This repository is devoted to ineractive demos useful for learning medical physics imaging concepts discussed in our class. I am a firm believer that you can't fully understand something until you can code it, so a lot of this was made to solidify my own knowledge! But it is my hope that students find these notebooks useful for learning image processing techniques we don't get to cover in class. The translation from theory to application is often overlooked in present teaching methods, and I remember being extremely lost when faced with my first real image processing task during my research as a medical physics grad student, despite passing homeworks and exams on the same topics. Feel free to try out the codes presented and try out your own variations! Play around with variables or load your own data and have fun!
- The projects included are all written in Jupyter notebooks for ease of demonstration with Python version 3.10.9
- For information on how to use and interact with Jupy notebooks, see documentation here: https://docs.jupyter.org/en/latest/
- The easiest way to run a notebook is through the Anaconda distribution: https://www.anaconda.com/download
- Anaconda is a package manager and code vizualizer that automatically installs python and various helpful packages/apps
- Use JupyterLab or Notebook found in the Anaconda navigator to open and run notebooks after sucesful installation
- If you are a more experienced programmer, VS Code is a convienent code visualizer that offers Jupy plugins
- Download VS Code here: https://code.visualstudio.com/
- For documentation on VS Code and Jupy interfacing, see documentation here: https://code.visualstudio.com/docs/datascience/jupyter-notebooks
- It can be tricky to manage your python environments and packages in VS Code so be prepaired to do some debugging if a notebook does not compile on the first try
- If you have trouble with python kernel selection, see this helpful forum post: https://code.visualstudio.com/docs/datascience/jupyter-kernel-management
Special thanks to my classmate Gia Jadick for teaching me the fundimentals of image processing as well as my professors Dr. Sam Armato, Dr. Patrick LaRivere, Dr. Chien-Min Kao, and Dr. Sean Foxley for giving me the theoretical background on this facinating field!
I am still learning just like you! There is a high probability there are some errors in these notebooks. If you come across any, please let me know by reaching out to me via email at arenne@uchiago.edu!