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Sequence-independent segmentation of magnetic resonance images.

NeuroImage | Oct 25, 2004

http://www.ncbi.nlm.nih.gov/pubmed/15501102

We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability.

Pubmed ID: 15501102 RIS Download

Mesh terms: Algorithms | Brain | Cerebral Cortex | Echo-Planar Imaging | Functional Laterality | Humans | Image Processing, Computer-Assisted | Magnetic Resonance Imaging | Models, Statistical | Nonlinear Dynamics

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Associated grants

  • Agency: NCRR NIH HHS, Id: P41-RR14075
  • Agency: NCRR NIH HHS, Id: R01 RR16594-01A1

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