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
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.
A number of sources from textbook chapters and research papers will be handed out for each lecture
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)
To be announced.
Please follow the links above to find out more information about me or links to my class web sites or publications.