Three-Dimensional Computer Reconstruction from Surface Contours for Head CT Examinations

Batnitzky, Solomon; Price, Hilton I.; Cook, P. Nong; Cook, Larry T.; Dwyer, Samuel J.

doi:10.1097/00004728-198102000-00012

Cited by 42 publications

(14 citation statements)

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“…These contours were then converted into an ensemble of closed polyhedra. Each set of contours was processed using a simplified version of well-known tiling algorithms (Christiansen and Sederberg, 1978;Batnitzky et al 1981). ' …”

Section: Vol Atlas Constructionmentioning

confidence: 99%

MRI-PET Correlation in Three Dimensions Using a Volume-of-Interest (VOI) Atlas

Evans

Marrett

Torres-Corzo

et al. 1991

J Cereb Blood Flow Metab

256

104

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Summary: Quantitative interpretation of functional im ages (PET or SPECT) is hampered by poor spatial reso lution, low counting statistics, and, for many tracers, low contrast between different brain structures of interest. Furthermore, normal tracer distributions can be severely disrupted by such gross pathologies as stroke, tumor, and dementia. Hence, the complementary anatomical infor mation provided by CT or MRI is essential for accurate and reproducible regional analysis of functional data. We have developed methods for the simultaneous three dimensional display and analysis of image volumes from MRI and PET. A general algorithm for defining the affine transformation between two equivalent point ensembles has been adapted for the purpose of registering MRI and PET image volumes by means of a simple fiducial arThe problem of quantitative regional analysis of positron emission tomography (PET) data from the human brain has been discussed at length (Bajcsy et aI. , 1983; Bohm et aI. , 1983 Bohm et aI. , , 1985 Dann et aI. , 1988; Evans et aI. , 1988 Evans et aI. , , 1989 Valentino et aI. , 1988; Marrett et aI. , 1989; Pelizarri et aI. , 1989). PET im ages offer imprecise anatomical information be cause of poor spatial resolution, poor statistics, or because the tracer distribution does not adequately reflect underlying anatomical variation. These problems are often exacerbated when imaging pathological brain, where extensive disruption of normal functional patterns can occur. Hence, accu rate and reproducible analysis of different types of A69rangement. In addition, we have extended previous MRI based computerized atlas methodology to three dimen sions. The native atlas planes were spaced at 2 mm inter vals, sufficient axial sampling to permit the generation of oblique planar sections through the atlas space. This will allow for an infinite number of angulations and axial off sets in two-dimensional region-of-interest (ROI) tem plates, all derived from the same master three-dimen sional volume-of-interest (Val) atlas and therefore main taining topographical consistency throughout. These ROI templates may be selected to match the image orientation for conventional two-dimensional segmentation and data extraction. Key Words: Positron emission tomography Anatomical-functional correlation-Magnetic resonance imaging-Volume-of-interest atlas-Regions of interest.PET image requires additional information from the structural imaging modalities of magnetic resonance imaging (MRI) or computed tomography (CT).In previous work (Evans et aI. , 1988), we devel oped procedures for the registration of MRI and PET image planes using fiducial markers for patient setup. We also implemented a method for transfer ring the MRI anatomical information to the PET analysis via a computerized brain atlas that is mod ified interactively, plane by plane, to fit each sub ject's brain. That methodology required careful planning to obtain matched planes from MRI, PET, and the brain atlas. In this report, we describe our implementation ...

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Section: Vol Atlas Constructionmentioning

confidence: 99%

MRI-PET Correlation in Three Dimensions Using a Volume-of-Interest (VOI) Atlas

Evans

Marrett

Torres-Corzo

et al. 1991

J Cereb Blood Flow Metab

256

104

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“…It was proved that if U P 1 is the solution of the eikonal equation (4), then every curve C(s) that is a solution of the ordinary differential equation (3) is also a solution of the Euler-Lagrange equations (5). This result paved the way to define and compute the globally minimal path between a point P and a curve Γ on the Riemannian manifold P .…”

Section: Preliminariesmentioning

confidence: 98%

“…The methods for computing such paths can be divided into two categories: the exhaustive search methods (e.g, [17,13]) that evaluate the final surface according to some global criterion, and the methods based on weighted graphs (e.g., [6,4,12]) according to which a weight is assigned on each graph node and then starting from the least one, the whole path is computed by choosing in each step, among the neighboring nodes, the one with minimum weight.…”

Section: Interpolating Two Polygons With C 0 Triangular Facet Surfacesmentioning

confidence: 99%

“…Thus, by definition, the final result depends on the relative position of the sets P Γ and P ∆ . The method of [4] proposes a translation of the polygons so as their centers, A Γ and A ∆ coinside. Thus, the square of the afore defined distance for the translated polygons, with respect to the initial points is equal to ||(…”

Section: Interpolating Two Polygons With C 0 Triangular Facet Surfacesmentioning

confidence: 99%

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An Implicit Method for Interpolating Two Digital Closed Curves on Parallel Planes

Gabrielides

Cohen

2009

Lecture Notes in Computer Science

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Abstract. Ardon et al. [2] presented an implicit method for surface segmentation in 3D images. The boundary of the surface is assumed to be constrained by two given curves in the image. In this work we adopt the afore approach to interpolate two given digital curves lying on parallel planes, by introducing an artificial image potential, which is based on a triangular facet surface interpolation technique.

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“…Batnitzky et al [3923] described another local method. They first mapped each contour (in some unspecified way) onto a contour that was star convex relative to the centroid of the original contour, that is, a contour on which every point could be joined to the centroid by a straight line which did not cross over the contour.…”

Section: Triangulation Algorithmsmentioning

confidence: 99%