Sequence-independent segmentation of magnetic resonance images. 2004

Bruce Fischl, and David H Salat, and André J W van der Kouwe, and Nikos Makris, and Florent Ségonne, and Brian T Quinn, and Anders M Dale
Department of Radiology, MGH, Athinoula A Martinos Center, Harvard Medical School, Charlestown, MA 02129, USA. fischl@nmr.mgh.harvard.edu

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.

UI MeSH Term Description Entries
D007091 Image Processing, Computer-Assisted A technique of inputting two-dimensional or three-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer. Biomedical Image Processing,Computer-Assisted Image Processing,Digital Image Processing,Image Analysis, Computer-Assisted,Image Reconstruction,Medical Image Processing,Analysis, Computer-Assisted Image,Computer-Assisted Image Analysis,Computer Assisted Image Analysis,Computer Assisted Image Processing,Computer-Assisted Image Analyses,Image Analyses, Computer-Assisted,Image Analysis, Computer Assisted,Image Processing, Biomedical,Image Processing, Computer Assisted,Image Processing, Digital,Image Processing, Medical,Image Processings, Medical,Image Reconstructions,Medical Image Processings,Processing, Biomedical Image,Processing, Digital Image,Processing, Medical Image,Processings, Digital Image,Processings, Medical Image,Reconstruction, Image,Reconstructions, Image
D007839 Functional Laterality Behavioral manifestations of cerebral dominance in which there is preferential use and superior functioning of either the left or the right side, as in the preferred use of the right hand or right foot. Ambidexterity,Behavioral Laterality,Handedness,Laterality of Motor Control,Mirror Writing,Laterality, Behavioral,Laterality, Functional,Mirror Writings,Motor Control Laterality,Writing, Mirror,Writings, Mirror
D008279 Magnetic Resonance Imaging Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques. Chemical Shift Imaging,MR Tomography,MRI Scans,MRI, Functional,Magnetic Resonance Image,Magnetic Resonance Imaging, Functional,Magnetization Transfer Contrast Imaging,NMR Imaging,NMR Tomography,Tomography, NMR,Tomography, Proton Spin,fMRI,Functional Magnetic Resonance Imaging,Imaging, Chemical Shift,Proton Spin Tomography,Spin Echo Imaging,Steady-State Free Precession MRI,Tomography, MR,Zeugmatography,Chemical Shift Imagings,Echo Imaging, Spin,Echo Imagings, Spin,Functional MRI,Functional MRIs,Image, Magnetic Resonance,Imaging, Magnetic Resonance,Imaging, NMR,Imaging, Spin Echo,Imagings, Chemical Shift,Imagings, Spin Echo,MRI Scan,MRIs, Functional,Magnetic Resonance Images,Resonance Image, Magnetic,Scan, MRI,Scans, MRI,Shift Imaging, Chemical,Shift Imagings, Chemical,Spin Echo Imagings,Steady State Free Precession MRI
D001921 Brain The part of CENTRAL NERVOUS SYSTEM that is contained within the skull (CRANIUM). Arising from the NEURAL TUBE, the embryonic brain is comprised of three major parts including PROSENCEPHALON (the forebrain); MESENCEPHALON (the midbrain); and RHOMBENCEPHALON (the hindbrain). The developed brain consists of CEREBRUM; CEREBELLUM; and other structures in the BRAIN STEM. Encephalon
D002540 Cerebral Cortex The thin layer of GRAY MATTER on the surface of the CEREBRAL HEMISPHERES that develops from the TELENCEPHALON and folds into gyri and sulci. It reaches its highest development in humans and is responsible for intellectual faculties and higher mental functions. Allocortex,Archipallium,Cortex Cerebri,Cortical Plate,Paleocortex,Periallocortex,Allocortices,Archipalliums,Cerebral Cortices,Cortex Cerebrus,Cortex, Cerebral,Cortical Plates,Paleocortices,Periallocortices,Plate, Cortical
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465 Algorithms A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. Algorithm
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D017352 Echo-Planar Imaging A type of MAGNETIC RESONANCE IMAGING that uses only one nuclear spin excitation per image and therefore can obtain images in a fraction of a second rather than the minutes required in traditional MRI techniques. It is used in a variety of medical and scientific applications. MR Tomography, Echo-Planar,Magnetic Resonance Imaging, Echo-Planar,Echo-Planar MR Tomography,Echo-Planar Magnetic Resonance Imaging,Echoplanar Imaging,Echoplanar MR Tomography,Echoplanar Magnetic Resonance Imaging,MR Tomography, Echoplanar,Magnetic Resonance Imaging, Echoplanar,Echo Planar Imaging,Echo Planar MR Tomography,Echo Planar Magnetic Resonance Imaging,Echo-Planar Imagings,Echo-Planar MR Tomographies,Echoplanar Imagings,Echoplanar MR Tomographies,Imaging, Echo-Planar,Imaging, Echoplanar,Imagings, Echo-Planar,Imagings, Echoplanar,MR Tomographies, Echo-Planar,MR Tomographies, Echoplanar,MR Tomography, Echo Planar,Magnetic Resonance Imaging, Echo Planar,Tomographies, Echo-Planar MR,Tomographies, Echoplanar MR,Tomography, Echo-Planar MR,Tomography, Echoplanar MR
D017711 Nonlinear Dynamics The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos. Chaos Theory,Models, Nonlinear,Non-linear Dynamics,Non-linear Models,Chaos Theories,Dynamics, Non-linear,Dynamics, Nonlinear,Model, Non-linear,Model, Nonlinear,Models, Non-linear,Non linear Dynamics,Non linear Models,Non-linear Dynamic,Non-linear Model,Nonlinear Dynamic,Nonlinear Model,Nonlinear Models,Theories, Chaos,Theory, Chaos

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