Lecture Notes

My handwritten lecture notes are available below, if you are signed up for the class.

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!

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, with Adam Wang as Lecturer.

Lecture 18: Course summary.