CSE5392 (Spring 2012)
||ERB 129 | Monday and Wednesday 4:00-5:20 PM|
||Junzhou Huang | ERB 650 | Office hours: Monday and Wednesday 2:00-4:00 PM|
Two homework sets. (20%)
Class presentations. (20%)
Writing final reports (that will be posted in this website) or using alternate programming projects (a short presentation is required in class). (40%)
||Basic math and programming background, for undergraduate student, CSE 2320 are requested|
This course presents an introduction to the mathematical, physical, and computational principles underlying modern medical imaging informatics systems. It will cover fundamentals of magnetic resonance imaging (MRI), and functional MRI (fMRI), X-ray computed tomography (CT), ultrasonic imaging, as well as more general concepts required for these, such as linear systems theory, the Fourier Transform, wavelet Transform and the emerging compressive sensing techniques. Popular techniques for the registration, segmentation, and analysis of medical image data will also be discussed, as well as applications of medical imaging to image-guided intervention and healthcare.
The course is application-driven and includes topics in medical
imaging and medical informatics, such as different imaging techniques and advanced
image analysis tools in medical applications and healthcare. It will also include
selected hot topics relating to the emerging compressive sensing theory and
techniques. The course will provide the participants with a thorough background
in current research in these areas, as well as to promote greater awareness
and interaction between multiple research groups within the university. The
course material is well suited for students in computer science, biomedical
engineering, and electrical engineering. It will be of appropriate difficulty
for both undergraduate and graduate students.
1 (Due in class)
Homework 2 (Due in class)
MATLAB - Programming language
Writing code by yourself
Compressive Sensing (CS) aims to reconstruct signals and images
from significantly fewer measurements than were traditionally thought necessary.
Magnetic resonance imaging (MRI) is an essential medical imaging tool with an
inherently slow data acquisition process. Applying CS to MRI offers potentially
significant scan time reductions, with benefits for patients and health care
economics. MRI obeys two key requirements for successful application of CS:
1) medical imagery is naturally compressible by sparse coding in an appropriate
transform domain (e.g., by wavelet transform), and 2) MRI scanners naturally
acquire encoded samples, rather than direct pixel samples (e.g., in spatial-frequency
encoding). This project will help to review the requirements for the successful
application of compressed sensing to the MRI. The students emphasize on an intuitive
understanding of CS MRI by describing the CS reconstruction as a process of
interference cancellation in MRI. There is also an emphasis on the understanding
of the driving factors in applications, including limitations imposed by MRI
hardware and by clinical concerns.
Due on May 1st.
Please select one of the following topics as your final report:
1) Compressive Sensing;
2) Compressive Sensing MRI;
3) Medical image segmentation;
4) Medical image registration.
You should study and summarize several popular methods in your selected topic from above and talk about their advantages and disadvantages in different cases. No more than 8 pages.
Due on .
Mon Feb 06: Medical Image Modalities (Slides)
Wed Apr 25: Class Presentations: Sigicherla, Sudha Girish "COMPRESSIVE SAMPLING"; TEJASWINI CHODEY "Compressed Sensing MRI"; Cai Xiao "Efficient MR Image Reconstruction for Compressed MR Imaging"
Mon Apr 30: (Report Due on Class) Class Presentations: Ismat Jahan "Snakes: Active Contour Models"; Soheil Shafiee "Robust Face Recognition via Sparse Representation"); Chen Chen "Efficient MR Image Reconstruction for Compressed MR Imaging"
Snakes: Active Contour Models
Deformable Models in Medical Image Analysis: A Survey
Metamorphs: Deformable Shape and Appearance Models
Image Registration Methods: A Survey
Multimodality Image Registration by Maximization of Mutual Information
in Implicit Spaces using Information Theory and Free Form Deformations
Model-Based Compressive Sensing
Learning with Structured Sparsity
Compressive Sensing MRI:
Compressed Sensing MRI
Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging
Efficient MR Image Reconstruction for Compressed MR Imaging
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