Sequence-independent segmentation of magnetic resonance images.
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.
Fischl
Bruce
B
Department of Radiology, MGH, Athinoula A Martinos Center, Harvard Medical School, Charlestown, MA 02129, USA. fischl@nmr.mgh.harvard.edu
Salat
David H
DH
van der Kouwe
André J W
AJ
Makris
Nikos
N
Ségonne
Florent
F
Quinn
Brian T
BT
Dale
Anders M
AM
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology*; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000473:pathology; D017352:Echo-Planar Imaging; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical; D017711:Nonlinear Dynamics