My handwritten lecture notes are available below, if you are signed up for the class. These may change as we get to these lectures, but this is the initial plan.
Lecture 1: Course Introduction
Lecture 2: 1D Sampling and Reconstruction
Lecture 3: Introduction to non-uniform 2D reconstruction
Lecture 4: Gridding reconstruction and density estimation
Lecture 6: Inverse gridding, and least squares perspective of gridding. Introduction to off-resonance correction.
Lecture 7: Automatic off-resonance correction
Lecture 8: Parallel imaging. Read this survey article on parallel imaging by Larkman and Nunes. Focus particularly on Section 2 on reconstruction algorithms. You can skim the rest of the article for history and background. For additional information, you can read Section 13.3 in Bernstein.
Lecture 9: Parallel imaging, SMASH and an introduction to GRAPPA.
Lecture 12: Compressed sensing and parallel imaging. These are slides with a black background. Don't print them on an MRSRL printer! A white background version is here which is easier on your printer. Thanks to Qiyuan Tian for providing this.
Lecture 13: Projection reconstruction for parallel beam and fan beam geometries, part 1.
Lecture 16: 3D PET, and iterative reconstruction algorithms.
Lecture 17: 3D CT.
Lecture 18: Course summary.