Diffusion MRI of the human brain: Signal modeling and quantitative analysis.

다국어 초록 (Multilingual Abstract)

Diffusion-weighted magnetic resonance imaging (DW-MRI) and specific applications such as DTI are uniquely capable of revealing the human brain's complex white-matter structure. Since its introduction two decades ago DTI has been applied to a broad range of neurological inquiry spanning neuroscience to clinical research. As the field has matured advances in signal modeling have led to a vast number of alternative diffusion sampling strategies (commonly HARDI techniques) and analysis methodologies.
New analysis methods are commonly introduced alongside simulation studies for comparison against alternatives, however differences in signal models, simulation parameters and/or evaluation metrics often prevents a broad comparison of similar work. Consequently it often remains unclear whether or not new techniques are improvements over existing approaches, and if so, under what conditions.
Development of real phantoms and synthetic data sets are indispensable for evaluating the accuracy, precision, reproducibility and noise sensitivity of DW-MRI analysis methods in a quantitative manner. While there has been a concerted effort towards this goal, there remains a need for publicly accessible DW-MRI data sets comprising realistic configurations of white matter pathways, with corresponding ground-truth of fiber directions and software tools to permit consistent and comparable detailed evaluation of analyzed data.
To this end we develop a comprehensive framework for synthesizing DW-MRI data resembling the human brain, with configurable SNR and diffusion sampling patterns, and apply the resulting data to evaluation of several multi-fiber DW-MRI analysis methods. The data sets and quantitative tools developed were made publicly available. It is our hope that the availability of these tools will enable a greater understanding of differences between analysis methods and development of better techniques.
With respect to quantitative diffusion imaging metrics---such as FA and MD which reflect changes in tissue microstructure and have become indispensable to non-invasive in-vivo assessment of neuropathology---it is important to assess the extent to which MR artifacts influence such metrics. While many geometric image distortion artifacts have been investigated and correction methods proposed, less work has examined the effect scanner drift and diffusion-weighting miscalibration have on diffusion metrics. We investigate these sources of erroneous signal change by developing a physical diffusion phantom. Our findings show differences in MD caused by diffusion-weighting miscalibration can be comparable to differences (in the same metric) between subjects or groups reported in clinical studies. This suggests differences in diffusion metrics cited in literature may not solely reflect changes in tissue pathology.