Image Acquisition

Calculation of the diffusion tensor requires acquisition of a set of MR images using suitable diffusion-weighted sequences. Echo-planar sequences with different gradient directions and intensities are the more appropriate for these applications and are those used most commonly [1, 14,15].

Diffusion weighting involves a general reduction in signal intensity, which is greater the greater the diffusion of water. This magnifies the SNR problems shared by all MR acquisitions and makes it difficult to obtain images with very high spatial resolution. Currently, the best spatial resolution that can be achieved is rarely less than 1 mm, particularly along the slice-encoding direction, but use of high magnetic fields (3.0 T or greater) and parallel imaging can further enhance resolution, thus fibre tracking [16]. Diffusion weighting depends on gradient intensity, which is usually denominated b factor and is measured as s/mm2. In theory, increasing b values should be used to calculate the ADC; in practice, limitations in the gradients used in clinical practice, specific absorption rate, and times of acquisition result in the prevalent use of only two b values, one virtually null (no diffusion weighting), and the second high, 1,000 s/mm2 or greater. A b value slightly greater than 0 (~20) is used to remove the effects of large vessels and flow. The minimum set of images to be acquired for a DTI study includes six different diffusion-weighted directions (with b = 1,000 or greater) and a non-diffusion-weighted scan (b = 0). The minimum set may be acquired several times to improve the SNR, whereas acquisitions with different b values for each direction are unnecessary as well as inefficient in terms of SNR [11]. More accurate evaluation of the diffusion coefficient D from two acquisitions has been demonstrated using two values of b differing by ~1/D, which in the brain entails that b2-b1< 1,000 -1,500 s/mm2 [11,17]. If more than two acquisitions are performed to optimize the SNR, the theory of error propagation states that it is more convenient to obtain multiple acquisitions at the two b values selected than to use a broader range of b values [11].

Acquisitions in a range, rather than a pair (b = 0 and b = 1,000) of b values, can however provide interesting information [18, 19]. Although a single ADC value tends to be assigned to each tissue voxel, most tissues are in fact made up of separate compartments, each with a distinct ADC. Brain tissue comprises at least two compartments, a fast-diffusion intracellular compartment and a slower-diffusion extracellular compartment. The ADC depends on the range of b values used, because low values (1,000) are more sensitive to fastdiffusion components and thus to the structural features of the interstitium, than to those of axon fibres. Ideally, the different tissue compartments should be studied separately using several different b values and then fitted with a multiexponential function. However, since slow-diffusion compartments can be studied only with high b values and favourable SNRs, this is difficult to achieve.

The number of directions offering the best compromise between a reliably reconstructed tensor (multiple directions) and long acquisition times is still debated. While from a mathematical standpoint at least six different directions plus a low b value acquisition are required, most researchers use 6 to 90 different directions, with considerable differences in acquisition times and uncertain benefit. A sequence with 68 directions, b = 100 and 1,000 s/mm2, and a cubic voxel of 2.3 mm lasts about 13 min, but actuallytakes much longer because image averaging to obtain an acceptable SNR requires multiple acquisitions. Here, a large role is playedby acquisition conditions, particularly magnetic field intensity and the availability of parallel imaging to improve the SNR.

High angular resolution techniques (HARDI; see below), which require a much greater number of directions (even 252 or more), benefit from favourable conditions of field intensity and high coil sensitivity.

As mentioned above, the diffusion tensor basically provides two types of information: a quantitative estimate of diffusion anisotropy and the spatial orientation of fibres (Fig. 8.2). These data are interesting but „local", i.e. they regard a single voxel. Tractography uses these microscopic data to obtain „global" information and reconstruct macroscopic fibre tracts.

Fig. 8.2. Diffusion tensor. Projection in the image plane of the principal eigenvector. Vectors are represented by double-headed arrows because diffusion data provide the direction, not the orientation of diffusion

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