Optical coherence tomography (OCT) has shown an affinity for imaging white matter using intrinsic signals. Combined with an automated vibratome and mosaic imaging, serial blockface histology (SBH) can yield whole-brain white matter images at high resolution. A current drawback of SBH is the lack of real-time information that complicates the localization of brain structures during imaging. To address this, imaged slices can be registered to a pre-existing 3D volume to provide more contextual information during the acquisition. 3D brain image registration is a process where a volume is aligned to a standard template to perform further analysis in a common reference frame. Without a full 3D volume, however, this slice-to-volume registration often proves difficult. The search space is large, and the limited information hampers existing algorithms. In this article, we present a neural network that predicts the 3D position of a 2D slice and aligns it to the corresponding slice in 3D template volume. The network uses a VGG16 backbone to extract features, followed by fully connected layers to predict the transformations. Six mouse brains at a resolution of 25μm, imaged using Serial OCT, have been used to train the network. The loss is calculated by taking the Euclidian distance between the predictions and the ground truth, which has been randomly sampled from the volume. Applications for this model are 2D to 3D slice registration, providing contextual information during serial OCT acquisitions such as the progress, or a parcelization of the current slice into its brain regions.
Previous studies have shown that the optical coherence tomography (OCT) signal in white matter (WM) is affected by the WM fiber bundles orientation with respect to the microscope’s optical axis. In this paper, we aim to exploit this contrast mechanism to generate a multi-orientation representation of WM microstructure in whole mouse brains. To achieve this, a serial blockface histology set-up has been developed combined with spectral domain OCT equipped with a long-range 10x magnification objective, achieving a near isotropic resolution of 3 micron laterally (xy) and 3.5 micron axially (z). With this imaging system, a map of WM structures can be generated for an entire agarose embedded mouse brain. To precisely control the mouse brain orientation within the agarose, we designed a multi-part 3D printed mold, which allows us to choose the vibratome’s slicing plane (e.g., coronal, axial, sagittal, etc.). After the serial OCT acquisition, every slice is reconstructed as 2D images and stacked to obtain a 2.5D volume. The reconstruction process uses a nextflow computational pipeline, allowing us to parallelize the calculations. Our proposed imaging method emphasizes different WM structures according to their orientation, which we illustrated in the mouse’s anterior commissure olfactory limb. This structure is very bright when observed in axial slices, whereas it has a darker appearance in the coronal slices. Using this method, we plan to acquire whole mouse brains oriented in multiple directions and to create a multi-orientation mouse brain template, which we believe will prove useful to get a better understanding of complex WM microstructure geometries, such as fiber crossing areas.
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