Image quality and preprocessing

How can the wine is absent for a nice meal, after appetizer, we need go through the drinks menu.

1 Inspect the image quality

When you go to a fancy restaurant,sit down and finish the appetizers, the next for you would be the drink menu and you will ask for the quality of wines.The most important ingredient for a wine is the grapes,they are the secret to the aroma and taste of wine. Unfortunately, there are several factors that could impact the quality of grapes such as the temperature, humidity, or latitude and longitude, so do the images. So, what could be better guarantee than inspect them closely by yourself?

Individual slices in an fMRI acquisition commonly suffer from random variations in average signal intensity, noise spikes, ghosts, and data glitches. These may result from physiological sources (participant motion, respiration, cardiac pulsations, anxiety, drowsiness, drugs) or from the scanner itself (field inhomogeneities, eddy currents, gradient heating, electronics). If unrecognized and included in the data analysis, these may spoil the entire experiment. A quick visual review of all source images together in montage mode is highly recommended to search for and exclude (“scrub”) aberrant slices that appear too bright, too dark, or contain artifacts.

2 Slice correction

There are no two identical wine in the world. we have to look at each bottle.

Most fMRI studies acquire one slice at a time, meaning that the signal recorded from one slice may be offset in time by up to several seconds when compared to another. Each of these slices takes tens to hundreds of milliseconds.The two most commonly used methods for creating volumes are sequential and interleaved slice acquisition. sequential acquisition slices have been acquired in sequential (1,2,3,4,5,6…), Interleaved slice acquisition (1,3,5,..2,4,6…) acquires every other slice and then fills in the gaps on the second pass o. Although slice timing differences may not be important for simple block design experiments, they can impact considerable errors in rapid, event-related fMRI studies if not accounted for.In addtion, the statistics model requires the time-series for each slice needs to be shifted back in time by the duration it took to acquire that slice.

figure created by Andrew Jahn

3 Motion correction

Another influential factor for the quality is the move, even a small motion would make a difference to the wine, just like they did to the scan.

Head motion is the largest source of error in fMRI studies, If the subject in the scanner is moving, the images will look blurry.If the subject moves a lot, we also risk measuring signal from a voxel that moves. We are then in danger of measuring signal from the voxel from a different region or tissue type. It is the false positive.In addtion, motion can introduce confounds into the imaging data because motion generates signal. If the subject moves every time in response to a stimulus - for example, if he jerks his head every time he feels an electrical shock, then it can become impossible to determine whether the signal we are measuring is in response to the stimulus, or because of the movement. Therefore, a variety of strategies have been developed to cope with this problem such as immobilization of the head using padding and straps, bite bars, masks during the scanning. What’s more, the standard motion correction method of neuroimage packages considers the head as a rigid body with three directions of translation (displacement) and three axes of rotation. A single functional volume of a run is chosen as the reference to which runs in all other volumes are aligned. Procedures are performed in which each volume is rotated and aligned with the reference, with the goal to minimize a cost function. This iterative adjustment terminates once no further improvement can be achieved..

figure created by Andrew Jahn

4 Registration

The easiest and quickest way to know the quality of a wine is comparison, we need to take different wines to know which one is better and we also need a standard for a reference, we can compare each wine to each other and a standard wine for a quality assessment. So, here is the registration and normalization.

Although most people’s brains are similar - everyone has 4 lobes and subcortical, there are also differences in brain size and shape. As a consequence, if we want to do a group analysis we need to ensure that each voxel for each subject corresponds to the same part of the brain. If we are measuring a voxel in the visual cortex, make sure that every subject’s visual cortex is in alignment with each other.

This alignment between the functional and anatomical images is called Registration. Most registration algorithms use the following steps:

1 Assume that the functional and anatomical images are in roughly the same location. If they are not, align the outlines of the images.

2 Take advantage of the fact that the anatomical and functional images have different contrast weightings - that is, areas where the image is dark on the anatomical image (such as cerebrospinal fluid) will appear bright on the functional image, and vice versa. This is called mutual information. The registration algorithm moves the images around to test different overlays of the anatomical and functional images, matching the bright voxels on one image with the dark voxels of another image, and the dark with the bright, until it finds a match that cannot be improved upon.

3 Once the best match has been found, then the same transformations that were used to warp the anatomical image to the template are applied to the functional images.

5 Normalization

Each brain needs to be transformed to have the same size, shape, and dimensions to be compared. We do this by normalizing (or warping) to a template. A template is a brain that has standard dimensions and coordinates, Each subjects’ functional images will be transformed to match the general shape and large anatomical features of the template. Most researchers have agreed to use them when reporting their results.The dimensions and coordinates of the template brain are also referred to as standardized space.

6 Smoothing

If we have the best wine, why don’t to mix it with other drinks in order to achieve the best taste? It is common for neuroimage software to smooth the functional data, or replace the signal at each voxel with a weighted average of that voxel’s neighbors. This may seem strange at first - why would we want to make the images blurrier than they already are?

Spatial smoothing is the averaging of signals from adjacent voxels. This improves the signal-to-noise ratio (SNR) but decreases spatial resolution, blurs the image, and smears activated areas into adjacent voxels. The process can be justified because closely neighboring brain voxels are usually inherently correlated in their function and blood supply. The standard method is to convolve (“multiply”) the fMRI data with a 3D Gaussian kernel (“filter”) that averages signals from neighboring voxels with weights that decrease with increasing distance from the target voxel. In practice, the full width half maximum (FWHM) value of the Gaussian spatial filter is typically set to about 4-6 mm for single subject studies and to about 6-8 mm for multi-subject analyses. The benefits of smoothing can outweigh the drawbacks:

1 We know that fMRI data contain a lot of noise, and that the noise is frequently greater than the signal. By averaging over nearby voxels we can cancel out the noise and enhance the signal.

2 Smoothing can be good for group analyses in which all of the subjects’ images have been normalized to a template. If the images are smoothed, there will be more overlap between clusters of signal, and therefore greater likelihood of detecting a significant effect.

figure created by Andrew Jahn

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