Methods are presented to map complex fiber architectures in tissues by imaging the 3D spectra of tissue water diffusion with MR. First, theoretical considerations show why and under what conditions diffusion contrast is positive. Using this result, spin displacement spectra that are conventionally phase‐encoded can be accurately reconstructed by a Fourier transform of the measured signal's modulus. Second, studies of in vitro and in vivo samples demonstrate correspondence between the orientational maxima of the diffusion spectrum and those of the fiber orientation density at each location. In specimens with complex muscular tissue, such as the tongue, diffusion spectrum images show characteristic local heterogeneities of fiber architectures, including angular dispersion and intersection. Cerebral diffusion spectra acquired in normal human subjects resolve known white matter tracts and tract intersections. Finally, the relation between the presented model‐free imaging technique and other available diffusion MRI schemes is discussed. Magn Reson Med, 2005.

Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T₁-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics.

Based on the Fourier transform relation between diffusion magnetic resonance (MR) signals and the underlying diffusion displacement, a new relation is derived to estimate the spin distribution function (SDF) directly from diffusion MR signals. This relation leads to an imaging method called generalized q -sampling imaging (GQI), which can obtain the SDF from the shell sampling scheme used in q -ball imaging (QBI) or the grid sampling scheme used in diffusion spectrum imaging (DSI). The accuracy of GQI was evaluated by a simulation study and an in vivo experiment in comparison with QBI and DSI. The simulation results showed that the accuracy of GQI was comparable to that of QBI and DSI. The simulation study of GQI also showed that an anisotropy index, named quantitative anisotropy, was correlated with the volume fraction of the resolved fiber component. The in vivo images of GQI demonstrated that SDF patterns were similar to the ODFs reconstructed by QBI or DSI. The tractography generated from GQI was also similar to those generated from QBI and DSI. In conclusion, the proposed GQI method can be applied to grid or shell sampling schemes and can provide directional and quantitative information about the crossing fibers.

Building the Brain: Brain connectivity is often described as a network of discrete independent cables analogous to a switchboard, but how is the physical structure of the brain constructed (see the Perspective by Zilles and Amunts )? Wedeen et al. (p. 1628 ) used high-resolution diffusion tensor imaging in humans and four species of nonhuman primates to identify and compare the geometric structure of large fiber tracts in the brain. Fiber tracts followed a highly constrained and regular geometry, which may provide an efficient solution for pathfinding during ontogenetic development. Much of development occurs through elaboration and assembly of semiautonomous building blocks. Chen et al. (p. 1634 ) applied statistical analysis to the form of the human cortex in brain-imaging studies that compared more than 400 di- and mono-zygotic twins. The findings suggest that the structure of the human cortex is defined by genetics.