Medical Image Reconstruction

Administrative Information

Class meeting time: Tuesdays 6:00p-8:00p @ BME 3201 auditorium

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

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.

References

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

1. Handout #1: Material for mathematical background lecture 1

2. Handout #2: Material for mathematical background lecture 2

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.

Grading Policy

100% on Class Projects

Course Topics to be Covered (Tentative)

1. Mathematical basis (Matrix computations and Fourier optics)
2. Image reconstruction in CT/MRI/ultrasound imaging
3. Compressed sensing theory and applications (if time allows)

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

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)

Homework/Project Grades

To be announced.