We come to a conclusion that for a modern 3T MRI scanner given considerable time constraints important for clinical practice (10-20 minutes) a b-value of 2000s=mm2 and a number of around 70 gradients are sufficient to accurately recover angles of crossings of about 60. We evaluate the angular error of the simulated crossings in the reconstructed diffusion profiles by analytical decomposition techniques Qball and the DOT.
Qspace 2011 series#
To find the optimal acquisition schemes for a modern 3T scanner, we perform series of synthetic data experiments, as well as corresponding real data MRI scans.In this thesis some of the drawbacks are addressed and solutions provided: Additionally, the resultant output of these models is difficult to interpret for clinicians without technical pre-knowledge. These acquisitions result in a large amount of data, that is difficult to process and visualize as they require a great deal of computational resources. In particular, for a more accurate description of the underlying diffusion process, HARDI modeling techniques require acquisitions that make the total measurement time 4-5 times longer than for DTI. However, HARDI has many disadvantages in the acquisition, processing and visualization pipeline, that prevents it, for now, from consideration for clinical research practice and application. To overcome these limitations of DTI, high angular resolution diffusion imaging (HARDI) was introduced enabling a more accurate description of the local diffusion process. However, in areas of complex fiber architecture, e.g., crossing fibers, it fails to adequately depict the local diffusion process due to the crude assumption of the underlying diffusion process as Gaussian. Diffusion tensor imaging (DTI) is a recent magnetic resonance imaging (MRI) technique that can map the orientation architecture of neural tissues in a completely non-invasive way by measuring the directional specificity (anisotropy) of the local water diffusion.
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