Difference between revisions of "CRUISE"

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{{h2|CRUISE:Cortical Reconstruction Using Implicit Surface Evolution}}
 
{{h2|CRUISE:Cortical Reconstruction Using Implicit Surface Evolution}}
 
{{iacl|~xhan/|X. Han}}, {{medic|dpham/|D.L. Pham}}, D. Tosun, {{iacl|~rettmann|M.E. Rettmann}}, B. Lucas, S. Roy, {{iacl|~chenyang/|C. Xu}}, {{iacl|~aaron/|A. Carass}}, and [[Prince|J.L. Prince]]
 
{{iacl|~xhan/|X. Han}}, {{medic|dpham/|D.L. Pham}}, D. Tosun, {{iacl|~rettmann|M.E. Rettmann}}, B. Lucas, S. Roy, {{iacl|~chenyang/|C. Xu}}, {{iacl|~aaron/|A. Carass}}, and [[Prince|J.L. Prince]]
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{{h3|Overview}}
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Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. A successful cortical reconstruction method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. In this work, we improve upon our previous efforts on central cortical surface reconstruction by addressing a few major limitations and introducing several novel developments. The resulting method, which we call CRUISE, is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections.
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{{h3|Introduction}}
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Geometrically, the cerebral cortex is a thin, folded sheet of gray matter (GM) that is 1–5 mm thick. The cortical GM is bounded by the cerebrospinal fluid (CSF) on the outside, forming the pial cortical surface, and by the white matter (WM) on the inside, forming the inner cortical surface. It is useful to define the central cortical surface as well, which lies at roughly the geometric center between the inner and pial surfaces and gives an overall 2-D approximation to the 3-D cortical sheet. Finding the three surfaces enables the visualization and study of the sulcal and gyral patterns of an individual subject, and allows morphometric measurements such as cortical volume, surface area, cortical thickness, and sulcal depth. These measures provide valuable information about the cortical characteristics of both normal development and pathological diseases. Correspondences between cortical reconstructions from different subjects can also be used for image registration, digital atlas labeling, and population-based probabilistic atlas generation. In addition, cortical reconstruction is important for functional brain mapping, surgical planning, and cortical unfolding or flattening.
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Cortical reconstruction is a step beyond pure segmentation that only classifies the image pixels as belonging to cortical GM or not. An accurate cortical reconstruction must generate a geometric representation of the cortex that is consistent with the true geometry of the brain cortex, complete with multiple lobes, gyral folds, and narrow sulci. In addition, a correct cortical surface reconstruction must have the correct spherical topology and be a valid 2-D manifold without self-intersections. This task is made difficult because of limitations in image quality, especially due to various imaging artifacts such as image noise, partial volume effects, and intensity inhomogeneities. To overcome these difficulties and get successful cortical reconstructions, the CRUISE method we developed systematically combines a robust fuzzy tissue classification algorithm, an efficient topology correction method, and a topology-preserving self-intersection free geometric deformable surface model.

Revision as of 20:15, 7 July 2008

CRUISE:Cortical Reconstruction Using Implicit Surface Evolution

X. Han, D.L. Pham, D. Tosun, M.E. Rettmann, B. Lucas, S. Roy, C. Xu, A. Carass, and J.L. Prince

Overview

Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. A successful cortical reconstruction method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. In this work, we improve upon our previous efforts on central cortical surface reconstruction by addressing a few major limitations and introducing several novel developments. The resulting method, which we call CRUISE, is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections.

Introduction

Geometrically, the cerebral cortex is a thin, folded sheet of gray matter (GM) that is 1–5 mm thick. The cortical GM is bounded by the cerebrospinal fluid (CSF) on the outside, forming the pial cortical surface, and by the white matter (WM) on the inside, forming the inner cortical surface. It is useful to define the central cortical surface as well, which lies at roughly the geometric center between the inner and pial surfaces and gives an overall 2-D approximation to the 3-D cortical sheet. Finding the three surfaces enables the visualization and study of the sulcal and gyral patterns of an individual subject, and allows morphometric measurements such as cortical volume, surface area, cortical thickness, and sulcal depth. These measures provide valuable information about the cortical characteristics of both normal development and pathological diseases. Correspondences between cortical reconstructions from different subjects can also be used for image registration, digital atlas labeling, and population-based probabilistic atlas generation. In addition, cortical reconstruction is important for functional brain mapping, surgical planning, and cortical unfolding or flattening.

Cortical reconstruction is a step beyond pure segmentation that only classifies the image pixels as belonging to cortical GM or not. An accurate cortical reconstruction must generate a geometric representation of the cortex that is consistent with the true geometry of the brain cortex, complete with multiple lobes, gyral folds, and narrow sulci. In addition, a correct cortical surface reconstruction must have the correct spherical topology and be a valid 2-D manifold without self-intersections. This task is made difficult because of limitations in image quality, especially due to various imaging artifacts such as image noise, partial volume effects, and intensity inhomogeneities. To overcome these difficulties and get successful cortical reconstructions, the CRUISE method we developed systematically combines a robust fuzzy tissue classification algorithm, an efficient topology correction method, and a topology-preserving self-intersection free geometric deformable surface model.