W. Clem Karl

Principles and methods of reconstructing images and estimating multidimensional fields from indirect and noisy data; general deterministic (variational) and stochastic (Bayesian) techniques of regularizing ill-posed inverse problems; relationship of problem structure (data and models) to computational efficiency; impact of typically large image processing problems on viability of solution methods; problems imaging and computer vision including tomography and surface reconstruction. Computer assignments.

Prerequisites: Stochastic Processes(SC505) and Introduction to Digital Signal Processing(SC416)

Day(s) and Time(s): Mondays & Wednesdays: 12:00 - 2:00 PM

Next Offered: Spring 2003

Distance Learning: NO - Available to BU and NU Students Only

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Recursive Estimation and Optimal Filtering (ENG SC702)
Prof. David Castañon (Graduate Course)

State-space theory of dynamic estimation in discrete and continuous time. Linear state-space models driven by white noise, Kalman filtering and its properties, optimal smoothing, nonlinear filtering, extended and second-order Kalman filters, and sequential detection. Applications to radar, sonar, and optimal multi-target tracking, parameter identification.

Prerequisites: Stochastic Processes

Next Offered: Spring 2003

Distance Learning: NO - Available to BU and NU Students Only

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Subsurface Sensing and Imaging Systems (ENG SC500 A5)
Prof. Bahaa Saleh (Seniors and Graduate Course)

Course Objectives:

· To introduce the field of subsurface sensing and imaging, its methods, applications, and research.
· To develop models for significant SSIS modalities, including 2D and 3D imaging, transmissive and reflective systems, and various scanning configurations.
· To develop analytic and numerical methods for image reconstruction and inversion, using real data sets from CenSSIS Testbeds and CenSSIS researchers.
· To prepare students for further graduate study or employment in SSIS in government and industry.

Prerequisites: Senior or graduate standing in ENG, PY, CH, MA, CS or related CenSSIS areas, including medical studies.

Next Offered: Spring 2003

Distance Learning: NO - Available to BU and NU Students Only

Rensselaer Polytechnic Institute

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Intro to Subsurface Sensing and Imaging Systems (ECSE-4963-01; CRN75865)
Dr. Kai E. Thomenius, Chief Technologist, Imaging Technologies, GE Corporate R&D Center

Engineers are often faced with the problem of sensing and imaging objects that are hidden under a surface. For example, detecting tumors using laser scanning microscopy, locating underground mines using radio waves, imaging infants using ultrasound, detecting cracks in machine parts and bridges, and medical imaging by x-ray radiography, magnetic resonance imaging (MRI), and computer assisted tomography (CAT). This course will introduce the student to the basics of subsurface sensing and imaging:

•Properties of probes such as optical beams, x-rays, ultrasonic waves, and electromagnetic waves.
•How the probes interact with media-transmission, reflection, scattering, diffusion
•Sensors for detecting subsurface signals
•Extracting information from subsurface signals using multi-view tomography (MVT), Localized probing and mosaicing (LPM), and multi-spectral discrimination (MSD).

Prerequisites: ECSE-2100 (Fields and Waves I), ECSE 2410 (Signals and Systems)

Next Offered: Spring 2003

Day(s) and Time(s): Tuesdays & Fridays: 12:00 - 1:00 PM

Distance Learning: NO

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Mathematical Methods in Medical (MATH-6792)
Prof. David Isaacson

This course focuses on the application of mathematics to obtaining medical images, including computed tomography (CT) and magnetic resonance imaging (MRI). Topics covered include the explanation of the physics involved and the derivation of the partial differential equations, integral equations and transforms used by algorithms for the reconstruction of images. The design of pulse sequences, questions of image resolution, and current problems in diffusion tensor imaging and homogenization will be discussed.

Prerequisites: working knowledge of partial differential equations.

Next Offered: Spring 2003

Day(s) and Time(s): Tuesdays & Fridays: 10:00 - 11:50 AM

Distance Learning: NO

University of Puerto Rico at Mayagüez

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Microwave Remote Sensing (INEL6069)
Prof. Sandra Cruz-Pol

Explore the interaction of electromagnetic waves with natural (clouds, rain, snow) and artificial targets. It also provides an introduction to radiometry principles (e.g. Planck's Law) and to active and passive instrumentation used in remote sensing such as radiometers, radars and altimeters, with emphasis on passive systems.

