Myocardial fiber orientation mapping using reduced encoding diffusion tensor imaging.

Published

Journal Article

A precise knowledge of the myocardial fiber architecture is essential to accurately understand and interpret cardiac electrical and mechanical functions. Diffusion tensor imaging has been used to noninvasively and quantitatively characterize myocardial fiber orientations. However, because the approach necessitates diffusion to be measured in multiple encoding directions and frequently at multiple weighting levels, the required data set size may present a limitation on its acquisition time efficiency. Applying the principles of reduced encoding imaging (REI), four basic reconstruction schemes, keyhole using direct substitution, keyhole with baseline correction, symmetrically encoded REI with generalized-series reconstruction (RIGR), and asymmetrically encoded RIGR, are evaluated in terms of their accuracy in diffusion tensorfiber orientation mapping of excised myocardial samples. Results show that the performances of all REI schemes, at approximately 50% reduced encoding, are at least comparable with that of a control experiment consisting of proportionally reduced number of full k-space images. Moreover, although performances of the symmetrically and asymmetrically encoded RIGR schemes are similar, both methods provide significant improvements over the control experiment and the direct-substitution keyhole technique. These findings demonstrate the potential of the general REI methodology for diffusion tensor imaging and pave the way for modified schemes involving rapid imaging sequences or alternative k-space sampling strategies to achieve even better data acquisition time efficiency and performance.

Full Text

Duke Authors

Cited Authors

  • Hsu, EW; Henriquez, CS

Published Date

  • January 2001

Published In

Volume / Issue

  • 3 / 4

Start / End Page

  • 339 - 347

PubMed ID

  • 11777226

Pubmed Central ID

  • 11777226

Electronic International Standard Serial Number (EISSN)

  • 1532-429X

International Standard Serial Number (ISSN)

  • 1097-6647

Digital Object Identifier (DOI)

  • 10.1081/jcmr-100108588

Language

  • eng