## Lecture NotesMy 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 5: Gridding kernel design and oversampling ratio. The Beatty paper is here. 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 10: Parallel imaging, GRAPPA calibration, SPIRiT, and coil compression. Read SPIRiT paper. Lecture 11: Compressed Sensing (CS) and sparse MRI. Read Sparse MRI paper. 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 14: Projection reconstruction for fan beam geometries, part 2. The rebinning example slides are here. Lecture 15: Introduction to Positron Emission Tomography. Read the Olligner and Fessler survey paper here. Lecture 16: 3D PET, and iterative reconstruction algorithms. Lecture 17: 3D CT. Lecture 18: Low rank reconstruction of time series data. Lecture 19: Simultaneous Multislice. Lecture 20: Course Summary. |