Prerequisites: Electromagnetics II (INEL 4152)

Next Offered: Tentatively Spring 2003

Distance Learning: NO - This course is currently taught in Spanish but if there was enough interest from BU, NU and RPI students, it could possibly be taught in English as the textbooks and notes are in English.

Boston University

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Hidden Worlds - Introduction to Subsurface Sensing and Imaging (ENG EK 130 B0/C0 )
Prof. Michael Ruane (Undergraduate Course)

Engineers often face the problem of detecting and imaging objects that are hidden underground or underwater, or embedded in the human body. A number of probes are possible, including optical beams, x-rays, ultrasonic waves, or electromagnetic waves. Sensors are used to detect the transmitted, reflected, or scattered waves, and the data are used to extract information about the hidden objects. Examples of applications include detecting tumors under human tissue, locating mines under ground, or imaging fish under water. Standard techniques include optical microscopy, x-ray radiography, ultrasonic imaging, magnetic resonance imaging (MRI), computer assisted tomography (CAT), etc. The designers of these systems must understand the physical models that describe the probing and sensing processes, before they can develop the necessary algorithms or software for solving the puzzle -- computing the image distribution and identifying the target.

In this course module (13 meetings over 6 weeks), you will learn the basic ideas behind probing hidden targets using various waves, including the basic principles of the more prevalent imaging techniques. You will develop the concept of modeling and learn about methods of reconstruction from measured data. Simplified test projects will be demonstrated in the 'High Tech Tools and Toys' Lab.

Prerequisites: ECE 1333 (Discrete Linear Systems), CE 1360 (Electromagnetic Fields and Waves), and MTH 1230 (Linear Algebra for Engineers), or by permission of instructor.

Day(s) and Time(s):	Lectures – Tuesday - Thursday - 2:00pm to 4:00pm
	Labs – Friday 9-10 or 10-11

Distance Learning: NO - Available to BU and NU Students Only

This is a ‘module’ in a two-module, 4 credit course. NU students would need to take a second module, or (possibly) enroll for only 2 credits.

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THIS COURSE IS NOT BEING OFFERED IN FALL 2003

SC503 IS A ‘SPECIAL TOPICS’ COURSE WITH A NEW FOCUS EACH YEAR.

Diagnostic Ultrasound Imaging: Inside Out (AM503)
Prof. Thomas Szabo (Graduate Course)

Diagnostic ultrasound is the second leading imaging modality worldwide behind digital/film x-rays. This course introduces the physics, signal and image processing of diagnostic ultrasound.

Topics: elastic wave propagation and scattering in tissues, absorption, piezoelectric transducers, array beamforming, principles of ultrasound imaging systems, harmonic imaging, acoustic measurements, and ultrasound-induced bioeffects.

Prerequisites: Calculus, partial differential equations, knowledge of elementary waves; familiarity with Fourier or Laplace Transforms helpful. Day(s) and Time(s): To Be Determined Distance Learning: NO - Available to BU and NU Students Only

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Biomedical Optics and BioPhotonics (SC765 and BE765)
Prof. Irving J. Bigio (Graduate Course)

Biomedical optics (or Biophotonics) is a newly developing field, dealing with the application of optical science and technology to biomedical problems, including clinical applications. There is no formal text yet available for this topic, although the recommended reference text on optics will prove valuable since we will concentrate on the optical science and engineering, as applied to biomedical problems, covering only those aspects of the biology itself that are necessary to understand the purpose of the application.

The course is modeled in the manner of a modified “journal club.” The instructor will provide lectures introducing the underlying principles of various current research areas in biomedical optics. For each area a publication from the recent literature will be chosen as illustrative of that topical area, and for each publication one student will be assigned to prepare an informal presentation, with overhead slides or PowerPoint, reviewing for the class the underlying principles of that paper and outlining the research results. During the first few class sessions, before the publication reviews begin, the instructor will provide a broad background on the optical properties of tissues and matters of nomenclature.

Day(s) and Time(s):	Lectures - Tues/Thurs, 4:00 - 6:00 PM
	starting September 3, 2002.

Prerequisites: Prior course in optics/photonics is highly recommended, and some cellular biology or physiology is also useful.

