Preprocessing Flashcards
What are two possible kinds of data that we work with in MRI preprocessing/analysis?
- structural data (mostly acquired using T1 MPRAGE sequence): anatonical image of the brain
- functional data (mostly acquired using T2* EPI sequence): changes of brain activity over time
Define preprocessing and statistical analysis.
in MRI of course (:
Preprocessing:
Algorithmic correction of temporal and spatial artefacts, that arise from measurements
Statistical analysis:
Statistical model estimation and parameter inference on experimental variations of interest
Explain the terminollgy of fMRI (for experiments)
- Subjects: people who perform an experiment
- Session: a single appointmen for testing
- Run: A run of an experiment (multiple possible per session)
in SPM Runs are called sessions
Explain the terminology of fMRI (focus on data)
- In each run a number of volumes is acquired
- Each volume consists of different slices
- Each slice consists of differnt voxels
- The thickness of a slice is equivalent to the height of a voxel
- The in plane resolution describes the width and depths of voxels
- the Matrix size describes the amount of voxels in a slixe
- Matrix size and in plane resolution make up the field of view
What are the three brain axis in neuroimaging
- Coronal: view from front
- Sagittal: view from the side
- axial/transversal: view from above
Name the three main objectives of the SPM module for Matlab
- Displaying data
- preprocessing data
- analyzing data
Name 4 alternatives to SPM for preprocessing.
- AFNI
- FSL
- Fressurfer
- fMRIprep
Name the six typical steps of preprocessing.
IMPORTANT!
- Slice timing
- Realignment
- Coregistration
- Segmentation
- Normalization
- Smoothing
Why is slice timing necessary?
- The volume is acquired as a number of sequentially acquired slices. -> Each slice is acquired at a different point in time
- By using slice timing we are correcting the data, to estimate what the volume would look like if each slice were reorded simultaneously
- Slice time = Time of repition / Number of slices
- We can either acquire slices in ascending- , descending slice order or ascending interleaved slice order
How is slice timing performed mathemtically?
- Data from each each voxel is Fourier transformed
- Data for each time course per voxel is interpolated.
- Signal for each time point per voxel is phase shifted to the timepoint where the reference slice was recorded
- Data is backtransformed into signal space
What do we receive after performing slice timing in SPM?
- We receive volumes that are corrected to be acquired at a single timepoint per volume
- visually we can not distinguish slice timed and unprocessed images
- however the choice of selecting a certain reference slide is relevant to our statistical analysis (different results!)
Why do we perform Realignement?
- Subjects move during MRI acquisition, even if fixated and asked not to move, this can have certain effects:
- Activation shifts between voxels (i.e. activation that is in voxe a will appear in voxel b after movement) -> failure to detect local activations -> reduces sensitivity of analysis
- Experimental paradigm may be connected to movement -> spurious activations -> reduces specificity
What two steps are performed during realignement?
…and how is realignement performed?
- Estimation/Registration: determine rigid-body transformation from each acquired image to reference by minimizing the sqaured difference between original and realigned images (rigid-body transformation consists of 3 translation and 3 rotation parameters that are calibrated)
- Reslicing/Resampling: Applying estimated transformations to correct whole series -> register image first, then resampled image. Resampling = the transformation from one grid to another
What does realignement return?
A series of realigned images, the mean functional image and the values of realignment parameters over time.
Why should we perform coregistration?
- Before mapping images to standard space we must align the structural image to the functional images.
- This allows for a more precise anatomical localization of activations
- This relies on the same algorithms as realignment
What do we have to specifiy for coregistration in SPM?
- source image (usually structural T1 image)
- image that source image is realigned to (usuayll mean functional scan)
What is the output of coregistration in SPM?
- The critertion optimized to fit reference (e.g. normalized mutual information)
- The estimated realignement parameters (three rotations and three translations)
- the coregistered source image (structural) and the mean functional image
Why should we perform segmentation?
- Segmentation improves mapping to standard space
Is segmentaion performed independently in SPM?
No. In SPM segmentaion and Normalization are usually performed together in the model “unified segmentation”
How is segmentaion performed in SPM and what tissue types are segemented in SPM?
- SPM uses spatial priors (the so call tissue probability map) and the intensity of voxels in order to assess what tissue type a voxel belongs to.
- The tissue typed differentiated are:
- Skull (bone)
- CSF
- White matter
- Grey matter
- Meninges (dura mater, arachnoid mater, subarachnoid membrane, pia mater)
- (air)
What is the output of segmentaion in SPM?
- A number of Tissue probability maps (for different tissue types)
- Normalization parameters used for warping volume to standard space
Why should we perform Normalization?
- There is a great inter-subject variability when it comes to anatomy in MRI scans:
- If we perform experiments with multiple subjects, we want to increase sensitivity of our statistical analysis by having each voxel represent the same anatomical strucure for each participant -> standard anatomical template
- In order to compare studies, we want to map the brain into a common standard coordinate system -> standard template (e.g. MNI or Tailarach)
What two steps of Normalization are performed in SPM?
- Linear Normalization: Adjusting for global differences using affine transformations with 12 parameters (3 rotation x 3 translation 3 zoom x 3 sheer)
- Non-Linear Normalization: Adjusting for local differences using deformation fields based on smooth basis functions
How is linear normaliztaion achieved and what is its general goal?
There is a affien transformation using 12 paramers: 3 Translation -; 3 Rotaion-; 3 Zoom-; and 3 Sheer-Parameters
The goal is to have a rough agreement between source and reference images.