KEYWORDS: Magnetic resonance imaging, Brain, Image registration, Neuroimaging, Neuroscience, Data modeling, Medical imaging, In vivo imaging, Image processing, Digital imaging
Coregistration of in vivo magnetic resonance imaging (MRI) with histology provides validation of disease
biomarker and pathobiology studies. Although thin-plate splines are widely used in such image registration,
point landmark selection is error prone and often time-consuming. We present a technique to optimize
landmark selection for thin-plate splines and demonstrate its usefulness in warping rodent brain MRI to
histological sections. In this technique, contours are drawn on the corresponding MRI slices and images of
histological sections. The landmarks are extracted from the contours by equal spacing then optimized by
minimizing a cost function consisting of the landmark displacement and contour curvature. The technique was
validated using simulation data and brain MRI-histology coregistration in a murine model of HIV-1
encephalitis. Registration error was quantified by calculating target registration error (TRE). The TRE of
approximately 8 pixels for 20-80 landmarks without optimization was stable at different landmark numbers.
The optimized results were more accurate at low landmark numbers (TRE of approximately 2 pixels for 50
landmarks), while the accuracy decreased (TRE approximately 8 pixels for larger numbers of landmarks (70-
80). The results demonstrated that registration accuracy decreases with the increasing landmark numbers
offering more confidence in MRI-histology registration using thin-plate splines.
KEYWORDS: Magnetic resonance imaging, Brain, Neuroimaging, Image registration, 3D image processing, Tissues, Head, Software development, In vivo imaging, Image processing
Registration of MRI to histopathological sections can enhance bioimaging validation for use in pathobiologic,
diagnostic, and therapeutic evaluations. However, commonly used registration methods fall short of this goal due to
tissue shrinkage and tearing after brain extraction and preparation. In attempts to overcome these limitations we
developed a software toolbox using 3D blockface imaging as the common space of reference. This toolbox includes a
semi-automatic brain extraction technique using constraint level sets (CLS), 3D reconstruction methods for the blockface
and MR volume, and a 2D warping technique using thin-plate splines with landmark optimization. Using this toolbox,
the rodent brain volume is first extracted from the whole head MRI using CLS. The blockface volume is reconstructed
followed by 3D brain MRI registration to the blockface volume to correct the global deformations due to brain extraction
and fixation. Finally, registered MRI and histological slices are warped to corresponding blockface images to correct
slice specific deformations. The CLS brain extraction technique was validated by comparing manual results showing
94% overlap. The image warping technique was validated by calculating target registration error (TRE). Results showed
a registration accuracy of a TRE < 1 pixel. Lastly, the registration method and the software tools developed were used to
validate cell migration in murine human immunodeficiency virus type one encephalitis.
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