Distance Learning: NO - Available to BU and NU Students Only

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Northeastern University

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Introduction to Inverse Problems (ECE 3694) (NTU Course Number - CC 768-F)
Prof. Eric Miller (Graduate Course)

The desire to extract information regarding the structure of a signal or image given a noise corrupted, "blurred'' version of the original is a common goal in many fields of engineering and the applied sciences including geophysical exploration, medical imaging, non-destructive testing, and radar signal processing. For example, a common signal and image processing problem is that of deconvolution where one observes a filtered version of a signal in additive noise and seeks to recover the uncorrupted original. The use of computer aided tomography and magnetic resonance imaging for medical diagnoses has lead to the development of algorithms for the inversion of the Radon transform. Probing the subsurface of the earth for oil deposits, minerals, and even buried landmines requires the processing of scattered acoustic or electromagnetic energy to ascertain the space varying nature of the earth’s density or electrical properties, changes in which are associated with the sought-after quantities.

While common enough in practice, problem such as these are notoriously difficult to solve. Most inverse problems are characterized by an unusually high sensitivity to perturbations in the data so that a small change in the measurements results in wildly nonphysical changes in the recovered signal. Understanding the origins of such sensitivity and designing algorithms for overcoming these difficulties form the backbone of much of the work in this fascinating area of study. ECE 3538 this quarter will be devoted to a comprehensive study of these and related issues. Using a rigorous mathematical framework, we shall develop a number of problems associated with “real world” applications including deconvolution, tomography, and linearized inverse scattering. A clear analysis of the sensitivity issue (called ill-posedness) will be presented discussed. Techniques for stabilizing these problems including the use of a pseudo-inverse and appropriate regularization procedures will occupy much of the remainder of the quarter.

The work in this class will center on a collection of about four bi-weekly problem sets emphasizing analytical as well as computational (i.e. Matlab) problems. Additionally, there will be a final project, and perhaps a midterm and/or final exam.

Prerequisites:

1. Strong facility with linear vector space ideas especially for finite dimensional cases, but some familiarity with Hilbert space ideas would not hurt. From linear algebra in particular concepts including as eigen-avalysis, singular value analysis, range, nullspace, and transpose are very important.

2. Fourier analysis including Fourier transform, discrete time Fourier transform, discrete Fourier transform, Fourier series, fast Fourier transform, and convolution. Comfort with doing all of this in multiple dimensions is a plus, but not required.

3. It would be helpful to have some working knowledge of probability and random processes. Notions of statistical independence, Gaussian random vectors, Poisson random variables, Bayes rule, expected values, covariance analysis may arise from time to time.

4. It would also be very helpful to have some interest in, recollection of, or a previous class dealing with some form of wave-based physics. A good undergraduate or introductory graduate class in electricity and magnetism or acoustics would be ideal. Basic familiarity with the partial differential equations (Laplace, Poisson, Helmholtz) encountered in these fields would be of use.

5. Fluency with Matlab or your own favorite programming language as there will be a good deal of computational exercises associated with the class.

Day(s) and Time(s): Monday/Wednesday 1:30pm-3:10pm

Location: 410 Ell Building, NU Campus

Distance Learning: YES - Available to BU, NU, RPI, and UPRM Students. Students may register for this course with National Technological University (NTU) by going to http://www.ntu.edu/ . The NTU course number is CC 768-F

Additional Information: Course Website (this will be continuously updated as the term gets closer).

Rensselaer Polytechnic Institute

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Biological Image Analysis (ECSE6963)
Prof. Badri Roysam

Survey of image analysis applications in biology, biotechnology, and medicine; Introduction to biological microscopy and selected medical imaging systems; Image reconstruction and preprocessing; Grayscale and geometric corrections; adaptive image segmentation; blob analysis, cell/colony counting, and cell morphometry; vessel and neuron tracing algorithms, with applications to neurobiology and medicine; feature extraction, pattern analysis, cluster analysis and classification; image registration algorithms with applications to mosaicing, spatial referencing, motion estimation, and change detection.

Prerequisites: A course in programming. Exposure to basic statistical concepts is desirable.

This class is open to a variety of students ranging from Biology majors to Engineering and Computing majors. Each student will be required to perform some image analysis programming using MATLAB, so some programming background is necessary. Individuals, or teams of two students each, can do the course projects. Cross-disciplinary teaming is encouraged, for example a biologist teaming with a computer scientist. Expectations on these projects will vary based on the student's background.

Day(s) and Time(s): Lectures -Tues/Fri 12:00pm to 1:20pm

Distance Learning: YES - Available to BU, NU, RPI, and UPRM Students

Additional Information: Course Information   Course Website