

Administrative InformationClass 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 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. ReferencesA number of sources from textbooks and research papers will be handed out for each lecture
Grading Policy100% on Class Projects and Final ProjectCourse Topics to be Covered (Tentative)
Lecture Presentations
Data Samples
1. Radial sampling of the analytical SheppLogan 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 kspace data value, format: "kx value" "ky value" "real part of kspace" "imaginary part of kspace").
2. Full kspace 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, kspace size: 256x256, each row is one sample with real and imaginary parts, 2D kspace 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

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