Bragi Sveinsson
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Correction of magnetic field distortion in EPI images
Magnetic field inhomogeneities are the most common cause of distortions in fMRI images, which make matching of data to the high-resolution structural images difficult. Various methods have been proposed to correct for this distortion. For this project, some of these methods were examined and tested on EPI data. In particular, a toolbox based on magnetic field mapping methods and developed for the SPM software environment, was tested on EPI data provided by the VISTA lab. Furthermore, the toolbox was integrated better into the mrVista software environment to make it work without installing SPM.
Background
Causes of field disturbances
The main reason for magnetic field disturbances in MRI imaging is the different magnetic susceptibility of the different materials in the brain. A material's magnetic susceptibility can be characterized by the equation Failed to parse (unknown function "\ksi"): {\displaystyle \textbf{M} = \ksi \textbf{H}}
You can use subsections if you like.
Below is an example of a retinotopic map. Or, to be precise, below will be an example of a retinotopic map once the image is uploaded. To add an image, simply put text like this inside double brackets 'MyFile.jpg | My figure caption'. When you save this text and click on the link, the wiki will ask you for the figure.
Below is another example of a reinotopic map in a different subject.
Figure 2
Once you upload the images, they look like this. Note that you can control many features of the images, like whether to show a thumbnail, and the display resolution.
MNI space
MNI is an abbreviation for Montreal Neurological Institute.
Methods
Measuring retinotopic maps
Retinotopic maps were obtained in 5 subjects using Population Receptive Field mapping methods Dumoulin and Wandell (2008). These data were collected for another research project in the Wandell lab. We re-analyzed the data for this project, as described below.
Subjects
Subjects were 5 healthy volunteers.
MR acquisition
Data were obtained on a GE scanner. Et cetera.
MR Analysis
The MR data was analyzed using mrVista software tools.
Pre-processing
All data were slice-time corrected, motion corrected, and repeated scans were averaged together to create a single average scan for each subject. Et cetera.
PRF model fits
PRF models were fit with a 2-gaussian model.
MNI space
After a pRF model was solved for each subject, the model was trasnformed into MNI template space. This was done by first aligning the high resolution t1-weighted anatomical scan from each subject to an MNI template. Since the pRF model was coregistered to the t1-anatomical scan, the same alignment matrix could then be applied to the pRF model.
Once each pRF model was aligned to MNI space, 4 model parameters - x, y, sigma, and r^2 - were averaged across each of the 6 subjects in each voxel.
Et cetera.
Results - What you found
Retinotopic models in native space
Some text. Some analysis. Some figures.
Retinotopic models in individual subjects transformed into MNI space
Some text. Some analysis. Some figures.
Retinotopic models in group-averaged data on the MNI template brain
Some text. Some analysis. Some figures.
Retinotopic models in group-averaged data projected back into native space
Some text. Some analysis. Some figures.
Conclusions
Here is where you say what your results mean.
References
Software
Appendix I - Code and Data
Code
Data
Appendix II - Work partition (if a group project)
Brian and Bob gave the lectures. Jon mucked around on the wiki.