The possibility of detecting and measuring the diffusivity of water with Nuclear Magnetic Resonance (NMR) methods was reported in the early work of pioneers of NMR in the early fifties (e.g. E. L. Hahn, Phys. Rev. 80, 580–594 (1950). In the seventies the first spectroscopic investigations of diffusion in biological tissues took place, followed in the mid eighties by the first clinical demonstration of diffusion weighted MRI (DWI). The investigation of diffusion in anisotropic and heterogeneous tissues has been facilitated by the introduction of diffusion tensor MRI (DTI) in the nineties. From diffusion tensor data one can compute quantities that characterize specific features of the diffusion process, such as the principal diffusivities (eigenvalues of D), the trace of the diffusion tensor (Trace(D)), indices of diffusion anisotropy, and the principal directions of diffusion (eigenvectors of D). The eigenvector associated to the largest diffusivity has been used as a proxy to infer the orientation of white matter fibers. Since the initial DTI “tractography” studies published about 20 years ago, several more sophisticated diffusion MRI – based tractography methods have been proposed generating a lot of enthusiasm in the neuroscience community in the hope that these tools could help elucidating anatomical connectivity in the central nervous system. In this talk we will provide an “historical” overview of various modelling approaches that have been proposed for inferring anatomy and connectivity of white matter fibers from diffusion MRI data. We will describe the foundations of methods for deterministic tractography, and probabilistic tractography that will be addressed in detail by the subsequent lecturers. We will also provide background information to introduce the lecture on “validation” of Diffusion MRI tractography. In general, understanding the relationship between a measured water diffusion pattern and the underlying histological features of the tissue is not simple. We lack a robust and comprehensive model that relates water diffusivity to specific biological features. Essentially, we do not have diffusion metrics that can be unequivocally used as biomarkers. For example, the main problem in inferring white matter trajectories from diffusion MRI measurements is that the diffusion properties measured in a voxel are affected by the presence of a large quantity of axons. The measured diffusion displacement profile is essentially a voxel-averaged quantity which provides a good estimate of fiber orientation only if the axons are oriented collinearly. In heterogeneous tissue, inferring the intravoxel architecture of white matter becomes a complicated inverse problem which we argue may be unsolvable with the limited information gathered by Diffusion MRI. One other obstacle is related to the quality of the measurement. The water diffusion displacement profile we measure in each voxel is affected by both instrumental and physiological noise. In diffusion-based tractography the effect of noise is magnified because errors are propagated from one voxel to the next in the tractography chain. Although diffusion MRI metrics are quantitative, several factors adversely affect the accuracy and precision of diffusion MRI tractography in practice. Such factors can be broadly classified as originating from thermal noise, system induced artifacts, and physiological noise. Physiological noise originates from subject motion, cardiac pulsation, partial volume contamination from cerebral-spinal fluid, and, possibly, respiratory motion and blood flow induced pseudo-diffusion effects. These factors affect the reproducibility of clinical diffusion MRI scans and negatively impact clinical studies in several ways. Not knowing the overall variability of diffusion MRI measurements precludes computing the number of subjects necessary to be able to detect a given effect. Moreover, longitudinal data and data from different centers cannot be compared reliably. Sources of variability also act as confounds in assessing differences between different groups of subjects. For example, brain “connectivity” differences found between healthy controls and patients may be due to artifacts originating from physiological noise, such as heart rate and subject motion, rather than to true anatomical differences. There is clearly a need for improving the resolution, reliability, and overall quality of diffusion tensor MRI acquisitions. The challenge is to achieve this goal by maintaining a reasonably short scan time.Proper data preprocessing, however, can effectively minimize the manifestation of many of these potential sources of artifacts. Specifically, we will examine the following steps that are desirable in diffusion MRI preprocessing:1)Importing data, check if images are sorted correctly2) Initial quality control by visual inspection or other automated tools3) Production of “corrected” raw images:Motion and eddy-current distortion correctionEPI distortion correctionEventual reorientation to a templateProper rotation of coordinate frame (b-matrices or b-val/bvec)Potential denoising and Gibbs ringing remediationAccounting for signal drifts during acquisitionCorrecting for cardiac pulsation artifactsCorrecting geometric distortions due to gradient non-linearities and amplifier miscalibration + correction of b-matrices or b-val/bvec.Correcting for ghosting and other imaging artifacts

Acknowledgements

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References

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