Three-Dimensional Reconstruction of Activated Columns from 2-[14C]Deoxy-d-glucose Data

Goldszal, Alberto; Tretiak, Oleh J.; Hand, Peter J.; Bhasin, Sanjay; McEachron, Donald L.

doi:10.1006/nimg.1995.1004

Cited by 31 publications

(18 citation statements)

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“…Registration methods for autoradiographs have used, for example, artificial landmarks (Goldszal et al, 1995), external section contour (Cohen et al, 1998), principle axes transformation (Hess et al, 1998;Hibbard et al, 1987), consistent matrix transformation (Andreasen et al, 1992;Hess et al, 1998), or multi-modal warping based on mutual information metric as a mapping cost function (Kim et al, 1997). We chose a non-warping geometric model restricted to rigid-body transformations that operated on an automated intensity-based registration algorithm (Thevenaz et al, 1998).…”

Section: Discussionmentioning

confidence: 99%

Statistical parametric mapping applied to an autoradiographic study of cerebral activation during treadmill walking in rats

et al. 2004

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Autoradiographs are conventionally analyzed by a region-of-interest (ROI) analysis. However, definition of ROIs on an image set is labor intensive, is subject to potential inter-rater bias, and is not well suited for anatomically variable structures that may not consistently correspond to specific ROIs. Most importantly, the ROI method is poorly suited for whole-brain analysis, where one wishes to detect all activations resulting from an experimental paradigm. A system developed for analysis of imaging data in humans, Statistical Parametric Mapping (SPM), avoids some of these limitations but has not previously been adapted as a tool for the analysis of autoradiographs. Here, we describe the application of SPM to an autoradiographic data set mapping cerebral activation in rats during treadmill walking. We studied freely moving, non-tethered rats that received injections of the cerebral blood flow tracer [ 14 C]-iodoantipyrine, while they were performing a treadmill task (n = 7) or during a quiescent control condition (n = 6). Results obtained with SPM were compared to those previously reported using a standard ROI-based method of analysis [J. Cereb. Blood Flow Metab. 23 (2003) 925]. The SPM method confirmed most areas detected as significant using the ROI approach. However, in the subcortex, SPM detected additional significant regions that, because of their irregular structures, fell short of statistical significance when analyzed by ROI. The SPM approach offers the ability to perform a semi-automated whole-brain analysis, and coupled with autoradiography, provides an effective means to globally localize functional activity in small animals.

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Section: Discussionmentioning

confidence: 99%

Statistical parametric mapping applied to an autoradiographic study of cerebral activation during treadmill walking in rats

et al. 2004

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“…Some interesting reviews of general medical image registration methods are presented in [6,47,30,22]. The principal 3D alignment (reconstruction from 2D images) methods may be classified in the following categories: fiducial marker-based methods [21], feature based methods using contours, crest lines or characteristic points extracted from the images [28,39], and gray level-based registration techniques using the intensities of the whole image [27,2,24,38]. Most of the above mentioned techniques do not simultaneously consider the two major difficulties involved in medical and CT scanned data registration.…”

Section: Related Workmentioning

confidence: 99%

3D reconstruction of medical images from slices automatically landmarked with growing neural models

et al. 2015

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In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the Growing Neural Gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge Email addresses: agelopa@wmin.ac.uk (Anastassia Angelopoulou), psarroa@wmin.ac.uk (Alexandra Psarrou), jgarcia@dtic.ua.es (Jose Garcia-Rodriguez), sorts@dtic.ua.es (Sergio Orts-Escolano), jazorin@dtic.ua.es (Jorge Azorin-Lopez), ken.revett@bue.edu.eg (Kenneth Revett) February 16, 2014 of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared with methods with similar efficiency as Voxel Grid. Preprint submitted to Neurocomputing

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“…More advanced methods based on Procrustes can be used as well (Goldszal et al, 1995;Hill et al, 2001). When less than two markers are found, the section is discarded.…”

Section: D Reconstructionmentioning

confidence: 99%

A low temperature embedding and section registration strategy for 3D image reconstruction of the rat brain from autoradiographic sections

Simonetti

Elezi

Farion

et al. 2006

Journal of Neuroscience Methods

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Three-Dimensional Reconstruction of Activated Columns from 2-[14C]Deoxy-d-glucose Data

Cited by 31 publications

References 0 publications

Statistical parametric mapping applied to an autoradiographic study of cerebral activation during treadmill walking in rats

Statistical parametric mapping applied to an autoradiographic study of cerebral activation during treadmill walking in rats

3D reconstruction of medical images from slices automatically landmarked with growing neural models

A low temperature embedding and section registration strategy for 3D image reconstruction of the rat brain from autoradiographic sections

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