Segmenting Anatomical Surfaces and Volumes with Markov Models

Bruce Fischl
Massachusetts General Hospital
Massachusetts General Hospital NMR Center

I present a set of techniques for embedding the physics of the imaging
process that generates a class of Magnetic Resonance Images (MRI) into a
segmentation/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 give rise to MR
contrast (e.g. T1, proton density, T2*), allowing access to these
physiologically relevant parameters that may change with disease or demographic,
resulting in non-morphometric alterations in MR images that are otherwise
difficult to detect. Finally, we also present a high-bandwidth multi-echo 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


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Presentation (PDF File)

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