A type of MRA used to display slow flow across a large volume with a good resolution. Two data volumes are measured; the flow-rephased images show bright signal, the flow-dephased image show dark flow, whereby in both data volumes the signal of the stationary tissue looks the same. The data volumes are subtracted and the signal intensity of flowing blood remains.

This function equalizes the signal intensity when surface coils are used. By using this filter, the signal intensity of the region close to the coil is reduced, and the signal intensity of the area remote to the coil is increased. Also called homogeneity correction.

Water and fat signals being in or out of phase result from the FFE method and the slight difference in resonance frequencies of the protons. It can cause black "outlining" of tissues and decrease in signal from voxel containing both water and fat. At 1.5 T, the water and fat signal are in phase when TE is an even multiple, and out of phase when TE is an odd multiple of 2.3 ms.
1.5T: OUT of PHASE = 2.3, 6.9, 11.5, 16.1, 20.7 ms
1.0T: OUT of PHASE = 3.5, 10.4, 17.3, 24.2 ms
0.5T: OUT of PHASE = 6.9, 20.7 ms

See also Opposed Phase Image, and Dixon.

Oversampling is the increase in data to avoid aliasing and wrap around artifacts. Aliasing is the incorrectly mapping of tissue signal from outside the FOV to a location inside the FOV. This is caused by the fact, that the acquired k-space frequency data is not sampled density enough.
Oversampling in frequency direction, done by increasing the sampling frequency, prevents this aliasing artifact. The proper frequency based on the sampling theorem (Shannon sampling theorem/Nyquist sampling theorem) must be at least twice the frequency of each frequency component in the incoming signal. All frequency components above this limit will be aliased to frequencies between zero and half of the sampling frequency and combined with the proper signal information, which creates the artifact. Oversampling creates a larger field of view, more data needs to be stored and processed, but this is for modern MRI systems not a real problem. Oversampling in phase direction (no phase wrap), to eliminate wrap around artifacts, by increasing the number of phase encoding steps, results in longer scan/processing times.

In parallel MR imaging, a reduced data set in the phase encoding direction(s) of k-space is acquired to shorten acquisition time, combining the signal of several coil arrays. The spatial information related to the phased array coil elements is utilized for reducing the amount of conventional Fourier encoding.
First, low-resolution, fully Fourier-encoded reference images are required for sensitivity assessment. Parallel imaging reconstruction in the Cartesian case is efficiently performed by creating one aliased image for each array element using discrete Fourier transformation. The next step then is to create an full FOV image from the set of intermediate images. Parallel reconstruction techniques can be used to improve the image quality with increased signal to noise ratio, spatial resolution, reduced artifacts, and the temporal resolution in dynamic MRI scans.
Parallel imaging algorithms can be divided into 2 main groups:
Image reconstruction produced by each coil (reconstruction in the image domain, after Fourier transform): SENSE (Sensitivity Encoding), PILS (Partially Parallel Imaging with Localized Sensitivity), ASSET.
Reconstruction of the Fourier plane of images from the frequency signals of each coil (reconstruction in the frequency domain, before Fourier transform): GRAPPA.
Additional techniques include SMASH, SPEEDER™, IPAT (Integrated Parallel Acquisition Techniques - derived of GRAPPA a k-space based technique) and mSENSE (an image based enhanced version of SENSE).