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Magn Reson Imaging. 2009 Apr;27(3):401-16. doi: 10.1016/j.mri.2008.07.016. Epub 2008 Sep 10.

Enhancement of histological volumes through averaging and their use for the analysis of magnetic resonance images.

Author information

1
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37240-1662, USA. xia.li.1@Vanderbilt.edu

Abstract

Magnetic resonance imaging (MRI) of small animals is routinely performed in research centers. But despite its many advantages, MR still suffers from limited spatial resolution which makes the interpretation and quantitative analysis of the images difficult, particularly for small structures of interest within areas of significant heterogeneity. One possibility to address this issue is to complement the MR images with histological data, which requires reconstructing 3D volumes from a series of 2D images. A number of methods have been proposed recently in the literature to address this issue, but deformation or tearing during the slicing process often produces reconstructed volumes with visible artifacts and imperfections. In this paper, we show that a possible solution to this problem is to work with several histological volumes, reconstruct each of these separately and then compute an average. The resulting histological atlas shows structures and substructures more clearly than any individual volume. We also propose an original approach to normalize intensity values across slices, a required preprocessing step when reconstructing histological volumes. We show that the histological atlas we have created can be used to localize structures and substructures, which cannot be seen easily in MR images. We also create an MR atlas that is associated with the histological atlas. We show that using the histological volumes to create the MR atlas is better than using the MR volumes only. Finally, we validate our approach quantitatively on MR image volumes by comparing volumetric measurements obtained manually and obtained automatically with our atlases.

PMID:
18786794
PMCID:
PMC2690703
DOI:
10.1016/j.mri.2008.07.016
[Indexed for MEDLINE]
Free PMC Article

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