(DTI) Diffusion tensor imaging is the more sophisticated form of DWI, which allows for the determination of directionality as well as the magnitude of water diffusion. This kind of MR imaging can estimates damage to nerve fibers that connect the area of the brain affected by the stroke to brain regions that are distant from it, and can be used to determine the effectiveness of stroke prevention medications.
DTI (FiberTrak) enables to visualize white matter fibers in the brain and can map (trace image) subtle changes in the white matter associated with diseases such as multiple sclerosis and epilepsy, as well as assessing diseases where the brain's wiring is abnormal, such as schizophrenia.
The fractional anisotropy (FA) gives information about the shape of the diffusion tensor at each voxel. The FA is based on the normalized variance of the eigenvalues. The fractional anisotropy reflects differences between an isotropic diffusion and a linear diffusion. The FA range is between 0 and 1 (0 = isotropic diffusion, 1 = highly directional).
The development of new imaging methods and some useful analysis techniques, such as 3-dimensional anisotropy contrast (3DAC) and spatial tracking of the diffusion tensor tractography (DTT), are currently under study.

(DTT) This technique has been reported on during the last few years and is the most intriguing demonstration that allows for the noninvasive racking of neuronal fiber projections in a living human brain. White matter fiber trajectories are reconstructed throughout the brain by tracking the direction of fastest diffusion, which is assumed to correspond to the longitudinal axis of the tract. Diffusion tensor tractography should provide new insights into white matter integrity, fiber connectivity, surgical planning, and patients prognosis.

See also B-Value.

(ADC) A diffusion coefficient to differentiate T2 shine through effects or artifacts from real ischemic lesions. In the human brain, water diffusion is a three-dimensional process that is not truly random because the diffusional motion of water is impeded by natural barriers. These barriers are cell membranes, myelin sheaths, white matter fiber tracts, and protein molecules.
The apparent water diffusion coefficients can be calculated by acquiring two or more images with a different gradient duration and amplitude (b-values). The contrast in the ADC map depends on the spatially distributed diffusion coefficient of the acquired tissues and does not contain T1 and T2* values.
The increased sensitivity of diffusion-weighted MRI in detecting acute ischemia is thought to be the result of the water shift intracellularly restricting motion of water protons (cytotoxic edema), whereas the conventional T2 weighted images show signal alteration mostly as a result of vasogenic edema.
The reduced ADC value also could be the result of decreased temperature in the nonperfused tissues, loss of brain pulsations leading to a decrease in apparent proton motion, increased tissue osmolality associated with ischemia, or a combination of these factors. The lower ADC measurements seen with early ischemia, have not been fully established, however, a lower apparent ADC is a sensitive indicator of early ischemic brain at a stage when ischemic tissue remains potentially salvageable.

See also Diffusion Weighted Imaging and Diffusion Tensor Tractography.

This family of sequences uses a balanced gradient waveform. This waveform will act on any stationary spin on resonance between 2 consecutive RF pulses and return it to the same phase it had before the gradients were applied. A balanced sequence starts out with a RF pulse of 90° or less and the spins in the steady state. Prior to the next TR in the slice encoding, the phase encoding and the frequency encoding direction, gradients are balanced so their net value is zero. Now the spins are prepared to accept the next RF pulse, and their corresponding signal can become part of the new transverse magnetization. If the balanced gradients maintain the longitudinal and transverse magnetization, the result is that both T1 and T2 contrast are represented in the image.
This pulse sequence produces images with increased signal from fluid (like T2 weighted sequences), along with retaining T1 weighted tissue contrast. Balanced sequences are particularly useful in cardiac MRI. Because this form of sequence is extremely dependent on field homogeneity, it is essential to run a shimming prior the acquisition.
Usually the gray and white matter contrast is poor, making this type of sequence unsuited for brain MRI. Modifications like ramping up and down the flip angles can increase signal to noise ratio and contrast of brain tissues (suggested under the name COSMIC - Coherent Oscillatory State acquisition for the Manipulation of Image Contrast).
These sequences include e.g. Balanced Fast Field Echo (bFFE), Balanced Turbo Field Echo (bTFE), Fast Imaging with Steady Precession (TrueFISP, sometimes short TRUFI), Completely Balanced Steady State (CBASS) and Balanced SARGE (BASG).

(SNR or S/N) The signal to noise ratio is used in MRI to describe the relative contributions to a detected signal of the true signal and random superimposed signals ('background noise') - a criterion for image quality.
One common method to increase the SNR is to average several measurements of the signal, on the expectation that random contributions will tend to cancel out. The SNR can also be improved by sampling larger volumes (increasing the field of view and slice thickness with a corresponding loss of spatial resolution) or, within limits, by increasing the strength of the magnetic field used. Surface coils can also be used to improve local signal intensity. The SNR will depend, in part, on the electrical properties of the sample or patient being studied. The SNR increases in proportion to voxel volume (1/resolution), the square root of the number of acquisitions (NEX), and the square root of the number of scans (phase encodings). SNR decreases with the field of view squared (FOV2) and wider bandwidths. See also Signal Intensity and Spin Density.

Measuring SNR:
Record the mean value of a small ROI placed in the most homogeneous area of tissue with high signal intensity (e.g. white matter in thalamus). Calculate the standard deviation for the largest possible ROI placed outside the object in the image background (avoid ghosting/aliasing or eye movement artifact regions).
The SNR is then:
Mean Signal/Standard Deviation of Background Noise