mpaquette / Intro-to-MRI

This is Jupyter notebook/python code developed for a UW-Madison introductory MRI class.

Intro-to-MRI

This is Jupyter notebook/python code developed for a UW-Madison introductory MRI class. The notebooks were made to support Google colab markdown but they should also open up in a standard jupyter enviroment. Most notebooks have a link on top of the page to allow you to open it directly from github.

Notebooks

1. Intro to Bloch Solvers : This notebooks introduces two ways to simulate the Bloch equations using either standard solvers or solvers assuming periods of free relaxation.
2. Spoiled Gradient Echo : This notebook simulates spoiled gradient echo as a means to create contrast in images.
3. Spin Echo : This notebook simulates spin echo as a means to create contrast in images.
4. Spatial Selective RF : This uses sinc pulses to investigate the tradeoffs in RF pulse choices.
5. Cartesian Sampling : This uses fake data to examine undersampling and reconstruction.
6. Cartesian Sampling Real Data : This uses real data to examine undersampling and reconstruction.

Advanced Notebooks

1. Variation Networks : Toy example of using model based machine reconstruction to reconstruction images with reduced artifacts.
2. Compressed Sensing : Example using parallel imaging and compressed SENSING

Simulations

1. EPI Distortions : Python code to simulate EPI distortions using brute force forward model with off-resonance.
2. Spiral Distortions : Python code to simulate spiral distortions using brute force forward model with off-resonance.
3. Complex Demodulation : Python code which shows the basic steps to convert real valued detected signal to complex signal in the rotating frame.