Educational simulations for understanding the MRI signal. This project is created for OHBM Brainhack 2024. The main goal is to create single script simulations that builds up an understanding of the MRI signal step by step. Fundamental papers, and equations will be linked. The long term goal of this project is to build this repository as an educational resource to teach MRI basics. As this is a long term project, it will be "wotk in progress" for some time (several years).
- Project summary video from OHBM 2024 Brainhack event: https://youtu.be/jQyK0ZV1LzY
| Package | Tested version |
|---|---|
| matplotlib | 3.1.1 |
| NumPy | 1.22.0 |
Clone this repository, navigate to the folder, and execute in your terminal:
python 01_t2starsim_v1.py
If everything went well, you should be able to see and interactive figure as show below:
- Reading 1: [A very good starting point] Read and discuss equations 2, 3, and 4 from Hagberg, G., Tuzzi, E., 2014. Phase Variations in fMRI Time Series Analysis: Friend or Foe? <https://doi.org/10.5772/58275> .
- Task 1: After running
python 01_t2starsim_v1.pyand playing around with the parameters, implement Hagberg, Tuzzi 2014 Equation 3 (T1 relaxation). - Task 2: Write a new script where the users enters an echo time (e.g. 40 ms) and a percent signal change (e.g. 5 %) to compute the "required T2* change" to give rise to that percent signal change at the chosen echo time.
- Reading 2 : Understand Equation 1 of Uludag, K., Müller-Bierl, B., Ugurbil, K., 2009. An integrative model for neuronal activity-induced signal changes for gradient and spin echo functional imaging <https://doi.org/10.1016/j.neuroimage.2009.05.051>. Particularly, familiarize yourself with intravascular and extravascular components of the fMRI signal.
- Task 3: Run
02_T2starsim_v2.pyand contemplate how it relates to Uludag et al. 2009 Equation 1. - Reading 3: Sections 3.1 and 3.2 from Hagberg, G., Tuzzi, E., 2014. Phase Variations in fMRI Time Series Analysis: Friend or Foe? <https://doi.org/10.5772/58275>
- Task 3: Once you are done reading, run
03_generate_complex_numbers.pyprogram and contemplate. - Reading 4: See Figure 1 and 2 from: Gudbjartsson, H., Patz, S., 1995. The rician distribution of noisy mri data <https://doi.org/10.1002/mrm.1910340618>. Particularly, think about and compare how the statistical properties of magnitiude and phase signal change across low and high signal to noise ratio (SNR) regimes.
- Reading 5: Understand Equation 10 from Section 3.3 (you can avoid later parts of the section) from Hagberg, G., Tuzzi, E., 2014. Phase Variations in fMRI Time Series Analysis: Friend or Foe? <https://doi.org/10.5772/58275>.
- Task 4: Run
04_boxerman1995_interactive.pyand contemplate the similarities or differences to Equation 10 from Hagberg, Tuzzi, 2014. Then, compare what you see to Figure 7 of the same article. - Reading 5: Understand Figure 1 and 2 from: Vu, A.T., Gallant, J.L., 2015. Using a novel source-localized phase regressor technique for evaluation of the vascular contribution to semantic category area localization in BOLD fMRI <https://doi.org/10.3389/fnins.2015.00411>.
- Reading 6: Now lets go back to the beginning of fMRI by having a look at Figure 4 and Equation 1 from Ogawa, S., Lee, T., Nayak, A.S., Glynn, P., 1990. Oxygenation‐sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields <https://doi.org/10.1002/mrm.1910140108>.
- Reading 7: Once you had a look at Figure 1 and Equation 1 from the Reading 6, switch to Figure 1 and 2 from Menon, R.S., 2002. Postacquisition suppression of large‐vessel BOLD signals in high‐resolution fMRI <https://doi.org/10.1002/mrm.10041>. These figures and the text referring to these figures will elucidate further on the coupling between phase and magnitude fMRI signal, as well as connecting spin echo and gradient echo concepts.
- Reading 8: If you are struggling to understand or imagine the argument of Menon 2002 Figure 2, please look at the Figure 4 from: Hoogenraad, F.G.C., Reichenbach, J.R., Haacke, E.M., Lai, S., Kuppusamy, K., Sprenger, M., 1998. In vivo measurement of changes in venous blood‐oxygenation with high resolution functional MRI at 0.95 Tesla by measuring changes in susceptibility and velocity <https://doi.org/10.1002/mrm.1910390116>. Pause and ponder how the oxygenation increase is reflected in phase and magnitude components of the fMRi signal that are shown on this figure.
- ... work in progress ...
- An important source of inspiration for this project is https://www.youtube.com/c/3blue1brown .
- VASOlib: https://github.com/ofgulban/VASOlib
- MP2RAGElib: https://github.com/ofgulban/mp2ragelib
- @pbellec suggested that at some point in the future this resource might be turned into a living publication at https://preprint.neurolibre.org/10.55458/neurolibre.00019/intro.html
- Imagerie par résonance magnétique: https://methods-cogneuro.github.io/irm.html , some simulations can be incorporated to such teaching resources.