Medical Image Reconstruction


[Under Construction]

Grad Student Info
Class Info
Useful Links


Administrative Information

Class meeting time: Sunday starting at 4:00p - BME 3201 auditorium

Attendance is required for all lectures - Attending 85% of classes is required to pass the course NO EXCEPTIONS

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Intended Learning Objectives (ILOs)

To build a strong theoretical and practical implementation skills for medical image reconstruction. This is an advanced course and therefore will cover advanced topics immediately from the start.

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A number of sources from textbooks and research papers will be handed out for each lecture

  1. Handout #1: Material for Mathematical lectures 1 and 2

  2. Handout #2: Material for Mathematical Background lectures 3 and 4

  3. Handout #3: Reference for interlaced Fourier transform

  4. Handout #4: Material for Shepp-Logan phantom

  5. Handout #5: Material for Partial Fourier Methods

  6. Handout #6: Material for Conventional Gridding

  7. Handout #7: Material for Matrix Equation Gridding

  8. Handout #8: Material for Motion Estimation

  9. Handout #9: Reference for ultrasound imaging.

  10. Handout #10: Reference for synthetic aperture ultrasound imaging.

  11. Handout #11: Material for Tomography lecture.

  12. Handout #12: Material for Super-Resolution lecture.



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Grading Policy

100% on Class Projects and Final Project

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Course Topics to be Covered (Tentative)

  1. Mathematical basis
  2. Image reconstruction in CT/MRI/ultrasound imaging
  3. Compressed sensing theory and applications

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Lecture Presentations

  1. Lecture presentation #1

  2. Lecture presentation #2

  3. Lecture presentation #3

  4. Lecture presentation #4

  5. Lecture presentation #5

  6. Lecture presentation #6

  7. Lecture presentation #7


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Data Samples


1. Radial sampling of the analytical Shepp-Logan phantom

(text file, target image size should be128x128, each row is one sample, four numbers on each row for normalized (kx,ky) and their complex k-space data value, format: "kx value"  "ky value"  "real part of k-space" "imaginary part of k-space").


2. Full k-space data of a real image of transverse slice of a normal human brain for partial Fourier reconstruction data.      (Download sample Matlab reading program here)

(text file, k-space size: 256x256, each row is one sample with real and imaginary parts, 2D k-space written in sequence row by row)


3. Synthetic aperture ultrasound data sets

(each is written in binary form - information about organization of data is included inside each archive - choose one archive only to work on)


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Homework/Project Grades


List of Grades (HW1)



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Last modified: May 11, 2012