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Diffusion tensor microscopy in human nervous tissue with quantitative correlation based on direct histological comparison

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  • Brian Hansen
  • Jeremy J Flint
  • ,
  • Choong Heon-Lee
  • ,
  • Michael Fey
  • ,
  • Franck Vincent
  • ,
  • Michael A King
  • ,
  • Peter Vestergaard-Poulsen, Denmark
  • Stephen J Blackband
  • Center for Functionally Integrative Neuroscience
  • Diagnostic Radiology
Thanks to its proven utility in both clinical and research applications, diffusion tensor tractography (DTT) is regularly employed as a means of delineating white-matter tracts. While successful efforts have been made to validate tractographic predictions, comparative methods which would permit the validation of such predictions at microscopic resolutions in complex biological tissues have remained elusive. In a previous study, we attempted to validate for the first time such predictions at microscopic resolutions in rat and pig spinal cords using a semi-quantitative analysis method. In the current study, we report improved quantitative analysis methods that can be used to determine the accuracy of DTT through comparative histology and apply these techniques for the first time to human tissue (spinal cord) samples. Histological images are down-sampled to resolutions equivalent to our magnetic resonance microscopy (MRM) and converted to binary maps using an automated thresholding tool. These maps (n=3) are co-registered to the MRM allowing us to quantify the agreement based on the number of pixels which contain tracts common to both imaging datasets. In our experiments, we find that-on average-89% of imaging pixels predicted by DTT to contain in-plane white-matter tract structure correspond to physical tracts identified by histology. In addition, angular analysis comparing the orientation of fiber tracts measured in histology to their corresponding in-plane primary eigenvector components is presented. Thus, as well as demonstrating feasibility in human tissue, we report a robust agreement between imaging datasets taken at microscopic resolution and confirm the primary eigenvector's role as a fundamental parameter with clear physical correlates in the microscopic regime.
Original languageEnglish
Pages (from-to)1458
Number of pages1,465
Publication statusPublished - 3 May 2011

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