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Fiber tractography based on diffusion tensor imaging compared with high-angular-resolution diffusion imaging with compressed sensing: initial experience.

Neurosurgery 2013 January
BACKGROUND: The most frequently used method for fiber tractography based on diffusion tensor imaging (DTI) is associated with restrictions in the resolution of crossing or kissing fibers and in the vicinity of tumor or edema. Tractography based on high-angular-resolution diffusion imaging (HARDI) is capable of overcoming this restriction. With compressed sensing (CS) techniques, HARDI acquisitions with a smaller number of directional measurements can be used, thus enabling the use of HARDI-based fiber tractography in clinical practice.

OBJECTIVE: To investigate whether HARDI+CS-based fiber tractography improves the display of neuroanatomically complex pathways and in areas of disturbed diffusion properties.

METHODS: Six patients with gliomas in the vicinity of language-related areas underwent 3-T magnetic resonance imaging including a diffusion-weighted data set with 30 gradient directions. Additionally, functional magnetic resonance imaging for cortical language sites was obtained. Fiber tractography was performed with deterministic streamline algorithms based on DTI using 3 different software platforms. Additionally, tractography based on reconstructed diffusion signals using HARDI+CS was performed.

RESULTS: HARDI+CS-based tractography displayed more compact fiber bundles compared with the DTI-based results in all cases. In 3 cases, neuroanatomically plausible fiber bundles were displayed in the vicinity of tumor and peritumoral edema, which could not be traced on the basis of DTI. The curvature around the sylvian fissure was displayed properly in 6 cases and in only 2 cases with DTI-based tractography.

CONCLUSION: HARDI+CS seems to be a promising approach for fiber tractography in clinical practice for neuroanatomically complex fiber pathways and in areas of disturbed diffusion, overcoming the problem of long acquisition times.

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